Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,481
datasets available to search
ShareScore release 0.9.0
Dataset results
1,481 results for “data processing”
Raw and processed SPT data for "Guided nuclear exploration increases CTCF target search efficiency"
<p><strong>Raw and processed SPT data for “Guided nuclear exploration increases CTCF target search efficiency”</strong></p> <p>Anders S. Hansen<sup>1,2,3,4,*</sup>, Assaf Amitai<sup>5,*</sup>, Claudia Cattoglio<sup>1,2,3,4</sup>, Robert Tjian<sup>1,2,3,4</sup>, Xavier Darzacq<sup>1,2,3</sup></p> <p> </p> <p>1: Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, USA;</p> <p>2: Li Ka Shing Center for Biomedical and Health Sciences</p> <p>3: CIRM Center of Excellence, University of California, Berkeley, Berkeley, USA;</p> <p>4: Howard Hughes Medical Institute, University of California, Berkeley, Berkeley, USA;</p> <p>5: Department of Chemical Engineering, MIT, Cambridge 02139, Massachusetts, USA</p> <p>*: ASH and AA contributed equally.</p> <p> </p> <p><strong>Overview</strong></p> <p>This repository contains all the raw and processed spaSPT data associated with “Guided nuclear exploration increases CTCF target search efficiency”. In total, this dataset contains data for 1,669 single-cell movies and trajectories with 85,612,029 unique displacements. In this ReadMe file we provide the following information:</p> <ul> <li>The cell lines used in this study.</li> <li>How the data was collected.</li> <li>How to read the raw single-cell SPT spaSPT data.</li> <li>How to read the processed, quality-controlled and HMM-classified data</li> </ul> <p>The document does not contain information about how the data was analyzed. For details on how the data was analyzed and for raw code to reproduce our figures, please go to <a href="https://gitlab.com/anders.sejr.hansen/anisotropy">https://gitlab.com/anders.sejr.hansen/anisotropy</a></p> <p> </p> <p><strong>Cell lines and transient transfection constructs</strong></p> <p>In total, 20 different cell lines were studied here. The mouse embryonic stem cells (mESCs) are in the JM8.N4 background (Pettitt et al., 2009) and the human cells were U2OS osteosarcoma cells. Cell lines expressing Halo-tagged proteins were either homozygous knock-in cell lines, wild-type cell lines transiently transfected with a plasmid over-expressing the protein of interest (Lipofectamine 3000), or wild-type cell lines stably over-expressing a transgene.</p> <p>The wild-type and C59 mESC cell lines were pathogen tested using the IMPACT II test (performed by IDEXX BioResearch) and where negative for all tested pathogens (full details provided in (Hansen et al., 2017)). The wild-type and C32 U2OS cell lines were tested for mycoplasma contamination and found to be clean and further authenticated by Short Tandem Repeat Profiling (STR profiling; 100% match with U2OS; full details provided in (Hansen et al., 2017)).</p> <p>Further details on each cell line or transfection construct are provided in the table in the ReadMe PDF (the MAT-file name is the name of the MAT-file containing the raw data).</p> <p><strong>Data collection using stroboscopic photo-activation single-particle tracking (spaSPT)</strong></p> <p>For full details on how the experiments where performed, please see the Materials and Methods section of the manuscript. Here we will briefly describe the protocol.</p> <p>To systematically investigate how proteins explore the nucleus we need a very large dataset (i.e. hundreds of thousands of trajectories) of very high quality SPT data (i.e. with minimal bias and without many tracking errors). The two major sources of bias in experimental SPT data are 1) motion-blurring and 2) tracking errors (Hansen et al., 2018). First, motion-blurring biases against detecting fast-moving molecules since while the camera is exposed, a fast-moving molecule will spread its photons over many pixels and no longer resemble a diffraction limited spot, whereas a bound or slow-moving molecule will emit all its photons as a single diffraction limited spot. Since most localization algorithms readily detect diffraction limited spots, but not motion-blurs this introduces a clear bias. spaSPT overcomes this bias by strobing the excitation laser (Elf et al., 2007): using just a single 1 ms excitation pulse, we simultaneously achieve high signal-to-background (~10-fold), while minimizing motion-blur as previously demonstrated (for a more complete discussion, please see (Hansen et al., 2018)). Second, tracking errors are largely caused by high particle densities: for example, when trajectories overlap, tracking errors occur which clearly prevents accurately analysis of protein diffusion and which could lead to artefactual conclusions. By using the bright photo-activatable Janelia Fluor dyes PA-JF<sub>549</sub> and PA-JF<sub>646</sub> (Grimm et al., 2016), we can overcome this challenge (this is also known as sptPALM (Manley et al., 2008)). During the camera integration time between frame (~447 microseconds), we pulse the 405 nm photo-activation laser at an intensity such that the mean localization density is around 1 molecule per nucleus per frame. At this density, tracking errors are greatly reduced, yet we are able to collect tens of thousands of frames at this density per cell and thus obtain large amounts of data despite imaging at a very low density. For a more complete discussion of spaSPT please see (Hansen et al., 2017, 2018).</p> <p>To generate a large dataset at multiple time-scales, we collected spaSPT data (generally around 8 cells per replicate and four biological replicates; occasionally a cell would be removed from subsequent analysis if the localization density was too high and thus prone to tracking errors) at 3 camera exposure times: 4 ms (add ~447 microseconds), 7 ms (add ~447 microseconds) and 13 ms (add ~447 microseconds); this roughly corresponds to frame-rates of ~223 Hz, 133 Hz, and 74 Hz. To generate data at longer lag times (this was only done after HMM-classification; see also main paper), this data was subsampled according to the table in the ReadMe PDF.</p> <p><strong>Overview of raw, unprocessed single-cell spaSPT data</strong></p> <p>This repository contains all the raw, unprocessed spaSPT data for each single cell in the directory “UnprocessedSingleCellData”. In total, this directory contains 1669 MAT-files corresponding to the 1669 single-movies recorded here. The files are systematically named for their parent cell line, replicate number and cell number. For example, “mESC_C59_Halo-mCTCF_133Hz_Rep4_Cell8.mat” contains SPT data for mouse embryonic stem cell line clone 59 and Halo-CTCF was imaged at ~133 Hz; the file is for cell number 8 in biological replicate number 4. All the other single-cell experiments are similarly named. Each MAT-file contain all the trajectories recording from a single-cell movie and stored as a “structure array” object, “trackedPar”. trackedPar contains three variables for each trajectory:</p> <ul> <li>trackedPar.xy: “xy” is a matrix with 2 columns and a number of rows corresponding to the number of frames where the molecule was located. The first column is the x-coordinate and the second column is the y-coordinate and the units are micrometers.</li> <li>trackedPar.Frame: “Frame” is a column vector where each element is an integer describing the frame number wherein the particle was localized (each trajectory contains at most 1 gap between frames).</li> <li>trackedPar.TimeStamp: “TimeStamp” is a column vector where each element is the timepoint (in units of seconds) where the molecule was localized.</li> </ul> <p>Note that this Matlab format is directly readable by Spot-On: <a href="https://spoton.berkeley.edu/">https://spoton.berkeley.edu/</a></p> <p> </p> <p><strong>Overview of HMM-classified and processed SPT data</strong></p> <p>The raw spaSPT data was processed (to remove any rare tracking errors) and all the data merged. The data was the classified into either a “bound” or “free” state using a 2-state Hidden-Markov Model (HHM; vbSPT (Persson et al., 2013)) and then temporally subsampled to generate data at longer frame rates. Full details on how the data was processed are given in the Materials and Methods section as well as on GitLab. Raw code to reproduce all the figures from unprocessed single-cell spaSPT data is also available at GitLab: <a href="https://gitlab.com/anders.sejr.hansen/anisotropy">https://gitlab.com/anders.sejr.hansen/anisotropy</a></p> <p>All the HMM-classified data for each cell line at each frame rate is stored in a single MAT-file. The MAT-file contains two key cell arrays: “CellTracks” and “CellTrackViterbiClass”. The two cell arrays contain the XY-coordinates and classification, respectively. More, specifically, suppose trajectory <em>k</em> is made up of <em>n</em> localizations. Then:</p> <p>CellTracks{k}: a matrix of <em>n</em> rows and 2 columns containing the x,y coordinates in units of micrometers.</p> <p>CellTrackViterbiClass{k}: a column vector of length <em>n</em>-1 where each entry is an integer: either “1” or “2”. Thus, the length of this vector is 1 less than the number of rows in the CellTracks matrix. This is because only the displacements and not the localizations are classified. For example, the CellTrackViterbiClass{k} reads [1;1;1;2;2] it means that there were 6 localizations and that the first 3 displacements (1à2, 2à3, 3à4) were classified as “bound” and the last 2 displacements (4à5, 5à6) were classified as “free”. </p> <p> </p> <p> </p> <p><strong>References</strong></p> <p>Elf, J., Li, G.-W., and Xie, X.S. (2007). Probing transcription factor dynamics at the single-molecule level in a living cell. Science <em>316</em>, 1191–1194.</p> <p>Grimm, J.B., English, B.P., Choi, H., Muthusamy, A.K., Mehl, B.P., Dong, P., Brown, T.A., Lippincott-Schwartz, J., Liu, Z., Lionnet, T., et al. (2016). Bright photoactivatable fluorophores for single-molecule imaging. Nat. Methods 66779.</p> <p>Hansen, A.S., Pustova, I., Cattoglio, C., Tjian, R., and Darzacq, X. (2017). CTCF and cohesin regulate chromatin loop stability with distinct dynamics. Elife <em>6</em>.</p> <p>Hansen, A.S., Woringer, M., Grimm, J.B., Lavis, L.D., Tjian, R., and Darzacq, X. (2018). Robust model-based analysis of single-particle tracking experiments with Spot-On. Elife <em>7</em>, e33125.</p> <p>Manley, S., Gillette, J.M., Patterson, G.H., Shroff, H., Hess, H.F., Betzig, E., and Lippincott-Schwartz, J. (2008). High-density mapping of single-molecule trajectories with photoactivated localization microscopy. Nat. Methods <em>5</em>, 155–157.</p> <p>Persson, F., Lindén, M., Unoson, C., and Elf, J. (2013). Extracting intracellular diffusive states and transition rates from single-molecule tracking data. Nat. Methods <em>10</em>, 265–269.</p> <p>Pettitt, S.J., Liang, Q., Rairdan, X.Y., Moran, J.L., Prosser, H.M., Beier, D.R., Lloyd, K.C., Bradley, A., and Skarnes, W.C. (2009). Agouti C57BL/6N embryonic stem cells for mouse genetic resources. Nat. Methods <em>6</em>, 493–495.</p> <p>Teves, S.S., An, L., Hansen, A.S., Xie, L., Darzacq, X., and Tjian, R. (2016). A dynamic mode of mitotic bookmarking by transcription factors. Elife <em>5</em>.</p> <p> </p> <p> </p> <p> </p>
Processed single cell data from CODEX multiplexed imaging of the human intestine
<p>We performed CODEX (co-detection by indexing) multiplexed imaging on 64 sections of the human intestine (~16 mm2) from 8 donors (B004, B005, B006, B008, B009, B010, B011, and B012) using a panel of 57 oligonucleotide-barcoded antibodies. Subsequently, images underwent standard CODEX image processing (tile stitching, drift compensation, cycle concatenation, background subtraction, deconvolution, and determination of best focal plane), single cell segmentation, and column marker z-normalization by tissue. The outputs of this process were data frames of 2.6 million cells with 57 antibody fluorescence values quantified from each marker. Each cell has its cell type, cellular neighborhood, community of neighborhooods, and tissue unit defined with x, y coordinates representing pixel location in the original image. This is from a total of 25 cell types, 20 multicellular neighborhoods, 10 communities of neighborhoods, and 3 tissue segments that could be used to understand the cellular interactions, composition, and structure of the human intestine from the duodenum to the sigmoid colon and understand differences between different areas of the intestine. This data could be used as a healthy baseline to compare other single-cell datasets of the human intestine, particularly multiplexed imaging ones. </p> <p>The overall structure of the datasets is individual cells segmented out in each row. Columns MUC2 through CD161 are the markers used for clustering the cell types. These are the columns that are the values of the antibody staining the target protein within the tissue quantified at the single-cell level. This value is the per cell/area averaged fluorescent intensity that has subsequently been z normalized along each column as described above. OLFM4 through MUC6 were captured in the quantification but not used within the clustering of cell types. Other columns are explained in the table in the Usage Notes section below.</p> <p>Along with this main data table, there is also a donor metadata table that links the donor ids to clinical metadata such as: age, sex, race, BMI, history of diabetes, history of cancer, history of hypertension, and history of gastorintestinal disease.</p> <p>The raw imaging data can be found at (<a href="https://portal.hubmapconsortium.org/">https://portal.hubmapconsortium.org/</a>). We have created a landing page with links to all the raw dataset IDs and the HuBMAP ID for this Collection is HBM692.JRZB.356 and the DOI is:10.35079/HBM692.JRZB.356. This can be used to also pair it with the matched snRNAseq and snATACseq for each section of tissue.</p>
Supplementary materials (processed data) for paper "Electrical signal transfer characteristics of mycelium-bound composites and fungal fruiting bodies."
<p>Processed data for paper "Electrical signal transfer characteristics of mycelium-bound composites and fungal fruiting bodies."</p>
Data and Code Supplement to: "Processes of change in a randomized clinical trial of Radically Open Dialectical Behavior Therapy (RO DBT) for adults with treatment refractory depression"
<p>Dataset to support secondary analyses reported in "Processes of change in a randomized clinical trial of Radically Open Dialectical Behavior Therapy (RO DBT) for adults with treatment refractory depression" in the Journal of Consulting and Clinical Psychology</p>
Data for: Historical and contemporary processes drive global phylogenetic structure across geographical scales: Insights from bat communities
<p><strong>Aim</strong>: Patterns of evolutionary relatedness among co-occurring species are driven by scale-dependent contemporary and historical processes. Yet, we still lack a detailed understanding of how these drivers impact the phylogenetic structure of biological communities. Here, we focused on bats – one of the most speciose and vagile groups of mammals – and test the predictions of three general biogeographical hypotheses that are particularly relevant to understanding how paleoclimatic stability, local diversification rates, and geographical scales shaped their present-day phylogenetic community structure.</p> <p><strong>Location</strong>: Worldwide, across restrictive geographical extents: global, east-west hemispheres, biogeographical realms, tectonic plates, biomes, and ecoregions.</p> <p><strong>Time period</strong>: Last Glacial Maximum (~22,000 years ago) to the present.</p> <p><strong>Major taxa studied</strong>: Bats (Chiroptera)</p> <p><strong>Methods</strong>: We estimated bat phylogenetic community structure across restrictive geographical extents and modelled it as a function of paleoclimatic stability, and in situ net diversification rates.</p> <p><strong>Results</strong>: Limiting geographical extents from larger to smaller scales strongly changed the phylogenetic structure of bat communities. The magnitude of these effects is less noticeable in the western hemisphere, where frequent among-realm biota interchange could have been maintained through bats' adaptive traits. Highly phylogenetically related bat communities are generally more common in regions that changed less in climate since the last glacial maximum, supporting the expectation that stable climates allow for increased phylogenetic clustering. Finally, increased in situ net diversification rates are associated with greater phylogenetic clustering in bat communities.</p> <p><strong>Main conclusions</strong>: We show that the worldwide phylogenetic structure of bat assemblages varies as a function of geographical extents, dispersal barriers, paleoclimatic stability and in situ diversification. The integrative framework used in our study, which can be applied to other taxonomic groups, has proven useful to not only explain the evolutionary dynamics of community assembly but could also help tackle questions related to scale dependence in community ecology and biogeography.</p>
Supplementary data for: "Towards automatic generation of control structures for Process Flow Diagrams (PFDs) with Artificial Intelligence"
<p>Uploaded on 20. February 2023</p> <p>This is the supplementary data for the publication</p> <p>"Towards automatic generation of control structures for Process Flow Diagrams (PFDs) with Artificial Intelligence" (2023) by Edwin Hirtreiter, Lukas Schulze Balhorn, Artur M. Schweidtmann</p> <p>Corresponding author: A. M. Schweidtmann, E-mail: a.schweidtmann@tudelft.nl<br> Delft University of Technology, Department of Chemical Engineering, Process Intelligence Group, Van der Maasweg 9, 2629 HZ Delft, The Netherlands</p> <p>The folder contains json files with the training (train), test (test), and augmented training (train_augm) data files. The json files contain syntetically generated SFILES. </p> <p>The pre-print of the manuscript is accessible at https://doi.org/10.48550/arXiv.2211.05583</p>
IMPROVER: the new probabilistic post processing system at the UK Met Office: BAMS paper Data
<p>© Crown Copyright, Met Office</p> <p>This is the data associated with the figures in the IMPROVER BAMS paper 2023: <a href="https://doi.org/10.1175/BAMS-D-21-0273.1">https://doi.org/10.1175/BAMS-D-21-0273.1</a>.</p> <p>Gridded data is in CF-NetCDF with reasonably self explanatory metadata, other data such as for Figure 9's wind speed calibration is in CSV.</p>
Tissue microarray data and processing scripts for The molecular consequences of androgen activity in the human breast
<p>This repository contains raw and processed data from the CODEX imaging dataset in this publication.</p> <p>The RAW data tables provide the resulting nuclei and membrane staining signals obtained from the nuclei segmentation described in the Methods.</p> <p>The processed data file provides the clustered and annotated version described in Methods.</p> <p>The repository also contains two scripts describing the processing of snRNA-seq and snATAC-seq data.</p>
i-Box (Innsbruck Box) – processed eddy-covariance data: 2-min statistics
<p><strong>Abstract</strong></p> <p>The dataset contains eddy-covariance data from five i-Box stations in the Austrian Inn Valley, which have been processed to 2-min statistics. The i-Box is a long-term measurement platform, including a small network of eddy-covariance stations in the lower Inn Valley, to study boundary-layer processes in mountainous terrain. More information about the i-Box can be found at <a href="https://www.uibk.ac.at/acinn/research/atmospheric-dynamics/projects/innsbruck-box-i-box.html.en">https://www.uibk.ac.at/acinn/research/atmospheric-dynamics/projects/innsbruck-box-i-box.html.en</a> and in Rotach et al. (2017).</p> <p> </p> <p><strong>Data description</strong></p> <p><em>Station locations</em></p> <p>The present dataset contains processed data from five i-Box stations located in the Austrian Inn Valley. The Inn Valley is an approximately southwest-northeast oriented valley in the western part of Austria, with a depth of about 2000 m and a width of about 2 km at the valley floor. The locations of the sites are shown in the overview figure i-Box_sites.pdf.</p> <ul> <li> <p>VF0 is located at the almost flat valley floor. The site is surrounded by grassland and agricultural fields. (47.305°N, 11.622°E, 545 m MSL)</p> </li> <li> <p>SF8 is located at the foot of the north sidewall next to a steep embankment between an agricultural field and a concrete parking lot. (47.326°N, 11.652°E, 575 m MSL)</p> </li> <li> <p>SF1 is located on an almost flat plateau running along the northern valley sidewall. The site is mainly surrounded by grassland and agricultural fields. (47.317°N, 11.616°E, 829 m MSL)</p> </li> <li> <p>NF10 is located on an approximately 10 deg slope on the south sidewall, covered by grassland. (47.300°N, 11.673°E, 930 m MSL)</p> </li> <li> <p>NF27 is located on a steep, grass-covered slope on the south sidewall, with a slope angle of about 25 deg. (47.288°N, 11.631°E, 1009 m MSL)</p> </li> </ul> <p>Further information about station locations can be found in Rotach et al. (2017) and Lehner et al. (2021).</p> <p><em>Temporal coverage</em></p> <p>The dataset contains processed data between 2014 and 2020. Some instruments were replaced and new instruments were added during this period. Data gaps occur as a result of instrument malfunctions and maintenance.</p> <p><em>Instrumentation</em></p> <p>Each station is equipped with at least one sonic anemometer and a gas analyzer. The instrumentation usually consists of a CSAT3 sonic anemometer (Campbell Scientific, USA) and KH20 Krypton hygrometer (Campbell Scientific) or an EC150 open-path infrared gas analyzer (Campbell Scientific). In 2020, several of the instruments were replaced with an Irgason (Campbell Scientific), which combines an open-path infrared gas analyzer with a sonic anemometer. Pressure, air temperature, and humidity used for calculating flux corrections are measured with Setra 278 sensors (Setra Systems, USA) and Rotronic HC2A-S temperature and humidity probes (Rotronic, Switzerland).</p> <ul> <li> <p>VF0: CSAT3 and EC150 at 4.0 m, CSAT3 at 8.7 m, CSAT3 and KH20 (until July 2020) or Irgason (since July 2020) at 16.9 m</p> </li> <li> <p>SF8: CSAT3 at 6.1, CSAT3 and KH20 (until September 2020) or Irgason (since September 2020) at 11.2 m</p> </li> <li> <p>SF1: CSAT3 and KH20 (until June 2020) or Irgason (since June 2020) at 6.8 m</p> </li> <li> <p>NF10: CSAT3 and KH20 (until June 2020) or Irgason (since June 2020) at 5.7 m</p> </li> <li> <p>NF27: CSAT3 at 1.5 (since September 2017), CSAT3 and KH20 (until November 2016) or Irgason (since September 2017) 6.8 m</p> </li> </ul> <p>Further information about the instrumentation can be found in Rotach et al. (2017), Lehner et al. (2021), and in the ACINN database:</p> <ul> <li> <p>VF0: <a href="https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html">https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html</a></p> </li> <li> <p>SF8: <a href="https://acinn-data.uibk.ac.at/pages/i-box-terfens.html">https://acinn-data.uibk.ac.at/pages/i-box-terfens.html</a></p> </li> <li> <p>SF1: <a href="https://acinn-data.uibk.ac.at/pages/i-box-eggen.html">https://acinn-data.uibk.ac.at/pages/i-box-eggen.html</a></p> </li> <li> <p>NF10: <a href="https://acinn-data.uibk.ac.at/pages/i-box-weerberg.html">https://acinn-data.uibk.ac.at/pages/i-box-weerberg.html</a></p> </li> <li> <p>NF27: <a href="https://acinn-data.uibk.ac.at/pages/i-box-hochhaeuser.html">https://acinn-data.uibk.ac.at/pages/i-box-hochhaeuser.html</a></p> </li> </ul> <p><em>Data processing</em></p> <p>Raw 20-Hz data were quality controlled and rotated into a streamline coordinate system using double rotation before block averaging the data to 2-min statistics, without previous filtering. Flux corrections were applied to the turbulence statistics, including a frequency response correction (Aubinet et al. 2012) with spectral models following Moore (1986), Højstrup (1981), and Kaimal et al. (1972); a sonic heat-flux correction of the vertical heat flux and the temperature variance (Schotanus et al. 1983); a WPL correction of the vertical moisture flux (Webb et al. 1980); and an Oxygen correction of the vertical moisture flux for data from Krypton hygrometers (van Dijk et al. 2003).</p> <p>The quality control procedures include the removal of data during periods of instrument malfunction as indicated by the instruments’ quality flags, a despiking, the removal of data points exceeding 30 m s<sup>-1</sup> for the horizontal wind components, 10 m s<sup>-1</sup> for the vertical wind velocity, and 50 g m<sup>3</sup> for water vapor density, and the removal of sonic temperature data outside the range -20 – 40°C. The removed data are replaced with random values drawn from a Gaussian distribution, with its mean and standard deviation calculated over a 30-s data window.</p> <p>Quality flags are based on the criteria described in Stiperski and Rotach (2016):</p> <ul> <li> <p>-1: More than 10% of the raw data within the averaging period are replaced during the quality control.</p> </li> <li> <p>0: More than 90% of the raw data fulfill the quality control criteria.</p> </li> <li> <p>1: In addition to fulfilling the quality control criteria, the skewness is within the range -2–2 and the kurtosis is less than 8.</p> </li> <li> <p>2: In addition to the above criteria, the stationarity test by Foken and Wichura (1996) is below 30% and the uncertainty is less than 50% based on Stiperski and Rotach (2016) and Wyngaard (1973)</p> </li> </ul> <p><em>Data files</em></p> <ul> <li> <p>i-Box_sites.pdf contains a map of the i-Box stations.</p> </li> <li> <p>list_variables.pdf contains a list of variable names with a short description.</p> </li> <li> <p>SITENAME_2min.zip contains the processed turbulence statistics, split into yearly files. There is more than one file per year if the instrumentation changed during the year or because of memory restrictions during the processing.</p> </li> </ul> <p><em>Acknowledgments</em></p> <p>Data processing was performed in the framework of the TExSMBL (Turbulent Exchange in the Stable Mountain Boundary Layer) project funded by the Austrian Science Fund (FWF) under grant V 791-N. Data were processed on the LEO HPC infrastructure of the University of Innsbruck.</p> <p><em>References</em></p> <p>Aubinet M, Vesala T, D P (eds) (2012) Eddy Covariance. A practical guide to measurements and data analysis. Springer, Dordrecht, DOI 10.1007/978-94-007-2351-1</p> <p>Højstrup J (1981) A simple model for the adjustment of velocity spectra in unstable conditions downstream of an abrupt change in roughness and heat flux. Boundary-Layer Meteorol 21:341–356, DOI 10.1007/bf00119278</p> <p>Kaimal JC, Wyngaard JC, Izumi Y, Coté OR (1972) Spectral characteristics of surface-layer turbulence. Q J R M Soc 98:563–589, DOI 10.1002/qj.49709841707</p> <p>Lehner M, Rotach MW, Sfyri E, Obleitner F (2021) Spatial and temporal variations in near-surface energy fluxes in an Alpine valley under synoptically undisturbed and clear-sky conditions. Q J R M Soc 147:2173–2196, DOI 10.1002/qj.4016</p> <p>Moore CJ (1986) Frequency response corrections for eddy correlation systems. Boundary-Layer Meteorol 37:17–35, DOI 10.1007/BF00122754</p> <p>Rotach MW, Stiperski I, Fuhrer O, Goger B, Gohm A, Obleitner F, Rau G, Sfyri E, Vergeiner J (2017) Investigating exchange processes over complex topography—the Innsbruck Box (i-Box). Bull Amer Meteorol Soc 98:787–805, DOI 10.1175/BAMS-D-15-00246.1</p> <p>Schotanus P, Nieuwstadt FTM, de Bruijn HAR (1983) Temperature measurement with a sonic anemometer and its application to heat and moisture fluxes. Boundary-Layer Meteorol 26:81–93, DOI 10.1007/BF00164332</p> <p>Stiperski, I. and Rotach, M.W. (2016) On the measurement of turbulence over complex mountainous terrain. Boundary-Layer Meteorology, 159, 97–121. DOI 10.1007/s10546-015-0103-z.</p> <p>Van Dijk A, Kohsiek W, de Bruin HAR (2003) Oxygen sensitivity of Krypton and Lyman-α hygrometers. J Atmos Ocean Technol 20:143–151, DOI 10.1175/1520-0426(2003)020¡0143:OSOKAL¿2.0.CO;2</p> <p>Webb EK, Pearman GI, R L (1980) Correction of flux measurements for density effects due to heat and water vapour transfer. Q J R M Soc 106:85–100, DOI 10.1002/qj.49710644707</p> <p>Wyngaard, J.C. (1973). On surface layer turbulence. In D.A. Haugen (Ed.), Workshop on Micrometeorology, American Meteorological Society, pp. 101–150.</p>
i-Box (Innsbruck Box) – processed eddy-covariance data: 1-min statistics
<p><strong>Abstract</strong></p> <p>The dataset contains eddy-covariance data from five i-Box stations in the Austrian Inn Valley, which have been processed to 1-min statistics. The i-Box is a long-term measurement platform, including a small network of eddy-covariance stations in the lower Inn Valley, to study boundary-layer processes in mountainous terrain. More information about the i-Box can be found at <a href="https://www.uibk.ac.at/acinn/research/atmospheric-dynamics/projects/innsbruck-box-i-box.html.en">https://www.uibk.ac.at/acinn/research/atmospheric-dynamics/projects/innsbruck-box-i-box.html.en</a> and in Rotach et al. (2017).</p> <p> </p> <p><strong>Data description</strong></p> <p><em>Station locations</em></p> <p>The present dataset contains processed data from five i-Box stations located in the Austrian Inn Valley. The Inn Valley is an approximately southwest-northeast oriented valley in the western part of Austria, with a depth of about 2000 m and a width of about 2 km at the valley floor. The locations of the sites are shown in the overview figure i-Box_sites.pdf.</p> <ul> <li> <p>VF0 is located at the almost flat valley floor. The site is surrounded by grassland and agricultural fields. (47.305°N, 11.622°E, 545 m MSL)</p> </li> <li> <p>SF8 is located at the foot of the north sidewall next to a steep embankment between an agricultural field and a concrete parking lot. (47.326°N, 11.652°E, 575 m MSL)</p> </li> <li> <p>SF1 is located on an almost flat plateau running along the northern valley sidewall. The site is mainly surrounded by grassland and agricultural fields. (47.317°N, 11.616°E, 829 m MSL)</p> </li> <li> <p>NF10 is located on an approximately 10 deg slope on the south sidewall, covered by grassland. (47.300°N, 11.673°E, 930 m MSL)</p> </li> <li> <p>NF27 is located on a steep, grass-covered slope on the south sidewall, with a slope angle of about 25 deg. (47.288°N, 11.631°E, 1009 m MSL)</p> </li> </ul> <p>Further information about station locations can be found in Rotach et al. (2017) and Lehner et al. (2021).</p> <p><em>Temporal coverage</em></p> <p>The dataset contains processed data between 2014 and 2020. Some instruments were replaced and new instruments were added during this period. Data gaps occur as a result of instrument malfunctions and maintenance.</p> <p><em>Instrumentation</em></p> <p>Each station is equipped with at least one sonic anemometer and a gas analyzer. The instrumentation usually consists of a CSAT3 sonic anemometer (Campbell Scientific, USA) and KH20 Krypton hygrometer (Campbell Scientific) or an EC150 open-path infrared gas analyzer (Campbell Scientific). In 2020, several of the instruments were replaced with an Irgason (Campbell Scientific), which combines an open-path infrared gas analyzer with a sonic anemometer. Pressure, air temperature, and humidity used for calculating flux corrections are measured with Setra 278 sensors (Setra Systems, USA) and Rotronic HC2A-S temperature and humidity probes (Rotronic, Switzerland).</p> <ul> <li> <p>VF0: CSAT3 and EC150 at 4.0 m, CSAT3 at 8.7 m, CSAT3 and KH20 (until July 2020) or Irgason (since July 2020) at 16.9 m</p> </li> <li> <p>SF8: CSAT3 at 6.1, CSAT3 and KH20 (until September 2020) or Irgason (since September 2020) at 11.2 m</p> </li> <li> <p>SF1: CSAT3 and KH20 (until June 2020) or Irgason (since June 2020) at 6.8 m</p> </li> <li> <p>NF10: CSAT3 and KH20 (until June 2020) or Irgason (since June 2020) at 5.7 m</p> </li> <li> <p>NF27: CSAT3 at 1.5 (since September 2017), CSAT3 and KH20 (until November 2016) or Irgason (since September 2017) 6.8 m</p> </li> </ul> <p>Further information about the instrumentation can be found in Rotach et al. (2017), Lehner et al. (2021), and in the ACINN database:</p> <ul> <li> <p>VF0: <a href="https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html">https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html</a></p> </li> <li> <p>SF8: <a href="https://acinn-data.uibk.ac.at/pages/i-box-terfens.html">https://acinn-data.uibk.ac.at/pages/i-box-terfens.html</a></p> </li> <li> <p>SF1: <a href="https://acinn-data.uibk.ac.at/pages/i-box-eggen.html">https://acinn-data.uibk.ac.at/pages/i-box-eggen.html</a></p> </li> <li> <p>NF10: <a href="https://acinn-data.uibk.ac.at/pages/i-box-weerberg.html">https://acinn-data.uibk.ac.at/pages/i-box-weerberg.html</a></p> </li> <li> <p>NF27: <a href="https://acinn-data.uibk.ac.at/pages/i-box-hochhaeuser.html">https://acinn-data.uibk.ac.at/pages/i-box-hochhaeuser.html</a></p> </li> </ul> <p><em>Data processing</em></p> <p>Raw 20-Hz data were quality controlled and rotated into a streamline coordinate system using double rotation before block averaging the data to 1-min statistics, without previous filtering. Flux corrections were applied to the turbulence statistics, including a frequency response correction (Aubinet et al. 2012) with spectral models following Moore (1986), Højstrup (1981), and Kaimal et al. (1972); a sonic heat-flux correction of the vertical heat flux and the temperature variance (Schotanus et al. 1983); a WPL correction of the vertical moisture flux (Webb et al. 1980); and an Oxygen correction of the vertical moisture flux for data from Krypton hygrometers (van Dijk et al. 2003).</p> <p>The quality control procedures include the removal of data during periods of instrument malfunction as indicated by the instruments’ quality flags, a despiking, the removal of data points exceeding 30 m s<sup>-1</sup> for the horizontal wind components, 10 m s<sup>-1</sup> for the vertical wind velocity, and 50 g m<sup>3</sup> for water vapor density, and the removal of sonic temperature data outside the range -20 – 40°C. The removed data are replaced with random values drawn from a Gaussian distribution, with its mean and standard deviation calculated over a 30-s data window.</p> <p>Quality flags are based on the criteria described in Stiperski and Rotach (2016):</p> <ul> <li> <p>-1: More than 10% of the raw data within the averaging period are replaced during the quality control.</p> </li> <li> <p>0: More than 90% of the raw data fulfill the quality control criteria.</p> </li> <li> <p>1: In addition to fulfilling the quality control criteria, the skewness is within the range -2–2 and the kurtosis is less than 8.</p> </li> <li> <p>2: In addition to the above criteria, the stationarity test by Foken and Wichura (1996) is below 30% and the uncertainty is less than 50% based on Stiperski and Rotach (2016) and Wyngaard (1973)</p> </li> </ul> <p><em>Data files</em></p> <ul> <li> <p>i-Box_sites.pdf contains a map of the i-Box stations.</p> </li> <li> <p>list_variables.pdf contains a list of variable names with a short description.</p> </li> <li> <p>SITENAME_1min.zip contains the processed turbulence statistics, split into yearly files. There is more than one file per year if the instrumentation changed during the year or because of memory restrictions during the processing.</p> </li> </ul> <p><em>Acknowledgments</em></p> <p>Data processing was performed in the framework of the TExSMBL (Turbulent Exchange in the Stable Mountain Boundary Layer) project funded by the Austrian Science Fund (FWF) under grant V 791-N. Data were processed on the LEO HPC infrastructure of the University of Innsbruck.</p> <p><em>References</em></p> <p>Aubinet M, Vesala T, D P (eds) (2012) Eddy Covariance. A practical guide to measurements and data analysis. Springer, Dordrecht, DOI 10.1007/978-94-007-2351-1</p> <p>Højstrup J (1981) A simple model for the adjustment of velocity spectra in unstable conditions downstream of an abrupt change in roughness and heat flux. Boundary-Layer Meteorol 21:341–356, DOI 10.1007/bf00119278</p> <p>Kaimal JC, Wyngaard JC, Izumi Y, Coté OR (1972) Spectral characteristics of surface-layer turbulence. Q J R M Soc 98:563–589, DOI 10.1002/qj.49709841707</p> <p>Lehner M, Rotach MW, Sfyri E, Obleitner F (2021) Spatial and temporal variations in near-surface energy fluxes in an Alpine valley under synoptically undisturbed and clear-sky conditions. Q J R M Soc 147:2173–2196, DOI 10.1002/qj.4016</p> <p>Moore CJ (1986) Frequency response corrections for eddy correlation systems. Boundary-Layer Meteorol 37:17–35, DOI 10.1007/BF00122754</p> <p>Rotach MW, Stiperski I, Fuhrer O, Goger B, Gohm A, Obleitner F, Rau G, Sfyri E, Vergeiner J (2017) Investigating exchange processes over complex topography—the Innsbruck Box (i-Box). Bull Amer Meteorol Soc 98:787–805, DOI 10.1175/BAMS-D-15-00246.1</p> <p>Schotanus P, Nieuwstadt FTM, de Bruijn HAR (1983) Temperature measurement with a sonic anemometer and its application to heat and moisture fluxes. Boundary-Layer Meteorol 26:81–93, DOI 10.1007/BF00164332</p> <p>Stiperski, I. and Rotach, M.W. (2016) On the measurement of turbulence over complex mountainous terrain. Boundary-Layer Meteorology, 159, 97–121. DOI 10.1007/s10546-015-0103-z.</p> <p>Van Dijk A, Kohsiek W, de Bruin HAR (2003) Oxygen sensitivity of Krypton and Lyman-α hygrometers. J Atmos Ocean Technol 20:143–151, DOI 10.1175/1520-0426(2003)020¡0143:OSOKAL¿2.0.CO;2</p> <p>Webb EK, Pearman GI, R L (1980) Correction of flux measurements for density effects due to heat and water vapour transfer. Q J R M Soc 106:85–100, DOI 10.1002/qj.49710644707</p> <p>Wyngaard, J.C. (1973). On surface layer turbulence. In D.A. Haugen (Ed.), Workshop on Micrometeorology, American Meteorological Society, pp. 101–150.</p>
i-Box (Innsbruck Box) – processed eddy-covariance data: 10-min statistics
<p><strong>Abstract</strong></p> <p>The dataset contains eddy-covariance data from five i-Box stations in the Austrian Inn Valley, which have been processed to 10-min statistics. The i-Box is a long-term measurement platform, including a small network of eddy-covariance stations in the lower Inn Valley, to study boundary-layer processes in mountainous terrain. More information about the i-Box can be found at <a href="https://www.uibk.ac.at/acinn/research/atmospheric-dynamics/projects/innsbruck-box-i-box.html.en">https://www.uibk.ac.at/acinn/research/atmospheric-dynamics/projects/innsbruck-box-i-box.html.en</a> and in Rotach et al. (2017).</p> <p> </p> <p><strong>Data description</strong></p> <p><em>Station locations</em></p> <p>The present dataset contains processed data from five i-Box stations located in the Austrian Inn Valley. The Inn Valley is an approximately southwest-northeast oriented valley in the western part of Austria, with a depth of about 2000 m and a width of about 2 km at the valley floor. The locations of the sites are shown in the overview figure i-Box_sites.pdf.</p> <ul> <li> <p>VF0 is located at the almost flat valley floor. The site is surrounded by grassland and agricultural fields. (47.305°N, 11.622°E, 545 m MSL)</p> </li> <li> <p>SF8 is located at the foot of the north sidewall next to a steep embankment between an agricultural field and a concrete parking lot. (47.326°N, 11.652°E, 575 m MSL)</p> </li> <li> <p>SF1 is located on an almost flat plateau running along the northern valley sidewall. The site is mainly surrounded by grassland and agricultural fields. (47.317°N, 11.616°E, 829 m MSL)</p> </li> <li> <p>NF10 is located on an approximately 10 deg slope on the south sidewall, covered by grassland. (47.300°N, 11.673°E, 930 m MSL)</p> </li> <li> <p>NF27 is located on a steep, grass-covered slope on the south sidewall, with a slope angle of about 25 deg. (47.288°N, 11.631°E, 1009 m MSL)</p> </li> </ul> <p>Further information about station locations can be found in Rotach et al. (2017) and Lehner et al. (2021).</p> <p><em>Temporal coverage</em></p> <p>The dataset contains processed data between 2014 and 2020. Some instruments were replaced and new instruments were added during this period. Data gaps occur as a result of instrument malfunctions and maintenance.</p> <p><em>Instrumentation</em></p> <p>Each station is equipped with at least one sonic anemometer and a gas analyzer. The instrumentation usually consists of a CSAT3 sonic anemometer (Campbell Scientific, USA) and KH20 Krypton hygrometer (Campbell Scientific) or an EC150 open-path infrared gas analyzer (Campbell Scientific). In 2020, several of the instruments were replaced with an Irgason (Campbell Scientific), which combines an open-path infrared gas analyzer with a sonic anemometer. Pressure, air temperature, and humidity used for calculating flux corrections are measured with Setra 278 sensors (Setra Systems, USA) and Rotronic HC2A-S temperature and humidity probes (Rotronic, Switzerland).</p> <ul> <li> <p>VF0: CSAT3 and EC150 at 4.0 m, CSAT3 at 8.7 m, CSAT3 and KH20 (until July 2020) or Irgason (since July 2020) at 16.9 m</p> </li> <li> <p>SF8: CSAT3 at 6.1, CSAT3 and KH20 (until September 2020) or Irgason (since September 2020) at 11.2 m</p> </li> <li> <p>SF1: CSAT3 and KH20 (until June 2020) or Irgason (since June 2020) at 6.8 m</p> </li> <li> <p>NF10: CSAT3 and KH20 (until June 2020) or Irgason (since June 2020) at 5.7 m</p> </li> <li> <p>NF27: CSAT3 at 1.5 (since September 2017), CSAT3 and KH20 (until November 2016) or Irgason (since September 2017) 6.8 m</p> </li> </ul> <p>Further information about the instrumentation can be found in Rotach et al. (2017), Lehner et al. (2021), and in the ACINN database:</p> <ul> <li> <p>VF0: <a href="https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html">https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html</a></p> </li> <li> <p>SF8: <a href="https://acinn-data.uibk.ac.at/pages/i-box-terfens.html">https://acinn-data.uibk.ac.at/pages/i-box-terfens.html</a></p> </li> <li> <p>SF1: <a href="https://acinn-data.uibk.ac.at/pages/i-box-eggen.html">https://acinn-data.uibk.ac.at/pages/i-box-eggen.html</a></p> </li> <li> <p>NF10: <a href="https://acinn-data.uibk.ac.at/pages/i-box-weerberg.html">https://acinn-data.uibk.ac.at/pages/i-box-weerberg.html</a></p> </li> <li> <p>NF27: <a href="https://acinn-data.uibk.ac.at/pages/i-box-hochhaeuser.html">https://acinn-data.uibk.ac.at/pages/i-box-hochhaeuser.html</a></p> </li> </ul> <p><em>Data processing</em></p> <p>Raw 20-Hz data were quality controlled and rotated into a streamline coordinate system using double rotation before block averaging the data to 10-min statistics, without previous filtering. Flux corrections were applied to the turbulence statistics, including a frequency response correction (Aubinet et al. 2012) with spectral models following Moore (1986), Højstrup (1981), and Kaimal et al. (1972); a sonic heat-flux correction of the vertical heat flux and the temperature variance (Schotanus et al. 1983); a WPL correction of the vertical moisture flux (Webb et al. 1980); and an Oxygen correction of the vertical moisture flux for data from Krypton hygrometers (van Dijk et al. 2003).</p> <p>The quality control procedures include the removal of data during periods of instrument malfunction as indicated by the instruments’ quality flags, a despiking, the removal of data points exceeding 30 m s<sup>-1</sup> for the horizontal wind components, 10 m s<sup>-1</sup> for the vertical wind velocity, and 50 g m<sup>3</sup> for water vapor density, and the removal of sonic temperature data outside the range -20 – 40°C. The removed data are replaced with random values drawn from a Gaussian distribution, with its mean and standard deviation calculated over a 30-s data window.</p> <p>Quality flags are based on the criteria described in Stiperski and Rotach (2016):</p> <ul> <li> <p>-1: More than 10% of the raw data within the averaging period are replaced during the quality control.</p> </li> <li> <p>0: More than 90% of the raw data fulfill the quality control criteria.</p> </li> <li> <p>1: In addition to fulfilling the quality control criteria, the skewness is within the range -2–2 and the kurtosis is less than 8.</p> </li> <li> <p>2: In addition to the above criteria, the stationarity test by Foken and Wichura (1996) is below 30% and the uncertainty is less than 50% based on Stiperski and Rotach (2016) and Wyngaard (1973)</p> </li> </ul> <p><em>Data files</em></p> <ul> <li> <p>i-Box_sites.pdf contains a map of the i-Box stations.</p> </li> <li> <p>list_variables.pdf contains a list of variable names with a short description.</p> </li> <li> <p>SITENAME_10min.zip contains the processed turbulence statistics, split into yearly files. There is more than one file per year if the instrumentation changed during the year or because of memory restrictions during the processing.</p> </li> </ul> <p><em>Acknowledgments</em></p> <p>Data processing was performed in the framework of the TExSMBL (Turbulent Exchange in the Stable Mountain Boundary Layer) project funded by the Austrian Science Fund (FWF) under grant V 791-N. Data were processed on the LEO HPC infrastructure of the University of Innsbruck.</p> <p><em>References</em></p> <p>Aubinet M, Vesala T, D P (eds) (2012) Eddy Covariance. A practical guide to measurements and data analysis. Springer, Dordrecht, DOI 10.1007/978-94-007-2351-1</p> <p>Højstrup J (1981) A simple model for the adjustment of velocity spectra in unstable conditions downstream of an abrupt change in roughness and heat flux. Boundary-Layer Meteorol 21:341–356, DOI 10.1007/bf00119278</p> <p>Kaimal JC, Wyngaard JC, Izumi Y, Coté OR (1972) Spectral characteristics of surface-layer turbulence. Q J R M Soc 98:563–589, DOI 10.1002/qj.49709841707</p> <p>Lehner M, Rotach MW, Sfyri E, Obleitner F (2021) Spatial and temporal variations in near-surface energy fluxes in an Alpine valley under synoptically undisturbed and clear-sky conditions. Q J R M Soc 147:2173–2196, DOI 10.1002/qj.4016</p> <p>Moore CJ (1986) Frequency response corrections for eddy correlation systems. Boundary-Layer Meteorol 37:17–35, DOI 10.1007/BF00122754</p> <p>Rotach MW, Stiperski I, Fuhrer O, Goger B, Gohm A, Obleitner F, Rau G, Sfyri E, Vergeiner J (2017) Investigating exchange processes over complex topography—the Innsbruck Box (i-Box). Bull Amer Meteorol Soc 98:787–805, DOI 10.1175/BAMS-D-15-00246.1</p> <p>Schotanus P, Nieuwstadt FTM, de Bruijn HAR (1983) Temperature measurement with a sonic anemometer and its application to heat and moisture fluxes. Boundary-Layer Meteorol 26:81–93, DOI 10.1007/BF00164332</p> <p>Stiperski, I. and Rotach, M.W. (2016) On the measurement of turbulence over complex mountainous terrain. Boundary-Layer Meteorology, 159, 97–121. DOI 10.1007/s10546-015-0103-z.</p> <p>Van Dijk A, Kohsiek W, de Bruin HAR (2003) Oxygen sensitivity of Krypton and Lyman-α hygrometers. J Atmos Ocean Technol 20:143–151, DOI 10.1175/1520-0426(2003)020¡0143:OSOKAL¿2.0.CO;2</p> <p>Webb EK, Pearman GI, R L (1980) Correction of flux measurements for density effects due to heat and water vapour transfer. Q J R M Soc 106:85–100, DOI 10.1002/qj.49710644707</p> <p>Wyngaard, J.C. (1973). On surface layer turbulence. In D.A. Haugen (Ed.), Workshop on Micrometeorology, American Meteorological Society, pp. 101–150.</p>
i-Box (Innsbruck Box) – processed eddy-covariance data: 3-min statistics
<p><strong>Abstract</strong></p> <p>The dataset contains eddy-covariance data from five i-Box stations in the Austrian Inn Valley, which have been processed to 3-min statistics. The i-Box is a long-term measurement platform, including a small network of eddy-covariance stations in the lower Inn Valley, to study boundary-layer processes in mountainous terrain. More information about the i-Box can be found at <a href="https://www.uibk.ac.at/acinn/research/atmospheric-dynamics/projects/innsbruck-box-i-box.html.en">https://www.uibk.ac.at/acinn/research/atmospheric-dynamics/projects/innsbruck-box-i-box.html.en</a> and in Rotach et al. (2017).</p> <p> </p> <p><strong>Data description</strong></p> <p><em>Station locations</em></p> <p>The present dataset contains processed data from five i-Box stations located in the Austrian Inn Valley. The Inn Valley is an approximately southwest-northeast oriented valley in the western part of Austria, with a depth of about 2000 m and a width of about 2 km at the valley floor. The locations of the sites are shown in the overview figure i-Box_sites.pdf.</p> <ul> <li> <p>VF0 is located at the almost flat valley floor. The site is surrounded by grassland and agricultural fields. (47.305°N, 11.622°E, 545 m MSL)</p> </li> <li> <p>SF8 is located at the foot of the north sidewall next to a steep embankment between an agricultural field and a concrete parking lot. (47.326°N, 11.652°E, 575 m MSL)</p> </li> <li> <p>SF1 is located on an almost flat plateau running along the northern valley sidewall. The site is mainly surrounded by grassland and agricultural fields. (47.317°N, 11.616°E, 829 m MSL)</p> </li> <li> <p>NF10 is located on an approximately 10 deg slope on the south sidewall, covered by grassland. (47.300°N, 11.673°E, 930 m MSL)</p> </li> <li> <p>NF27 is located on a steep, grass-covered slope on the south sidewall, with a slope angle of about 25 deg. (47.288°N, 11.631°E, 1009 m MSL)</p> </li> </ul> <p>Further information about station locations can be found in Rotach et al. (2017) and Lehner et al. (2021).</p> <p><em>Temporal coverage</em></p> <p>The dataset contains processed data between 2014 and 2020. Some instruments were replaced and new instruments were added during this period. Data gaps occur as a result of instrument malfunctions and maintenance.</p> <p><em>Instrumentation</em></p> <p>Each station is equipped with at least one sonic anemometer and a gas analyzer. The instrumentation usually consists of a CSAT3 sonic anemometer (Campbell Scientific, USA) and KH20 Krypton hygrometer (Campbell Scientific) or an EC150 open-path infrared gas analyzer (Campbell Scientific). In 2020, several of the instruments were replaced with an Irgason (Campbell Scientific), which combines an open-path infrared gas analyzer with a sonic anemometer. Pressure, air temperature, and humidity used for calculating flux corrections are measured with Setra 278 sensors (Setra Systems, USA) and Rotronic HC2A-S temperature and humidity probes (Rotronic, Switzerland).</p> <ul> <li> <p>VF0: CSAT3 and EC150 at 4.0 m, CSAT3 at 8.7 m, CSAT3 and KH20 (until July 2020) or Irgason (since July 2020) at 16.9 m</p> </li> <li> <p>SF8: CSAT3 at 6.1, CSAT3 and KH20 (until September 2020) or Irgason (since September 2020) at 11.2 m</p> </li> <li> <p>SF1: CSAT3 and KH20 (until June 2020) or Irgason (since June 2020) at 6.8 m</p> </li> <li> <p>NF10: CSAT3 and KH20 (until June 2020) or Irgason (since June 2020) at 5.7 m</p> </li> <li> <p>NF27: CSAT3 at 1.5 (since September 2017), CSAT3 and KH20 (until November 2016) or Irgason (since September 2017) 6.8 m</p> </li> </ul> <p>Further information about the instrumentation can be found in Rotach et al. (2017), Lehner et al. (2021), and in the ACINN database:</p> <ul> <li> <p>VF0: <a href="https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html">https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html</a></p> </li> <li> <p>SF8: <a href="https://acinn-data.uibk.ac.at/pages/i-box-terfens.html">https://acinn-data.uibk.ac.at/pages/i-box-terfens.html</a></p> </li> <li> <p>SF1: <a href="https://acinn-data.uibk.ac.at/pages/i-box-eggen.html">https://acinn-data.uibk.ac.at/pages/i-box-eggen.html</a></p> </li> <li> <p>NF10:<a href="http://https://acinn-data.uibk.ac.at/pages/i-box-weerberg.html"> https://acinn-data.uibk.ac.at/pages/i-box-weerberg.html</a></p> </li> <li> <p>NF27: <a href="https://acinn-data.uibk.ac.at/pages/i-box-hochhaeuser.html">https://acinn-data.uibk.ac.at/pages/i-box-hochhaeuser.html</a></p> </li> </ul> <p><em>Data processing</em></p> <p>Raw 20-Hz data were quality controlled and rotated into a streamline coordinate system using double rotation before block averaging the data to 3-min statistics, without previous filtering. Flux corrections were applied to the turbulence statistics, including a frequency response correction (Aubinet et al. 2012) with spectral models following Moore (1986), Højstrup (1981), and Kaimal et al. (1972); a sonic heat-flux correction of the vertical heat flux and the temperature variance (Schotanus et al. 1983); a WPL correction of the vertical moisture flux (Webb et al. 1980); and an Oxygen correction of the vertical moisture flux for data from Krypton hygrometers (van Dijk et al. 2003).</p> <p>The quality control procedures include the removal of data during periods of instrument malfunction as indicated by the instruments’ quality flags, a despiking, the removal of data points exceeding 30 m s<sup>-1</sup> for the horizontal wind components, 10 m s<sup>-1</sup> for the vertical wind velocity, and 50 g m<sup>3</sup> for water vapor density, and the removal of sonic temperature data outside the range -20 – 40°C. The removed data are replaced with random values drawn from a Gaussian distribution, with its mean and standard deviation calculated over a 30-s data window.</p> <p>Quality flags are based on the criteria described in Stiperski and Rotach (2016):</p> <ul> <li> <p>-1: More than 10% of the raw data within the averaging period are replaced during the quality control.</p> </li> <li> <p>0: More than 90% of the raw data fulfill the quality control criteria.</p> </li> <li> <p>1: In addition to fulfilling the quality control criteria, the skewness is within the range -2–2 and the kurtosis is less than 8.</p> </li> <li> <p>2: In addition to the above criteria, the stationarity test by Foken and Wichura (1996) is below 30% and the uncertainty is less than 50% based on Stiperski and Rotach (2016) and Wyngaard (1973)</p> </li> </ul> <p><em>Data files</em></p> <ul> <li> <p>i-Box_sites.pdf contains a map of the i-Box stations.</p> </li> <li> <p>list_variables.pdf contains a list of variable names with a short description.</p> </li> <li> <p>SITENAME_3min.zip contains the processed turbulence statistics, split into yearly files. There is more than one file per year if the instrumentation changed during the year or because of memory restrictions during the processing.</p> </li> </ul> <p><em>Acknowledgments</em></p> <p>Data processing was performed in the framework of the TExSMBL (Turbulent Exchange in the Stable Mountain Boundary Layer) project funded by the Austrian Science Fund (FWF) under grant V 791-N. Data were processed on the LEO HPC infrastructure of the University of Innsbruck.</p> <p><em>References</em></p> <p>Aubinet M, Vesala T, D P (eds) (2012) Eddy Covariance. A practical guide to measurements and data analysis. Springer, Dordrecht, DOI 10.1007/978-94-007-2351-1</p> <p>Højstrup J (1981) A simple model for the adjustment of velocity spectra in unstable conditions downstream of an abrupt change in roughness and heat flux. Boundary-Layer Meteorol 21:341–356, DOI 10.1007/bf00119278</p> <p>Kaimal JC, Wyngaard JC, Izumi Y, Coté OR (1972) Spectral characteristics of surface-layer turbulence. Q J R M Soc 98:563–589, DOI 10.1002/qj.49709841707</p> <p>Lehner M, Rotach MW, Sfyri E, Obleitner F (2021) Spatial and temporal variations in near-surface energy fluxes in an Alpine valley under synoptically undisturbed and clear-sky conditions. Q J R M Soc 147:2173–2196, DOI 10.1002/qj.4016</p> <p>Moore CJ (1986) Frequency response corrections for eddy correlation systems. Boundary-Layer Meteorol 37:17–35, DOI 10.1007/BF00122754</p> <p>Rotach MW, Stiperski I, Fuhrer O, Goger B, Gohm A, Obleitner F, Rau G, Sfyri E, Vergeiner J (2017) Investigating exchange processes over complex topography—the Innsbruck Box (i-Box). Bull Amer Meteorol Soc 98:787–805, DOI 10.1175/BAMS-D-15-00246.1</p> <p>Schotanus P, Nieuwstadt FTM, de Bruijn HAR (1983) Temperature measurement with a sonic anemometer and its application to heat and moisture fluxes. Boundary-Layer Meteorol 26:81–93, DOI 10.1007/BF00164332</p> <p>Stiperski, I. and Rotach, M.W. (2016) On the measurement of turbulence over complex mountainous terrain. Boundary-Layer Meteorology, 159, 97–121. DOI 10.1007/s10546-015-0103-z.</p> <p>Van Dijk A, Kohsiek W, de Bruin HAR (2003) Oxygen sensitivity of Krypton and Lyman-α hygrometers. J Atmos Ocean Technol 20:143–151, DOI 10.1175/1520-0426(2003)020¡0143:OSOKAL¿2.0.CO;2</p> <p>Webb EK, Pearman GI, R L (1980) Correction of flux measurements for density effects due to heat and water vapour transfer. Q J R M Soc 106:85–100, DOI 10.1002/qj.49710644707</p> <p>Wyngaard, J.C. (1973). On surface layer turbulence. In D.A. Haugen (Ed.), Workshop on Micrometeorology, American Meteorological Society, pp. 101–150.</p>
i-Box (Innsbruck Box) – processed eddy-covariance data: 5-min statistics
<p><strong>Abstract</strong></p> <p>The dataset contains eddy-covariance data from five i-Box stations in the Austrian Inn Valley, which have been processed to 5-min statistics. The i-Box is a long-term measurement platform, including a small network of eddy-covariance stations in the lower Inn Valley, to study boundary-layer processes in mountainous terrain. More information about the i-Box can be found at <a href="https://www.uibk.ac.at/acinn/research/atmospheric-dynamics/projects/innsbruck-box-i-box.html.en">https://www.uibk.ac.at/acinn/research/atmospheric-dynamics/projects/innsbruck-box-i-box.html.en</a> and in Rotach et al. (2017).</p> <p> </p> <p><strong>Data description</strong></p> <p><em>Station locations</em></p> <p>The present dataset contains processed data from five i-Box stations located in the Austrian Inn Valley. The Inn Valley is an approximately southwest-northeast oriented valley in the western part of Austria, with a depth of about 2000 m and a width of about 2 km at the valley floor. The locations of the sites are shown in the overview figure i-Box_sites.pdf.</p> <ul> <li> <p>VF0 is located at the almost flat valley floor. The site is surrounded by grassland and agricultural fields. (47.305°N, 11.622°E, 545 m MSL)</p> </li> <li> <p>SF8 is located at the foot of the north sidewall next to a steep embankment between an agricultural field and a concrete parking lot. (47.326°N, 11.652°E, 575 m MSL)</p> </li> <li> <p>SF1 is located on an almost flat plateau running along the northern valley sidewall. The site is mainly surrounded by grassland and agricultural fields. (47.317°N, 11.616°E, 829 m MSL)</p> </li> <li> <p>NF10 is located on an approximately 10 deg slope on the south sidewall, covered by grassland. (47.300°N, 11.673°E, 930 m MSL)</p> </li> <li> <p>NF27 is located on a steep, grass-covered slope on the south sidewall, with a slope angle of about 25 deg. (47.288°N, 11.631°E, 1009 m MSL)</p> </li> </ul> <p>Further information about station locations can be found in Rotach et al. (2017) and Lehner et al. (2021).</p> <p><em>Temporal coverage</em></p> <p>The dataset contains processed data between 2014 and 2020. Some instruments were replaced and new instruments were added during this period. Data gaps occur as a result of instrument malfunctions and maintenance.</p> <p><em>Instrumentation</em></p> <p>Each station is equipped with at least one sonic anemometer and a gas analyzer. The instrumentation usually consists of a CSAT3 sonic anemometer (Campbell Scientific, USA) and KH20 Krypton hygrometer (Campbell Scientific) or an EC150 open-path infrared gas analyzer (Campbell Scientific). In 2020, several of the instruments were replaced with an Irgason (Campbell Scientific), which combines an open-path infrared gas analyzer with a sonic anemometer. Pressure, air temperature, and humidity used for calculating flux corrections are measured with Setra 278 sensors (Setra Systems, USA) and Rotronic HC2A-S temperature and humidity probes (Rotronic, Switzerland).</p> <ul> <li> <p>VF0: CSAT3 and EC150 at 4.0 m, CSAT3 at 8.7 m, CSAT3 and KH20 (until July 2020) or Irgason (since July 2020) at 16.9 m</p> </li> <li> <p>SF8: CSAT3 at 6.1, CSAT3 and KH20 (until September 2020) or Irgason (since September 2020) at 11.2 m</p> </li> <li> <p>SF1: CSAT3 and KH20 (until June 2020) or Irgason (since June 2020) at 6.8 m</p> </li> <li> <p>NF10: CSAT3 and KH20 (until June 2020) or Irgason (since June 2020) at 5.7 m</p> </li> <li> <p>NF27: CSAT3 at 1.5 (since September 2017), CSAT3 and KH20 (until November 2016) or Irgason (since September 2017) 6.8 m</p> </li> </ul> <p>Further information about the instrumentation can be found in Rotach et al. (2017), Lehner et al. (2021), and in the ACINN database:</p> <ul> <li> <p>VF0: <a href="https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html">https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html</a></p> </li> <li> <p>SF8: <a href="https://acinn-data.uibk.ac.at/pages/i-box-terfens.html">https://acinn-data.uibk.ac.at/pages/i-box-terfens.html</a></p> </li> <li> <p>SF1: <a href="https://acinn-data.uibk.ac.at/pages/i-box-eggen.html">https://acinn-data.uibk.ac.at/pages/i-box-eggen.html</a></p> </li> <li> <p>NF10: <a href="https://acinn-data.uibk.ac.at/pages/i-box-weerberg.html">https://acinn-data.uibk.ac.at/pages/i-box-weerberg.html</a></p> </li> <li> <p>NF27: <a href="https://acinn-data.uibk.ac.at/pages/i-box-hochhaeuser.html">https://acinn-data.uibk.ac.at/pages/i-box-hochhaeuser.html</a></p> </li> </ul> <p><em>Data processing</em></p> <p>Raw 20-Hz data were quality controlled and rotated into a streamline coordinate system using double rotation before block averaging the data to 5-min statistics, without previous filtering. Flux corrections were applied to the turbulence statistics, including a frequency response correction (Aubinet et al. 2012) with spectral models following Moore (1986), Højstrup (1981), and Kaimal et al. (1972); a sonic heat-flux correction of the vertical heat flux and the temperature variance (Schotanus et al. 1983); a WPL correction of the vertical moisture flux (Webb et al. 1980); and an Oxygen correction of the vertical moisture flux for data from Krypton hygrometers (van Dijk et al. 2003).</p> <p>The quality control procedures include the removal of data during periods of instrument malfunction as indicated by the instruments’ quality flags, a despiking, the removal of data points exceeding 30 m s<sup>-1</sup> for the horizontal wind components, 10 m s<sup>-1</sup> for the vertical wind velocity, and 50 g m<sup>3</sup> for water vapor density, and the removal of sonic temperature data outside the range -20 – 40°C. The removed data are replaced with random values drawn from a Gaussian distribution, with its mean and standard deviation calculated over a 30-s data window.</p> <p>Quality flags are based on the criteria described in Stiperski and Rotach (2016):</p> <ul> <li> <p>-1: More than 10% of the raw data within the averaging period are replaced during the quality control.</p> </li> <li> <p>0: More than 90% of the raw data fulfill the quality control criteria.</p> </li> <li> <p>1: In addition to fulfilling the quality control criteria, the skewness is within the range -2–2 and the kurtosis is less than 8.</p> </li> <li> <p>2: In addition to the above criteria, the stationarity test by Foken and Wichura (1996) is below 30% and the uncertainty is less than 50% based on Stiperski and Rotach (2016) and Wyngaard (1973)</p> </li> </ul> <p><em>Data files</em></p> <ul> <li> <p>i-Box_sites.pdf contains a map of the i-Box stations.</p> </li> <li> <p>list_variables.pdf contains a list of variable names with a short description.</p> </li> <li> <p>SITENAME_5min.zip contains the processed turbulence statistics, split into yearly files. There is more than one file per year if the instrumentation changed during the year or because of memory restrictions during the processing.</p> </li> </ul> <p><em>Acknowledgments</em></p> <p>Data processing was performed in the framework of the TExSMBL (Turbulent Exchange in the Stable Mountain Boundary Layer) project funded by the Austrian Science Fund (FWF) under grant V 791-N. Data were processed on the LEO HPC infrastructure of the University of Innsbruck.</p> <p><em>References</em></p> <p>Aubinet M, Vesala T, D P (eds) (2012) Eddy Covariance. A practical guide to measurements and data analysis. Springer, Dordrecht, DOI 10.1007/978-94-007-2351-1</p> <p>Højstrup J (1981) A simple model for the adjustment of velocity spectra in unstable conditions downstream of an abrupt change in roughness and heat flux. Boundary-Layer Meteorol 21:341–356, DOI 10.1007/bf00119278</p> <p>Kaimal JC, Wyngaard JC, Izumi Y, Coté OR (1972) Spectral characteristics of surface-layer turbulence. Q J R M Soc 98:563–589, DOI 10.1002/qj.49709841707</p> <p>Lehner M, Rotach MW, Sfyri E, Obleitner F (2021) Spatial and temporal variations in near-surface energy fluxes in an Alpine valley under synoptically undisturbed and clear-sky conditions. Q J R M Soc 147:2173–2196, DOI 10.1002/qj.4016</p> <p>Moore CJ (1986) Frequency response corrections for eddy correlation systems. Boundary-Layer Meteorol 37:17–35, DOI 10.1007/BF00122754</p> <p>Rotach MW, Stiperski I, Fuhrer O, Goger B, Gohm A, Obleitner F, Rau G, Sfyri E, Vergeiner J (2017) Investigating exchange processes over complex topography—the Innsbruck Box (i-Box). Bull Amer Meteorol Soc 98:787–805, DOI 10.1175/BAMS-D-15-00246.1</p> <p>Schotanus P, Nieuwstadt FTM, de Bruijn HAR (1983) Temperature measurement with a sonic anemometer and its application to heat and moisture fluxes. Boundary-Layer Meteorol 26:81–93, DOI 10.1007/BF00164332</p> <p>Stiperski, I. and Rotach, M.W. (2016) On the measurement of turbulence over complex mountainous terrain. Boundary-Layer Meteorology, 159, 97–121. DOI 10.1007/s10546-015-0103-z.</p> <p>Van Dijk A, Kohsiek W, de Bruin HAR (2003) Oxygen sensitivity of Krypton and Lyman-α hygrometers. J Atmos Ocean Technol 20:143–151, DOI 10.1175/1520-0426(2003)020¡0143:OSOKAL¿2.0.CO;2</p> <p>Webb EK, Pearman GI, R L (1980) Correction of flux measurements for density effects due to heat and water vapour transfer. Q J R M Soc 106:85–100, DOI 10.1002/qj.49710644707</p> <p>Wyngaard, J.C. (1973). On surface layer turbulence. In D.A. Haugen (Ed.), Workshop on Micrometeorology, American Meteorological Society, pp. 101–150.</p>
i-Box (Innsbruck Box) – processed eddy-covariance data: 30-min statistics
<p><strong>Abstract</strong></p> <p>The dataset contains eddy-covariance data from five i-Box stations in the Austrian Inn Valley, which have been processed to 30-min statistics. The i-Box is a long-term measurement platform, including a small network of eddy-covariance stations in the lower Inn Valley, to study boundary-layer processes in mountainous terrain. More information about the i-Box can be found at <a href="https://www.uibk.ac.at/acinn/research/atmospheric-dynamics/projects/innsbruck-box-i-box.html.en">https://www.uibk.ac.at/acinn/research/atmospheric-dynamics/projects/innsbruck-box-i-box.html.en</a> and in Rotach et al. (2017).</p> <p> </p> <p><strong>Data description</strong></p> <p><em>Station locations</em></p> <p>The present dataset contains processed data from five i-Box stations located in the Austrian Inn Valley. The Inn Valley is an approximately southwest-northeast oriented valley in the western part of Austria, with a depth of about 2000 m and a width of about 2 km at the valley floor. The locations of the sites are shown in the overview figure i-Box_sites.pdf.</p> <ul> <li> <p>VF0 is located at the almost flat valley floor. The site is surrounded by grassland and agricultural fields. (47.305°N, 11.622°E, 545 m MSL)</p> </li> <li> <p>SF8 is located at the foot of the north sidewall next to a steep embankment between an agricultural field and a concrete parking lot. (47.326°N, 11.652°E, 575 m MSL)</p> </li> <li> <p>SF1 is located on an almost flat plateau running along the northern valley sidewall. The site is mainly surrounded by grassland and agricultural fields. (47.317°N, 11.616°E, 829 m MSL)</p> </li> <li> <p>NF10 is located on an approximately 10 deg slope on the south sidewall, covered by grassland. (47.300°N, 11.673°E, 930 m MSL)</p> </li> <li> <p>NF27 is located on a steep, grass-covered slope on the south sidewall, with a slope angle of about 25 deg. (47.288°N, 11.631°E, 1009 m MSL)</p> </li> </ul> <p>Further information about station locations can be found in Rotach et al. (2017) and Lehner et al. (2021).</p> <p><em>Temporal coverage</em></p> <p>The dataset contains processed data between 2014 and 2020. Some instruments were replaced and new instruments were added during this period. Data gaps occur as a result of instrument malfunctions and maintenance.</p> <p><em>Instrumentation</em></p> <p>Each station is equipped with at least one sonic anemometer and a gas analyzer. The instrumentation usually consists of a CSAT3 sonic anemometer (Campbell Scientific, USA) and KH20 Krypton hygrometer (Campbell Scientific) or an EC150 open-path infrared gas analyzer (Campbell Scientific). In 2020, several of the instruments were replaced with an Irgason (Campbell Scientific), which combines an open-path infrared gas analyzer with a sonic anemometer. Pressure, air temperature, and humidity used for calculating flux corrections are measured with Setra 278 sensors (Setra Systems, USA) and Rotronic HC2A-S temperature and humidity probes (Rotronic, Switzerland).</p> <ul> <li> <p>VF0: CSAT3 and EC150 at 4.0 m, CSAT3 at 8.7 m, CSAT3 and KH20 (until July 2020) or Irgason (since July 2020) at 16.9 m</p> </li> <li> <p>SF8: CSAT3 at 6.1, CSAT3 and KH20 (until September 2020) or Irgason (since September 2020) at 11.2 m</p> </li> <li> <p>SF1: CSAT3 and KH20 (until June 2020) or Irgason (since June 2020) at 6.8 m</p> </li> <li> <p>NF10: CSAT3 and KH20 (until June 2020) or Irgason (since June 2020) at 5.7 m</p> </li> <li> <p>NF27: CSAT3 at 1.5 (since September 2017), CSAT3 and KH20 (until November 2016) or Irgason (since September 2017) 6.8 m</p> </li> </ul> <p>Further information about the instrumentation can be found in Rotach et al. (2017), Lehner et al. (2021), and in the ACINN database:</p> <ul> <li> <p>VF0: <a href="https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html">https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html</a></p> </li> <li> <p>SF8: <a href="https://acinn-data.uibk.ac.at/pages/i-box-terfens.html">https://acinn-data.uibk.ac.at/pages/i-box-terfens.html</a></p> </li> <li> <p>SF1: <a href="https://acinn-data.uibk.ac.at/pages/i-box-eggen.html">https://acinn-data.uibk.ac.at/pages/i-box-eggen.html</a></p> </li> <li> <p>NF10: <a href="https://acinn-data.uibk.ac.at/pages/i-box-weerberg.html">https://acinn-data.uibk.ac.at/pages/i-box-weerberg.html</a></p> </li> <li> <p>NF27: <a href="https://acinn-data.uibk.ac.at/pages/i-box-hochhaeuser.html">https://acinn-data.uibk.ac.at/pages/i-box-hochhaeuser.html</a></p> </li> </ul> <p><em>Data processing</em></p> <p>Raw 20-Hz data were quality controlled and rotated into a streamline coordinate system using double rotation before block averaging the data to 30-min statistics, without previous filtering. Flux corrections were applied to the turbulence statistics, including a frequency response correction (Aubinet et al. 2012) with spectral models following Moore (1986), Højstrup (1981), and Kaimal et al. (1972); a sonic heat-flux correction of the vertical heat flux and the temperature variance (Schotanus et al. 1983); a WPL correction of the vertical moisture flux (Webb et al. 1980); and an Oxygen correction of the vertical moisture flux for data from Krypton hygrometers (van Dijk et al. 2003).</p> <p>The quality control procedures include the removal of data during periods of instrument malfunction as indicated by the instruments’ quality flags, a despiking, the removal of data points exceeding 30 m s<sup>-1</sup> for the horizontal wind components, 10 m s<sup>-1</sup> for the vertical wind velocity, and 50 g m<sup>3</sup> for water vapor density, and the removal of sonic temperature data outside the range -20 – 40°C. The removed data are replaced with random values drawn from a Gaussian distribution, with its mean and standard deviation calculated over a 30-s data window.</p> <p>Quality flags are based on the criteria described in Stiperski and Rotach (2016):</p> <ul> <li> <p>-1: More than 10% of the raw data within the averaging period are replaced during the quality control.</p> </li> <li> <p>0: More than 90% of the raw data fulfill the quality control criteria.</p> </li> <li> <p>1: In addition to fulfilling the quality control criteria, the skewness is within the range -2–2 and the kurtosis is less than 8.</p> </li> <li> <p>2: In addition to the above criteria, the stationarity test by Foken and Wichura (1996) is below 30% and the uncertainty is less than 50% based on Stiperski and Rotach (2016) and Wyngaard (1973)</p> </li> </ul> <p><em>Data files</em></p> <ul> <li> <p>i-Box_sites.pdf contains a map of the i-Box stations.</p> </li> <li> <p>list_variables.pdf contains a list of variable names with a short description.</p> </li> <li> <p>SITENAME_30min.zip contains the processed turbulence statistics, split into yearly files. There is more than one file per year if the instrumentation changed during the year or because of memory restrictions during the processin</p> </li> </ul> <p><em>Acknowledgments</em></p> <p>Data processing was performed in the framework of the TExSMBL (Turbulent Exchange in the Stable Mountain Boundary Layer) project funded by the Austrian Science Fund (FWF) under grant V 791-N. Data were processed on the LEO HPC infrastructure of the University of Innsbruck.</p> <p><em>References</em></p> <p>Aubinet M, Vesala T, D P (eds) (2012) Eddy Covariance. A practical guide to measurements and data analysis. Springer, Dordrecht, DOI 10.1007/978-94-007-2351-1</p> <p>Højstrup J (1981) A simple model for the adjustment of velocity spectra in unstable conditions downstream of an abrupt change in roughness and heat flux. Boundary-Layer Meteorol 21:341–356, DOI 10.1007/bf00119278</p> <p>Kaimal JC, Wyngaard JC, Izumi Y, Coté OR (1972) Spectral characteristics of surface-layer turbulence. Q J R M Soc 98:563–589, DOI 10.1002/qj.49709841707</p> <p>Lehner M, Rotach MW, Sfyri E, Obleitner F (2021) Spatial and temporal variations in near-surface energy fluxes in an Alpine valley under synoptically undisturbed and clear-sky conditions. Q J R M Soc 147:2173–2196, DOI 10.1002/qj.4016</p> <p>Moore CJ (1986) Frequency response corrections for eddy correlation systems. Boundary-Layer Meteorol 37:17–35, DOI 10.1007/BF00122754</p> <p>Rotach MW, Stiperski I, Fuhrer O, Goger B, Gohm A, Obleitner F, Rau G, Sfyri E, Vergeiner J (2017) Investigating exchange processes over complex topography—the Innsbruck Box (i-Box). Bull Amer Meteorol Soc 98:787–805, DOI 10.1175/BAMS-D-15-00246.1</p> <p>Schotanus P, Nieuwstadt FTM, de Bruijn HAR (1983) Temperature measurement with a sonic anemometer and its application to heat and moisture fluxes. Boundary-Layer Meteorol 26:81–93, DOI 10.1007/BF00164332</p> <p>Stiperski, I. and Rotach, M.W. (2016) On the measurement of turbulence over complex mountainous terrain. Boundary-Layer Meteorology, 159, 97–121. DOI 10.1007/s10546-015-0103-z.</p> <p>Van Dijk A, Kohsiek W, de Bruin HAR (2003) Oxygen sensitivity of Krypton and Lyman-α hygrometers. J Atmos Ocean Technol 20:143–151, DOI 10.1175/1520-0426(2003)020¡0143:OSOKAL¿2.0.CO;2</p> <p>Webb EK, Pearman GI, R L (1980) Correction of flux measurements for density effects due to heat and water vapour transfer. Q J R M Soc 106:85–100, DOI 10.1002/qj.49710644707</p> <p>Wyngaard, J.C. (1973). On surface layer turbulence. In D.A. Haugen (Ed.), Workshop on Micrometeorology, American Meteorological Society, pp. 101–150.</p> <p> </p>
i-Box (Innsbruck Box) – processed eddy-covariance data: 15-min statistics
<p><strong>Abstract</strong></p> <p>The dataset contains eddy-covariance data from five i-Box stations in the Austrian Inn Valley, which have been processed to 15-min statistics. The i-Box is a long-term measurement platform, including a small network of eddy-covariance stations in the lower Inn Valley, to study boundary-layer processes in mountainous terrain. More information about the i-Box can be found at <a href="https://www.uibk.ac.at/acinn/research/atmospheric-dynamics/projects/innsbruck-box-i-box.html.en">https://www.uibk.ac.at/acinn/research/atmospheric-dynamics/projects/innsbruck-box-i-box.html.en</a> and in Rotach et al. (2017).</p> <p> </p> <p><strong>Data description</strong></p> <p><em>Station locations</em></p> <p>The present dataset contains processed data from five i-Box stations located in the Austrian Inn Valley. The Inn Valley is an approximately southwest-northeast oriented valley in the western part of Austria, with a depth of about 2000 m and a width of about 2 km at the valley floor. The locations of the sites are shown in the overview figure i-Box_sites.pdf.</p> <ul> <li> <p>VF0 is located at the almost flat valley floor. The site is surrounded by grassland and agricultural fields. (47.305°N, 11.622°E, 545 m MSL)</p> </li> <li> <p>SF8 is located at the foot of the north sidewall next to a steep embankment between an agricultural field and a concrete parking lot. (47.326°N, 11.652°E, 575 m MSL)</p> </li> <li> <p>SF1 is located on an almost flat plateau running along the northern valley sidewall. The site is mainly surrounded by grassland and agricultural fields. (47.317°N, 11.616°E, 829 m MSL)</p> </li> <li> <p>NF10 is located on an approximately 10 deg slope on the south sidewall, covered by grassland. (47.300°N, 11.673°E, 930 m MSL)</p> </li> <li> <p>NF27 is located on a steep, grass-covered slope on the south sidewall, with a slope angle of about 25 deg. (47.288°N, 11.631°E, 1009 m MSL)</p> </li> </ul> <p>Further information about station locations can be found in Rotach et al. (2017) and Lehner et al. (2021).</p> <p><em>Temporal coverage</em></p> <p>The dataset contains processed data between 2014 and 2020. Some instruments were replaced and new instruments were added during this period. Data gaps occur as a result of instrument malfunctions and maintenance.</p> <p><em>Instrumentation</em></p> <p>Each station is equipped with at least one sonic anemometer and a gas analyzer. The instrumentation usually consists of a CSAT3 sonic anemometer (Campbell Scientific, USA) and KH20 Krypton hygrometer (Campbell Scientific) or an EC150 open-path infrared gas analyzer (Campbell Scientific). In 2020, several of the instruments were replaced with an Irgason (Campbell Scientific), which combines an open-path infrared gas analyzer with a sonic anemometer. Pressure, air temperature, and humidity used for calculating flux corrections are measured with Setra 278 sensors (Setra Systems, USA) and Rotronic HC2A-S temperature and humidity probes (Rotronic, Switzerland).</p> <ul> <li> <p>VF0: CSAT3 and EC150 at 4.0 m, CSAT3 at 8.7 m, CSAT3 and KH20 (until July 2020) or Irgason (since July 2020) at 16.9 m</p> </li> <li> <p>SF8: CSAT3 at 6.1, CSAT3 and KH20 (until September 2020) or Irgason (since September 2020) at 11.2 m</p> </li> <li> <p>SF1: CSAT3 and KH20 (until June 2020) or Irgason (since June 2020) at 6.8 m</p> </li> <li> <p>NF10: CSAT3 and KH20 (until June 2020) or Irgason (since June 2020) at 5.7 m</p> </li> <li> <p>NF27: CSAT3 at 1.5 (since September 2017), CSAT3 and KH20 (until November 2016) or Irgason (since September 2017) 6.8 m</p> </li> </ul> <p>Further information about the instrumentation can be found in Rotach et al. (2017), Lehner et al. (2021), and in the ACINN database:</p> <ul> <li> <p>VF0: <a href="https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html">https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html</a></p> </li> <li> <p>SF8: <a href="https://acinn-data.uibk.ac.at/pages/i-box-terfens.html">https://acinn-data.uibk.ac.at/pages/i-box-terfens.html</a></p> </li> <li> <p>SF1: <a href="https://acinn-data.uibk.ac.at/pages/i-box-eggen.html">https://acinn-data.uibk.ac.at/pages/i-box-eggen.html</a></p> </li> <li> <p>NF10: <a href="https://acinn-data.uibk.ac.at/pages/i-box-weerberg.html">https://acinn-data.uibk.ac.at/pages/i-box-weerberg.html</a></p> </li> <li> <p>NF27: <a href="https://acinn-data.uibk.ac.at/pages/i-box-hochhaeuser.html">https://acinn-data.uibk.ac.at/pages/i-box-hochhaeuser.html</a></p> </li> </ul> <p><em>Data processing</em></p> <p>Raw 20-Hz data were quality controlled and rotated into a streamline coordinate system using double rotation before block averaging the data to 15-min statistics, without previous filtering. Flux corrections were applied to the turbulence statistics, including a frequency response correction (Aubinet et al. 2012) with spectral models following Moore (1986), Højstrup (1981), and Kaimal et al. (1972); a sonic heat-flux correction of the vertical heat flux and the temperature variance (Schotanus et al. 1983); a WPL correction of the vertical moisture flux (Webb et al. 1980); and an Oxygen correction of the vertical moisture flux for data from Krypton hygrometers (van Dijk et al. 2003).</p> <p>The quality control procedures include the removal of data during periods of instrument malfunction as indicated by the instruments’ quality flags, a despiking, the removal of data points exceeding 30 m s<sup>-1</sup> for the horizontal wind components, 10 m s<sup>-1</sup> for the vertical wind velocity, and 50 g m<sup>3</sup> for water vapor density, and the removal of sonic temperature data outside the range -20 – 40°C. The removed data are replaced with random values drawn from a Gaussian distribution, with its mean and standard deviation calculated over a 30-s data window.</p> <p>Quality flags are based on the criteria described in Stiperski and Rotach (2016):</p> <ul> <li> <p>-1: More than 10% of the raw data within the averaging period are replaced during the quality control.</p> </li> <li> <p>0: More than 90% of the raw data fulfill the quality control criteria.</p> </li> <li> <p>1: In addition to fulfilling the quality control criteria, the skewness is within the range -2–2 and the kurtosis is less than 8.</p> </li> <li> <p>2: In addition to the above criteria, the stationarity test by Foken and Wichura (1996) is below 30% and the uncertainty is less than 50% based on Stiperski and Rotach (2016) and Wyngaard (1973)</p> </li> </ul> <p><em>Data files</em></p> <ul> <li> <p>i-Box_sites.pdf contains a map of the i-Box stations.</p> </li> <li> <p>list_variables.pdf contains a list of variable names with a short description.</p> </li> <li> <p>SITENAME_15min.zip contains the processed turbulence statistics, split into yearly files. There is more than one file per year if the instrumentation changed during the year or because of memory restrictions during the processing.</p> </li> </ul> <p><em>Acknowledgments</em></p> <p>Data processing was performed in the framework of the TExSMBL (Turbulent Exchange in the Stable Mountain Boundary Layer) project funded by the Austrian Science Fund (FWF) under grant V 791-N. Data were processed on the LEO HPC infrastructure of the University of Innsbruck.</p> <p><em>References</em></p> <p>Aubinet M, Vesala T, D P (eds) (2012) Eddy Covariance. A practical guide to measurements and data analysis. Springer, Dordrecht, DOI 10.1007/978-94-007-2351-1</p> <p>Højstrup J (1981) A simple model for the adjustment of velocity spectra in unstable conditions downstream of an abrupt change in roughness and heat flux. Boundary-Layer Meteorol 21:341–356, DOI 10.1007/bf00119278</p> <p>Kaimal JC, Wyngaard JC, Izumi Y, Coté OR (1972) Spectral characteristics of surface-layer turbulence. Q J R M Soc 98:563–589, DOI 10.1002/qj.49709841707</p> <p>Lehner M, Rotach MW, Sfyri E, Obleitner F (2021) Spatial and temporal variations in near-surface energy fluxes in an Alpine valley under synoptically undisturbed and clear-sky conditions. Q J R M Soc 147:2173–2196, DOI 10.1002/qj.4016</p> <p>Moore CJ (1986) Frequency response corrections for eddy correlation systems. Boundary-Layer Meteorol 37:17–35, DOI 10.1007/BF00122754</p> <p>Rotach MW, Stiperski I, Fuhrer O, Goger B, Gohm A, Obleitner F, Rau G, Sfyri E, Vergeiner J (2017) Investigating exchange processes over complex topography—the Innsbruck Box (i-Box). Bull Amer Meteorol Soc 98:787–805, DOI 10.1175/BAMS-D-15-00246.1</p> <p>Schotanus P, Nieuwstadt FTM, de Bruijn HAR (1983) Temperature measurement with a sonic anemometer and its application to heat and moisture fluxes. Boundary-Layer Meteorol 26:81–93, DOI 10.1007/BF00164332</p> <p>Stiperski, I. and Rotach, M.W. (2016) On the measurement of turbulence over complex mountainous terrain. Boundary-Layer Meteorology, 159, 97–121. DOI 10.1007/s10546-015-0103-z.</p> <p>Van Dijk A, Kohsiek W, de Bruin HAR (2003) Oxygen sensitivity of Krypton and Lyman-α hygrometers. J Atmos Ocean Technol 20:143–151, DOI 10.1175/1520-0426(2003)020¡0143:OSOKAL¿2.0.CO;2</p> <p>Webb EK, Pearman GI, R L (1980) Correction of flux measurements for density effects due to heat and water vapour transfer. Q J R M Soc 106:85–100, DOI 10.1002/qj.49710644707</p> <p>Wyngaard, J.C. (1973). On surface layer turbulence. In D.A. Haugen (Ed.), Workshop on Micrometeorology, American Meteorological Society, pp. 101–150.</p>
i-Box (Innsbruck Box) – processed eddy-covariance data: 30-s statistics
<p><strong>Abstract</strong></p> <p>The dataset contains eddy-covariance data from five i-Box stations in the Austrian Inn Valley, which have been processed to 30-s statistics. The i-Box is a long-term measurement platform, including a small network of eddy-covariance stations in the lower Inn Valley, to study boundary-layer processes in mountainous terrain. More information about the i-Box can be found at <a href="https://www.uibk.ac.at/acinn/research/atmospheric-dynamics/projects/innsbruck-box-i-box.html.en">https://www.uibk.ac.at/acinn/research/atmospheric-dynamics/projects/innsbruck-box-i-box.html.en</a> and in Rotach et al. (2017).</p> <p> </p> <p><strong>Data description</strong></p> <p><em>Station locations</em></p> <p>The present dataset contains processed data from five i-Box stations located in the Austrian Inn Valley. The Inn Valley is an approximately southwest-northeast oriented valley in the western part of Austria, with a depth of about 2000 m and a width of about 2 km at the valley floor. The locations of the sites are shown in the overview figure i-Box_sites.pdf.</p> <ul> <li> <p>VF0 is located at the almost flat valley floor. The site is surrounded by grassland and agricultural fields. (47.305°N, 11.622°E, 545 m MSL)</p> </li> <li> <p>SF8 is located at the foot of the north sidewall next to a steep embankment between an agricultural field and a concrete parking lot. (47.326°N, 11.652°E, 575 m MSL)</p> </li> <li> <p>SF1 is located on an almost flat plateau running along the northern valley sidewall. The site is mainly surrounded by grassland and agricultural fields. (47.317°N, 11.616°E, 829 m MSL)</p> </li> <li> <p>NF10 is located on an approximately 10 deg slope on the south sidewall, covered by grassland. (47.300°N, 11.673°E, 930 m MSL)</p> </li> <li> <p>NF27 is located on a steep, grass-covered slope on the south sidewall, with a slope angle of about 25 deg. (47.288°N, 11.631°E, 1009 m MSL)</p> </li> </ul> <p>Further information about station locations can be found in Rotach et al. (2017) and Lehner et al. (2021).</p> <p><em>Temporal coverage</em></p> <p>The dataset contains processed data between 2014 and 2020. Some instruments were replaced and new instruments were added during this period. Data gaps occur as a result of instrument malfunctions and maintenance.</p> <p><em>Instrumentation</em></p> <p>Each station is equipped with at least one sonic anemometer and a gas analyzer. The instrumentation usually consists of a CSAT3 sonic anemometer (Campbell Scientific, USA) and KH20 Krypton hygrometer (Campbell Scientific) or an EC150 open-path infrared gas analyzer (Campbell Scientific). In 2020, several of the instruments were replaced with an Irgason (Campbell Scientific), which combines an open-path infrared gas analyzer with a sonic anemometer. Pressure, air temperature, and humidity used for calculating flux corrections are measured with Setra 278 sensors (Setra Systems, USA) and Rotronic HC2A-S temperature and humidity probes (Rotronic, Switzerland).</p> <ul> <li> <p>VF0: CSAT3 and EC150 at 4.0 m, CSAT3 at 8.7 m, CSAT3 and KH20 (until July 2020) or Irgason (since July 2020) at 16.9 m</p> </li> <li> <p>SF8: CSAT3 at 6.1, CSAT3 and KH20 (until September 2020) or Irgason (since September 2020) at 11.2 m</p> </li> <li> <p>SF1: CSAT3 and KH20 (until June 2020) or Irgason (since June 2020) at 6.8 m</p> </li> <li> <p>NF10: CSAT3 and KH20 (until June 2020) or Irgason (since June 2020) at 5.7 m</p> </li> <li> <p>NF27: CSAT3 at 1.5 (since September 2017), CSAT3 and KH20 (until November 2016) or Irgason (since September 2017) 6.8 m</p> </li> </ul> <p>Further information about the instrumentation can be found in Rotach et al. (2017), Lehner et al. (2021), and in the ACINN database:</p> <ul> <li> <p>VF0: <a href="https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html">https://acinn-data.uibk.ac.at/pages/i-box-kolsass.html</a></p> </li> <li> <p>SF8: <a href="https://acinn-data.uibk.ac.at/pages/i-box-terfens.html">https://acinn-data.uibk.ac.at/pages/i-box-terfens.html</a></p> </li> <li> <p>SF1: <a href="https://acinn-data.uibk.ac.at/pages/i-box-eggen.html">https://acinn-data.uibk.ac.at/pages/i-box-eggen.html</a></p> </li> <li> <p>NF10: <a href="https://acinn-data.uibk.ac.at/pages/i-box-weerberg.html">https://acinn-data.uibk.ac.at/pages/i-box-weerberg.html</a></p> </li> <li> <p>NF27: <a href="https://acinn-data.uibk.ac.at/pages/i-box-hochhaeuser.html">https://acinn-data.uibk.ac.at/pages/i-box-hochhaeuser.html</a></p> </li> </ul> <p><em>Data processing</em></p> <p>Raw 20-Hz data were quality controlled and rotated into a streamline coordinate system using double rotation before block averaging the data to 30-s statistics, without previous filtering. Flux corrections were applied to the turbulence statistics, including a frequency response correction (Aubinet et al. 2012) with spectral models following Moore (1986), Højstrup (1981), and Kaimal et al. (1972); a sonic heat-flux correction of the vertical heat flux and the temperature variance (Schotanus et al. 1983); a WPL correction of the vertical moisture flux (Webb et al. 1980); and an Oxygen correction of the vertical moisture flux for data from Krypton hygrometers (van Dijk et al. 2003).</p> <p>The quality control procedures include the removal of data during periods of instrument malfunction as indicated by the instruments’ quality flags, a despiking, the removal of data points exceeding 30 m s<sup>-1</sup> for the horizontal wind components, 10 m s<sup>-1</sup> for the vertical wind velocity, and 50 g m<sup>3</sup> for water vapor density, and the removal of sonic temperature data outside the range -20 – 40°C. The removed data are replaced with random values drawn from a Gaussian distribution, with its mean and standard deviation calculated over a 30-s data window.</p> <p>Quality flags are based on the criteria described in Stiperski and Rotach (2016):</p> <ul> <li> <p>-1: More than 10% of the raw data within the averaging period are replaced during the quality control.</p> </li> <li> <p>0: More than 90% of the raw data fulfill the quality control criteria.</p> </li> <li> <p>1: In addition to fulfilling the quality control criteria, the skewness is within the range -2–2 and the kurtosis is less than 8.</p> </li> <li> <p>2: In addition to the above criteria, the stationarity test by Foken and Wichura (1996) is below 30% and the uncertainty is less than 50% based on Stiperski and Rotach (2016) and Wyngaard (1973)</p> </li> </ul> <p><em>Data files</em></p> <ul> <li> <p>i-Box_sites.pdf contains a map of the i-Box stations.</p> </li> <li> <p>list_variables.pdf contains a list of variable names with a short description.</p> </li> <li> <p>SITENAME_30s.zip contains the processed turbulence statistics, split into yearly files. There is more than one file per year if the instrumentation changed during the year or because of memory restrictions during the processing.</p> </li> </ul> <p><em>Acknowledgments</em></p> <p>Data processing was performed in the framework of the TExSMBL (Turbulent Exchange in the Stable Mountain Boundary Layer) project funded by the Austrian Science Fund (FWF) under grant V 791-N. Data were processed on the LEO HPC infrastructure of the University of Innsbruck.</p> <p><em>References</em></p> <p>Aubinet M, Vesala T, D P (eds) (2012) Eddy Covariance. A practical guide to measurements and data analysis. Springer, Dordrecht, DOI 10.1007/978-94-007-2351-1</p> <p>Højstrup J (1981) A simple model for the adjustment of velocity spectra in unstable conditions downstream of an abrupt change in roughness and heat flux. Boundary-Layer Meteorol 21:341–356, DOI 10.1007/bf00119278</p> <p>Kaimal JC, Wyngaard JC, Izumi Y, Coté OR (1972) Spectral characteristics of surface-layer turbulence. Q J R M Soc 98:563–589, DOI 10.1002/qj.49709841707</p> <p>Lehner M, Rotach MW, Sfyri E, Obleitner F (2021) Spatial and temporal variations in near-surface energy fluxes in an Alpine valley under synoptically undisturbed and clear-sky conditions. Q J R M Soc 147:2173–2196, DOI 10.1002/qj.4016</p> <p>Moore CJ (1986) Frequency response corrections for eddy correlation systems. Boundary-Layer Meteorol 37:17–35, DOI 10.1007/BF00122754</p> <p>Rotach MW, Stiperski I, Fuhrer O, Goger B, Gohm A, Obleitner F, Rau G, Sfyri E, Vergeiner J (2017) Investigating exchange processes over complex topography—the Innsbruck Box (i-Box). Bull Amer Meteorol Soc 98:787–805, DOI 10.1175/BAMS-D-15-00246.1</p> <p>Schotanus P, Nieuwstadt FTM, de Bruijn HAR (1983) Temperature measurement with a sonic anemometer and its application to heat and moisture fluxes. Boundary-Layer Meteorol 26:81–93, DOI 10.1007/BF00164332</p> <p>Stiperski, I. and Rotach, M.W. (2016) On the measurement of turbulence over complex mountainous terrain. Boundary-Layer Meteorology, 159, 97–121. DOI 10.1007/s10546-015-0103-z.</p> <p>Van Dijk A, Kohsiek W, de Bruin HAR (2003) Oxygen sensitivity of Krypton and Lyman-α hygrometers. J Atmos Ocean Technol 20:143–151, DOI 10.1175/1520-0426(2003)020¡0143:OSOKAL¿2.0.CO;2</p> <p>Webb EK, Pearman GI, R L (1980) Correction of flux measurements for density effects due to heat and water vapour transfer. Q J R M Soc 106:85–100, DOI 10.1002/qj.49710644707</p> <p>Wyngaard, J.C. (1973). On surface layer turbulence. In D.A. Haugen (Ed.), Workshop on Micrometeorology, American Meteorological Society, pp. 101–150.</p>
Data for: Chronic exposure to odors at naturally occurring concentrations triggers limited plasticity in early stages of Drosophila olfactory processing
<p>In insects and mammals, olfactory experience in early life alters olfactory behavior and function in later life. In the vinegar fly <em>Drosophila</em>, flies chronically exposed to a high concentration of a monomolecular odor exhibit reduced behavioral aversion to the familiar odor when it is re-encountered. This change in olfactory behavior has been attributed to selective decreases in the sensitivity of second-order olfactory projection neurons (PNs) in the antennal lobe that respond to the overrepresented odor. However, since odorant compounds do not occur at similarly high concentrations in natural sources, the role of odor experience-dependent plasticity in natural environments is unclear. Here, we investigated olfactory plasticity in the antennal lobe of flies chronically exposed to odors at concentrations that are typically encountered in natural odor sources. These stimuli were chosen to each strongly and selectively excite a single class of primary olfactory receptor neuron (ORN), thus facilitating a rigorous assessment of the selectivity of olfactory plasticity for PNs directly excited by overrepresented stimuli. Unexpectedly, we found that chronic exposure to three such odors did not result in decreased PN sensitivity but rather mildly increased responses to weak stimuli in most PN types. Odor-evoked PN activity in response to stronger stimuli was mostly unaffected by odor experience. When present, plasticity was observed broadly in multiple PN types and thus was not selective for PNs receiving direct input from the chronically active ORNs. We further investigated the DL5 olfactory coding channel and found that chronic odor-mediated excitation of its input ORNs did not affect PN intrinsic properties, local inhibitory innervation, ORN responses, or ORN-PN synaptic strength; however, broad-acting lateral excitation evoked by some odors was increased. These results show that PN odor coding is only mildly affected by strong persistent activation of a single olfactory input, highlighting the stability of early stages of insect olfactory processing to significant perturbations in the sensory environment.</p>
Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 4)
<p>The dataset contains:</p> <p>i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input</p> <p>ii. the model output and skill produced</p> <p>for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 4)</p>
Raw data and analysis code for "Higher-order Process Matrix Tomography of a passively-stable Quantum SWITCH"
<p>This folder contains the raw data and analysis coded need to reproduce all of the major results in the manuscript "Higher-order Process Matrix Tomography of a passively-stable Quantum SWITCH". </p>
Processed data and scripts supporting the manuscript "Single-cell transcriptomics reveals immune suppression and cell states predictive of patient outcomes in rhabdomyosarcoma"
<p>This submission contains the compiled count table, processed R objects and various scripts and output files accompanying our manuscript "Single-cell transcriptomics reveals immune suppression and cell states predictive of patient outcomes in rhabdomyosarcoma" (Nature Communications, 2023, https://doi.org/10.1038/s41467-023-38886-8)</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.