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Simulated thickness profiles of ALD film in a wide microchannel of 500 nm height published as Fig.4 in PCCP 24 (2022) 8645-8660
<p>A series of simulated thickness profiles of atomic layer deposition (ALD) film grown in a wide lateral high-aspect-ratio (LHAR) microchannel is archived as an Excel file. This dataset has been published as Figure 4 in the publication "Conformality of atomic layer deposition in microchannels: impact of process parameters on the simulated thickness profile" (Yim and Verkama et al., Phys. Chem. Chem. Phys. 24 (2022) 8645-8660. https://doi.org/10.1039/D1CP04758B). A diffusion-reaction model by Ylilammi et al. (Ylilammi et al., J. Appl. Phys. 123 (2018) 205301. https://doi.org/10.1063/1.5028178) was re-implemented for the simulation. For this simulation, a channel height of 500 nm, which is a typical height for microscopic PillarHallTM LHAR test chips (Yim and Ylivaara et al., Phys. Chem. Chem. Phys., 22 (2020) 23107-23120. https://doi.org/10.1039/D0CP03358H), was used.<br> The Excel file consists of 11 tabs in total: metadata, baseline thickness profile, and Fig4a to Fig4i. The baseline thickness profile and data of fig4a Fig4i are also available as a CSV file. The metadata page describes the data with its baseline conditions. The baseline conditions used in the simulation are: sticking coefficient = 0.01, temperature = 250 °C, initial partial pressure of Reactant A = 100 Pa, molar mass of Reactant A = 0.100 kg mol-1, hard-sphere diameter of Reactant A = 6 × 10-10 m, partial pressure of inert gas I = 500 Pa, molar mass of inert gas I = 0.028 kg mol-1, hard-sphere diameter of inert gas I = 3.74 × 10-10 m, mass density of deposited film = 3500 kg m-3, areal number density of metal M atoms in MyZx material = 4 nm-2, number of metal atoms in a Reactant A molecule = 1, number of metal atoms in a formula unit of growing film = 1, number of cycle = 250, desorption probability = 0.01 s-1, channel height = 500 nm, and channel width = 10 mm. The baseline thickness profile tab contains a thickness profile obtained in the baseline conditions as film thickness versus distance within a microchannel. The thickness profile stored from Fig4a to Fig4i tabs was obtained by varying individual parameters with other parameter values in baseline conditions: initial partial pressure of Reactant A (Fig4a), pulse time (Fig4b), molar mass of Reactant A (Fig4c), mass density of deposited film (Fig4d), adsorption density (Fig4e), desorption probability (Fig4f), sticking coefficient (Fig4g), temperature (Fig4h) and partial pressure of inert gas (Fig4i).</p>
IceAq Project - Ground Penetrating Profiles Data
<p>Ground Penetrating Radar profiles on 47 sections. Equipment used: Cobra Plug-In by Radarteam</p> <p>45 .sgy files containing the GPR data, 45 text files containing the corresponding GPS data, 1 spreadsheet giving the correlation table between the .sgy and GPS text files. Total 424 Mb.</p> <p>Origin: MSCA Project Number: 885891 Project Acronym: IceAq</p> <p>Project title: Proglacial and subglacial aquifers: their evolution under climate change and the potential impacts in terms of resources and natural hazards, through the case of eastern Iceland</p> <p>Period of data collection: June 2022, precise periods specified for each file.</p> <p>Area of data collection: Iceland, south-east of Vatnajökull, Suðursveit and Mýrar.</p> <p>Accessibility: The data can be read using e.g. the following open source softwares: LibreOffice (https://www.libreoffice.org), QGIS (<a href="https://qgis.org/">https://qgis.org</a>), and Geopsy (https://www.geopsy.org/).</p>
Increased egg shell temperature during incubation leads to changes in transcriptional and epigenetic profiles in chicken lungs
<p>These RDS files contain <strong>DESeqDataSet </strong>objects subsets per broiler age and treatment. These objects are the result of DESeq2::DESeq( … ,betaPrior=FALSE).The .txt-objects contain the normalized sequencing counts per broiler age and treatment group. These objects are the result of DESeq2::counts( … , normalized=TRUE). Data was generated using STAR v2.7.10a and DESeq2 v1.36. Metadata is included as Excel file.</p> <p>Sequencing data is deposited at NCBI-SRA under BioProject: PRJNA949139. </p> <p> </p> <p><strong>Study abstract</strong></p> <p>D. Schokker, J. de Vos, P.B. Stege, O. Madsen, H.J. Wijnen, S.K. Kar, and J.M.J. Rebel</p> <p>Health and resilience against respiratory diseases are important features for broiler chicken. In this study, epigenetic and transcriptomic changes in the lungs of broiler chickens of different ages during rearing that were either exposed to elevated egg shell temperature (HIGH) of 38.9°C during mid-incubation or normal egg shell temperature (control; CON). The objective was to better understand how environmental challenges, such as heat stress during egg incubation, affect the development of the immune system and health of broiler chicken at later age. To this end we generated both epigenetic and transcriptomic data of lung tissue of elevated HIGH and CON chicken, furthermore these chicken were challenged by introducing either an infectious E. coli or an IBV vaccination to monitor the respiratory response. Thousands of differential methylated sites were observed at days 15 and 33, when comparing HIGH vs. CON. Pathway enrichment analysis of HIGH vs. CON showed that differentially expressed genes were mainly involved in cilium, cytoskeleton, and immune processes. These findings provide insight into the underlying biological mechanisms of early life conditions, like elevated EST, and their potential role in health of broilers.</p>
Pulse Profiles and Times of Arrival Measurements from a Rotating Radio Transient Census with the Irish LOFAR station
<p>The reduced data produced as a part of a census of rotating radio transients (RRATs) with the Irish LOFAR station.</p> <p> </p> <p>This deposit contains:</p> <ul> <li>Metadata regarding observed data</li> <li>A copy of RFI-zapped, single pulse archives</li> <li>A copy of the time-flattened periodic emission archives</li> <li>A copy of the measured pulse times of arrival</li> <li>Ephemerides used and produced as a part of the work</li> </ul> <p>Additional data can be made available on request to the author.</p>
Pinterest dataset for age and gender identification in author profiling
<p>This dataset was used for the experiments presented in the article "Reconstructive Classification for Age and Gender Identification in Social Networks" - IEEE Transactions on Computational Social Systems</p> <p>The dataset contains text data from 548,761 pins corresponding to 264 users of Pinterest.</p> <p>There are 7 files.</p> <p>The first 5 files correspond to the extracted textual features from the pins that are aggregated per user: ats, emojis/emoticons, hashtags, links, and words.</p> <p>There are 264 lines in each file (one per user), as the concatenation of the extracted features from all the pins corresponding to each user.</p> <p>The last 2 files are the labels for the age and gender of the users. There are also 264 lines (one per user).</p> <p>For age, there are 4 possible labels: 18-24, 25-34, 35-46, and 50+</p> <p>For gender, there are 2 possible labels: F and M</p>
Single-Cell Profiling of CD8+ T Cells in Acute Myeloid Leukemia Reveals a Continuous Spectrum of Differentiation and Clonal Hyperexpansion
<p>Data for the publication <strong>Single-Cell Profiling of CD8<sup>+</sup> T Cells in Acute Myeloid Leukemia Reveals a Continuous Spectrum of Differentiation and Clonal Hyperexpansion</strong></p>
LungVis1.0: Active learning AI-powered 3D imaging ecosystem for spatial profiling of lung geometry and pulmonary nanoparticle delivery
<p>The imaging dataset was obtained by light sheet fluorescence microscopy on tissue cleared murine lungs. It includes whole lung autofluorence image, particle fluorescence image, and artifical intelligence nnU-Net generated lung airway segments. The dataset provides 78 healthy murine lung strucutre and airway geometry for C57BL/6 mice and offers comprehensive delivery features including qualitative and quantitative analysis on the temporal and spatial inter- and intra-acinar deposition patterns and NP regional dosimetry for four commonly-used routes of pulmonary delivery,namely intranasal liquid aspiration, intratracheal liquid instillation, ventilator-assisted and nose-only aerosol inhalation.</p> <p>Raw LSFM imaging data collection was carried out between 2017-2021, the AI code and generated airway segmention were performed in 2021-2022, the whole datasets were then compiled in 2023. </p> <p>Please ensure to cite our paper for any reuse or reanalysis. Yang, L., Liu, Q., Kumar, P. <em>et al.</em> LungVis 1.0: an automatic AI-powered 3D imaging ecosystem unveils spatial profiling of nanoparticle delivery and acinar migration of lung macrophages. <em>Nat Commun</em> <strong>15</strong>, 10138 (2024). https://doi.org/10.1038/s41467-024-54267-1</p> <p>For any inquiries, please feel free to contact us at lin.yang@helmholtz-munich.de </p>
Polar Iridium Surface Velocity Profilers (p-iSVP), and standard Iridium Surface Velocity Profilers (iSVP) during SCALE 2019 Winter and Spring Cruises
<p><strong>Brief data description</strong></p> <p>In 2019, winter and spring scientific research expeditions aboard the SA Agulhas II were conducted along the Good-Hope line (0<sup>o</sup> E) to the Antarctic marginal ice zone (MIZ) in the north-eastern Weddell Sea region as part of the <em>Southern oCean seAsonal Experiment</em> (SCALE; Ryan-Keogh and Vichi, 2022).</p> <p>During the winter expedition, three polar Iridium Surface Velocity Profilers (p-iSVPs; MetOcean model) were deployed by the South African Weather Service (SAWS) between 27 July and 28 July 2019. These buoys were analysed in de Vos et al. (2022). The region of deployment consisted of pancake-ice conditions with an average ice thickness of 40-60 cm. The instruments were deployed by hand by three people, lowered by crane from the ship to the ice on a basket cradle. The first buoy (p-iSVP 1) was deployed in water, in between pancake ice floes, while the other two buoys (p-iSVP 2 and p-iSVP 3) were deployed on roughly circular ice floes > 3 m in diameter.</p> <p>These buoys were expendable devices that recorded GPS position, air and ice temperature, and barometric pressure. The temporal resolution is 30 minutes for p-iSVP 1 and hourly for p-iSVP 2 and p-iSVP 3. The survival of these sensors depended on their battery life, since p-iSVPs can continue to drift in the ocean after ice melting and can be further refrozen in between floes. p-iSVP 1 and p-iSVP 3 continued to transmit data until 15 October 2019. p-iSVP 2 stopped transmitting data on 25 August 2019.</p> <p>During the spring expedition, three standard Iridium Surface Velocity Profilers (iSVPs 4-6; Pacific Gyre model) were deployed by SAWS between 24 October and 28 October 2019 (de Vos et al., 2022). Specifically-designed frames were built around these three iSVPs to allow them to stand securely on the ice, without damaging the non-polar battery, and also to make sure they operated as Lagrangian ice trackers. These buoys were deployed during first-year ice conditions, with an average ice thickness of 80-90 cm. The instruments were deployed with the same protocol as the winter buoys.</p> <p>These buoys recorded GPS position, air temperature and barometric pressure, every hour. Their survival, like the winter p-iSVPs, also depended on their battery life, and therefore it was possible for them to continue to drift after ice melting. The iSVPs transmitted data until 19 December 2019.</p> <p><strong>Buoy names and raw data:</strong></p> <p>p-iSVP 1: 300234067003010-300234067003010-20191015T064320UTC.csv</p> <p>p-iSVP 2: 300234067002060-300234067002060-20191015T064316UTC.csv</p> <p>p-iSVP 3: 300234066992870-300234066992870-20191015T064314UTC.csv</p> <p>iSVP 4: 300234066433050.xlsx</p> <p>iSVP 5: 300234066433051.xlsx</p> <p>iSVP 6: 300234066433052.xlsx</p> <p><strong>Related code: </strong>The buoy data has been processed using https://github.com/mvichi/antarctic-buoys/. </p>
Chesley et al., 2023 - Southern Hikurangi Margin Electromagnetic Data from HT-RESIST trench-crossing profile
<p>Chesley_etal_2023_South-Hikurangi-EM-data.txt contains controlled-source electromagnetic data and magnetotelluric data from the southern trench-crossing profile of the Hikurangi Trench Regional Electromagnetic Survey to Image the Subduction Thrust (HT-RESIST) project (see https://emlab.ldeo.columbia.edu/index.php/category/ht-resist/ for information regarding the survey). Chesley_etal_2023_South-Hikurangi-bathymetry.txt is the associated bathymetry file. </p>
Main sediment profiles and 3D-pdf from archaeological wetland excavation at Immensee-Dorfplatz
<p>These are a 3D model and the longest and most representative sediment profiles through the waterlogged Late Neolithic lakeside site of Immensee-Dorfplatz (SZ). </p>
Packing provenance using CPM RO-Crate profile
<p>This dataset is an <a href="https://www.researchobject.org/ro-crate/">RO-Crate</a> that bundles artifacts of an AI-based computational pipeline execution. It is an example of application of the <a href="https://by-covid.github.io/cpm-ro-crate/0.1/">CPM RO-Crate profile</a>, which integrates the <a href="https://doi.org/10.1038/s41597-022-01537-6">Common Provenance Model</a> (CPM), and the <a href="https://www.researchobject.org/workflow-run-crate/profiles/process_run_crate">Process Run Crate profile</a>.</p> <p>As the CPM is a groundwork for the<em> ISO 23494 Biotechnology — Provenance information model for biological material and data</em> provenance standards series development, the resulting profile and the example is intended to be presented at one of the ISO TC275 WG5 regular meetings, and will become an input for the<em> ISO 23494-5 Biotechnology — Provenance information model for biological material and data — Part 5: Provenance of Data Processing</em> standard development.</p> <p><strong>Description of the AI pipeline</strong></p> <p>The goal of the AI pipeline whose execution is described in the dataset is to train an AI model to detect the presence of carcinoma cells in high resolution human prostate images. The pipeline is implemented as a set of python scripts that work over a filesystem, where the datasets, intermediate results, configurations, logs, and other artifacts are stored. In particular, the AI pipeline consists of the following three general parts:</p> <ul> <li> <p><strong>Image data preprocessing</strong>. Goal of this step is to prepare the input dataset – whole slide images (WSIs) and their annotations – for the AI model. As the model is not able to process the entire high resolution images, the preprocessing step of the pipeline splits the WSIs into groups (training and testing). Furthermore, each WSI is broken down into smaller overlapping parts called patches. The background patches are filtered out and the remaining tissue patches are labeled according to the provided pathologists’ annotations.</p> </li> <li> <p><strong>AI model training</strong>. Goal of this step is to train the AI model using the training dataset generated in the previous step of the pipeline. Result of this step is a trained AI model.</p> </li> <li> <p><strong>AI model evaluation</strong>. Goal of this step is to evaluate the trained model performance on a dataset which was not provided to the model during the training. Results of this step are statistics describing the AI model performance.</p> </li> </ul> <p>In addition to the above, execution of the steps results in generation of log files. The log files contain detailed traces of the AI pipeline execution, such as file paths, model weight parameters, timestamps, etc. As suggested by the CPM, the logfiles and additional metadata present on the filesystem are then used by a provenance generation step that transforms available information into the CPM compliant data structures, and serializes them into files. </p> <p>Finally, all these artifacts are packed together in an RO-Crate.</p> <p>For the purpose of the example, we have included only a small fragment of the input image dataset in the resulting crate, as this has no effect on how the Process Run Crate and CPM RO-Crate profiles are applied to the use case. In real world execution, the input dataset would consist of terabytes of data. In this example, we have selected a representative image for each of the input dataset parts. As a result, the only difference between the real world application and this example would be that the resulting real world crate would contain more input files. </p> <p><strong>Description of the RO-Crate</strong></p> <p><strong>Process Run Crate related aspects</strong></p> <p>The Process Run Crate profile can be used to pack artifacts of a computational workflow of which individual steps are not controlled centrally. Since the pipeline presented in this example consists of steps that are executed individually, and that the pipeline execution is not managed centrally by a workflow engine, the process run crate can be applied. </p> <p>Each of the computational steps is expressed within the crate’s ro-crate-metadata.json file as a pair of elements: 1) SW used to create files; 2) specific execution of that SW. In particular, we use the SoftwareSourceCode type to indicate the executed python scripts and the CreateAction type to indicate actual executions. </p> <p>As a result, the crate consists the seven following “executables”:</p> <ul> <li> <p>Three python scripts, each corresponding to a part of the pipeline: preprocessing, training, and evaluation.</p> </li> <li> <p>Four provenance generation scripts, three of which implement the transformation of the proprietary log files generated by the AI pipeline scripts into CPM compliant provenance files. The fourth one is a meta provenance generation script.</p> </li> </ul> <p>For each of the executables, their execution is expressed in the resulting ro-crate-metadata.json using the CreateAction type. As a result, seven create-actions are present in the resulting crate.</p> <p>Input dataset, intermediate results, configuration files and resulting provenance files are expressed according to the underlying RO Crate specification.</p> <p><strong>CPM RO-Crate related aspects</strong></p> <p>The main purpose of the CPM RO-Crate profile is to enable identification of the CPM compliant provenance files within a crate. To achieve this, the CPM RO-Crate profile specification prescribes specific file types for such files: CPMProvenanceFile, and CPMMetaProvenanceFile.</p> <p>In this case, the RO Crate contains three CPM Compliant files, each documenting a step of the pipeline, and a single meta-provenance file. These files are generated as a result of the three provenance generation scripts that use available log files and additional information to generate the CPM compliant files. In terms of the CPM, the provenance generation scripts are implementing the concept of provenance finalization event. The three provenance generation scripts are assigned SoftwareSourceCode type, and have corresponding executions expressed in the crate using the CreateAction type.</p> <p><strong>Remarks</strong></p> <p>The resulting RO Crate packs artifacts of an execution of the AI pipeline. The scripts that implement individual steps of the pipeline and provenance generation are not included in the crate directly. The implementation scripts are hosted on github and just referenced from the crate’s ro-crate-metadata.json file to their remote location.</p> <p>The input image files included in this RO-Crate are coming from the <a href="http://gigadb.org/dataset/100439">Camelyon16 dataset</a>.</p>
Pertubation Profiles Dataset used for "Convection-generated gravity waves in the tropical lower stratosphere from Aeolus wind profiling and ERA5 reanalysis"
<p>These are the perturbation profiles, from 5km to 29.5km, with a 500m grid. In the study, we picked up the data between tropopause-1km to 22km, which was then squared, smoothed, and averaged into one value. We used a 14 points moving average for the smoothing.</p> <p>The data is from 2018-09 to 2022-09, based on the Aeolus L2B Rayleigh clear wind, using only quality flag 1 data.</p> <p>Please email me at mathieu.ratynski@estaca.eu if you're interested in the 100m resolution version, used in the final version of the manuscript.</p>
Crosswalk between CESSDA Data Catalogue (CDC) Metadata Profile and ECRIN Metadata Schema.
<p>This dataset contains two files: (1) a crosswalk between CESSDA Data Catalogue (CDC) DDI2.5 Metadata Profile (<a href="https://cmv.cessda.eu/profiles/cdc/ddi-2.5/1.0.4/profile.html">https://cmv.cessda.eu/profiles/cdc/ddi-2.5/1.0.4/profile.html</a>) and ECRIN Metadata Schema for Clinical Research Data Objects Version 6.0 (August 2021) (<a href="https://zenodo.org/record/5554961">https://zenodo.org/record/5554961</a>) with an extension to “geographical data” and (2) vice versa.</p>
Primary data – 13C Solid-State Buildup and Microwave Sweep Profiles in a DNP Polarizer
<p>These primary datasets consist of:</p> <p>1. 13C polarization buildup data in solid-state</p> <p>2. 13C microwave profiles in solid state</p> <p>Please consult the Archive Guide.</p>
Data of the publication "Real-time broadening of bath-induced density profiles from closed-system correlation functions"
<p>The Lindblad master equation is one of the main approaches to open quantum systems. While it has been<br> widely applied in the context of condensed matter systems to study properties of steady states in the limit<br> of long times, the actual route to such steady states has attracted less attention yet. Here, we investigate the<br> nonequilibrium dynamics of spin chains with a local coupling to a single Lindblad bath and analyze the transport<br> properties of the induced magnetization. Combining typicality and equilibration arguments with stochastic<br> unraveling, we unveil for the case of weak driving that the dynamics in the open system can be constructed<br> on the basis of correlation functions in the closed system, which establishes a connection between the Lindblad<br> approach and linear response theory at finite times. In this way, we provide a particular example where closed and<br> open approaches to quantum transport agree strictly. We demonstrate this fact numerically for the spin-1/2 XXZ<br> chain at the isotropic point and in the easy-axis regime, where superdiffusive and diffusive scaling is observed,<br> respectively.</p>
Metabolic profiling and antibacterial activity of tree wood extracts obtained under variable extraction conditions
<p>Scripts and dataset for Vinchira-Villarraga et al., 2024 "Metabolic profiling and antibacterial activity of tree wood extracts obtained under variable extraction conditions". The files label as A1, C6, H2/HCN2 and O1 correspond to the dataset obtained for Ash, Cherry, Horse-chestnut and Oak respectively. </p> <p>_batch.xml files contains the script for the pre-processing of .mzML mass spectrometry data created on MzMine 3.2.8. The file can be uploaded to newer versions of MzMine, but due to the addition of new modules, some parameters name have changed.</p> <p>_SIRIUS.mfg files contains the mass spectrometry data of each dataset for analysis in SIRIUS. The files were used for chemical formula prediction, classification and compound annotation using SIRIUS 5.0</p> <p>_quant.csv correspond to the output matrix as it was obtained from MzMine without noise, adducts and highly variable features filtering.</p> <p>_abundance.csv correspond to the output matrix after noise, adducts and highly variable features filtering.</p>
Mountain Lake Chemistry and Physics Profile Data since 2015 at Castle Lake
This data set contains long-term limnology data from Castle Lake (located 5440 ft above sea level in Northern California). The data contained can be broadly grouped into two different types: chemical and physical data. This data set contains dissolved oxygen saturation, dissolved oxygen concentration, chlorophyll-a concentration, CDOM, phycocyanin concentration, conductivity, specific conductivity, pH, salinity, water temperature, pressure, turbidity, sea pressure, density anomaly, and speed of sound at various depths. Sampling Frequency: Continuous measurements were made and recorded down to milliseconds. Sampling dates vary from year to year, starting as early as February and as late as November. This data collection is part of an ongoing project funded by the US National Science Foundation, private donors, US AID, and the University of Nevada's Global Water Center. This package was updated in February 2023 with data from 2021 and 2022. Dissolved O2 concentration data in the 2020 data file was updated to values in units of mg/L instead of umol/L and specific conductivity units in all files was revised to be in microSiemens per centimeter and not milliSiemens per centimeter.
Stable isotope and conservative tracer data used to estimate uptake of stream water dissolved organic carbon (DOC) through a whole-stream addition of a ¹³C-DOC tracer coupled with laboratory measurements of bioavailability of the tracer and stream water DOC using lability profiling with bioreactors
We performed a whole-stream addition of a ¹³C-DOC tracer and made laboratory measurements of the biological availability of the tracer as well as stream water DOC. The study was performed in October 2002 in a 1.27 km stretch of the third-order White Clay Creek in southeastern Pennsylvania. The tracer was prepared as a cold-water leachate of ¹³C-labeled tulip poplar saplings and it was added to the stream along with sodium bromide, a conservative tracer, over a 2-h period. Stream water samples were collected at 8 downstream stations over an 8-h period, filtered, and analyzed for concentrations of bromide and DOC. DOC was measured by Pt-catalyzed, persulfate oxidation, Br- was analyzed by ion chromatography, and C isotope samples were rotary evaporated, acidified, lyophilized, combusted, and the CO₂ analyzed with an elemental analyzer interfaced with an isotope ratio mass spectrometer. Lability profiling of the ¹³C-DOC tracer and stream water DOC were performed with a series of plug-flow bioreactors of increasing empty-bed contact times with the concentration of biodegradable DOC operationally defined as the difference between the DOC concentrations in the influent and effluent waters of the bioreactors. The bioreactor measurements were performed 2 days after the whole-stream release. Data were analyzed to estimate the uptake of stream water DOC associated with labile and semi-labile fraction of biodegradable DOC. These data have been previously used in a 2008 publication in Freshwater Biology, doi:10.1111/j.1365-2427.2007.01941.x.
A Study of Hyporheic Characteristics Along a Longitudinal Profile of Lookout Creek, Oregon, 2003
Time series of tracer (Rhodamine WT) concentration data representing a "break-through curve" resulting from a single, multi-scale tracer test conducted along the length of Lookout Creek, from its 2nd-order headwaters through its 5th-order main stem. The longitudinal tracer test involved an in-stream injection of Rhodamine WT over 78 hrs, followed by monitoring at eight downstream locations for five months, starting in mid-June 2003 through the middle of November 2003. The injection site was located below the upper Old Growth Trail bridge. The monitoring sites include one downstream of the injection site, above and below the Cold Creek confluence, below the lower Old Growth Trail bridge, below Mack Creek confluence, above and below McRae Creek confluence, and at the HJA headquarters. Three additional inter-order tracer tests (IOTT) were carried out between each of the major stream confluences along Lookout Creek. The injection site for the McRae Creek IOTT was located below the McRae Cr. confluence and was monitored just downstream, at the end of the main floodplain, and at HJA headquarters. The injection site for the Mack Creek IOTT was located below the Mack Cr. confluence and was monitored just downstream, at an unnamed tributary midway between Mack Cr. and McRae Cr., and above the McRae Cr. confluence. The injection site for the Cold Creek IOTT was located below the Cold Cr. confluence and was monitored just downstream, at Longer Cr., and at the lower Old Growth Trail bridge. Concentration break through curves are available for all of the monitoring sites for the longitudinal and inter-order tracer tests. Flux break through curves are available for most of the monitoring sites. STAMMT-L model estimates for hyporheic exchange is listed for most of the monitoring sites.
Nitrogen cycling at treeline. IV. Soil Profile Descriptions
We studied spatial and temporal patterns of nitrogen pools and fluxes in soils at treeline and forested sites within three mountain ranges across a 785 km transect in Alaska during 2001- 2002. We measured pools of soil mineral (ammonium and nitrate) and organic (amino acid and microbial biomass) nitrogen, in situ rates of net mineralization, net nitrification, net amino acid production, and decomposition, as well as soil carbon turnover in a laboratory incubation experiment. A complete characterization of the study can be found in Loomis et al. (2006).
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.