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Data for: "Comprehensive sampling of coverage effects in catalysis by leveraging generalization in neural network models"
<p>This repository contains the raw data to reproduce the paper: "Comprehensive sampling of coverage effects in catalysis by leveraging generalization in neural network models". Within the .tar.gz file, you will find the directory structure described above.</p> <h2>Directory Structure</h2> <h3>`data`</h3> <p>Contains the data to reproduce all figures in the manuscript. Used primarily by the Jupyter Notebooks that plot the data from the paper.</p> <h3>`eval`</h3> <p>Contains the predicted energies according to a MACE model for the following systems and facets:<br>- covsplit (100, 111, 211, 331, 410, 711): The NN model is trained on low-coverage structures and tested on high-coverage structures for a single facet<br>- evencov (100, 111, 211, 331, 410, 711): The NN is trained on even coverages and tested on odd coverages for a single facet<br>- facet (100, 111, 211, 331, 410, 711): the NN is trained on the facet indicated by the folder name (e.g., facet-100 means that the model was trained on Cu(100)) and tested on all of the other facets.<br>- full: the model was trained on all facets and all coverages<br>- slopes (various versions and configurations): the models were trained with different body-order correlation (v) for the Cu(711) facet and tested only on the Cu(711) facet<br>- Rh111: Energies for the Rh(111) + CHOH + CO systems.</p> <h3>`mcmc`</h3> <p>Contains the data for MCMC (Markov Chain Monte Carlo) evaluations for two systems: Cu and Rh<br>- copper-mcmc-public.tar.gz<br>- rhodium-mcmc-public.tar.gz</p> <h3>`models`</h3> <p>Contains the weights and parameters of the best-performing MACE models trained in this work, as selected by the validation loss:</p> <p>File formats: `.model` and `_swa.model` relate to the first-stage of training and the second-stage of training.</p> <h3>`pyscripts`</h3> <p>Python scripts to perform the MCMC sampling given the custom configuration file `sample_cfg.json`.</p> <h3>`scripts`</h3> <p>Shell scripts for evaluation and training the MACE models, along with the hyperparameters used in doing so.</p> <p>- Evaluation scripts (eval-*.sh)<br>- Training scripts (train-*.sh)</p> <h3>`train`</h3> <p>Training, validation, and testing data for all Cu and Rh facets in this work, according to the naming scheme described above.</p> <p>- Rh111<br>- covsplit<br>- evencov<br>- facet<br>- full<br>- slopes</p>
Sampling study and collection phase (q-interline data)
<p>This dataset contains the spectral data acquired on waste wood samples using a FT-NIR spectrophotometer (Q-Interline A/S, Tølløse, Denmark) provided with the patented spiral sampler (Spiral Sampler, Q-Interline A/S, Tølløse, Denmark). The waste wood samples have been collected in a panel board company located in the Northern part of Italy during two days of sampling (February 18-19, 2020). In detail, 64 samples (16 lots x 4 samples from each lot) were obtained; later each sample has been split into two replicates for a total of 128 samples. Because of an unfortunate computer error (i.e. the computer froze without saving the collected data) only realized after arriving back at the University, the spectral data of two sample replicates have not been stored. Hence, the final dataset consists of 26192 observations and 1091 wavenumbers (around 210 NIR scans per sample replicate). The sample has been analysed with a particle size of 5 mm.</p> <p>The CSV file contains the following information in the columns:</p> <ul> <li>Sample code: it is reporting the sample code where S1 is the number of lot, the successive number is the number of subsample and the last number the NIR replicate. E.g. S01-01_122: lot number 1, subsample number 01, NIR replicate number 122. Please note that we have also letter ‘B’ when we are looking at the second subsample replicate.</li> <li>Lot: number of lot to which the sample belongs (from 1 to 16).</li> <li>Subsample: number 1, 7, 13 or 19 indicating the four subsamples;</li> <li>Replicate: number 1 or 2 indicating first or second replicate.</li> <li>Measurement: the number of the NIR measurement.</li> <li>Spectral data: absorbance values for each sample from 3595.05 cm<sup>-1</sup> to 12004.07 cm<sup>-1</sup>.</li> </ul> <p>The aim behind this dataset is to provide information about the frequency of sampling and number of replicates and scans to perform for describing the waste wood variability (WP1 of WoodSpec project). A correct sampling procedure is fundamental to guarantee an accurate and successful use of a NIR sensor into real industrial applications and improve the waste wood management, especially when dealing with heterogeneous material.</p> <p><em>Funding: The project leading to this application has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 838560. </em></p> <p><em>Terms of use: These data are provided "as is", without any warranties of any kind. The data are provided under the Creative Commons Attribution 4.0 International license.</em></p>
GVI: Sample data for computing VGVI. Vancouver, BC and Manchester.
<p>This is a supplement for the <a href="http://doi.org/10.5281/zenodo.5068835">GVI: Greenness Visibility Index R package</a>.</p> <p> </p> <p>Description:</p> <p>This dataset contains raster (TIFF) data for computing the VGVI for the City of Vancouver and Manchester.</p> <p><strong>Greater Manchester:</strong></p> <ul> <li>Digital Terrain Model (DTM): <ul> <li>Spatial Resolution: 5m</li> <li>Source: <a href="https://data.gov.uk/dataset/5f6f7d5b-3f4c-4476-bfb8-cda490c9cf0e/lidar-composite-dtm-2017-50cm">LIDAR Composite DTM 2017 - 50cm</a></li> <li>Licence: <a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">Open Government Licence (OGL)</a></li> <li>File name: GreaterManchester_DTM_5m.tif<br> </li> </ul> </li> <li>Digital Surface Model (DSM): <ul> <li>Spatial Resolution: 5m</li> <li>Source: <a href="https://data.gov.uk/dataset/0ab507af-cd91-40cb-8524-3efafc267211/lidar-composite-dsm-2017-50cm">LIDAR Composite DSM 2017 - 50cm</a></li> <li>Licence: <a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">Open Government Licence (OGL)</a></li> <li>File name: GreaterManchester_DSM_5m.tif<br> </li> </ul> </li> <li>Greenspace Mask: <ul> <li>Spatial resolution: 5m</li> <li>Source: <a href="https://doi.org/10.3390/land7010017">Dennis et al. 2017</a></li> <li>Licence: <a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">Open Government Licence (OGL)</a></li> <li>File name: GreaterManchester_GreenSpace_5m.tif</li> </ul> </li> </ul> <p> </p> <p><strong>Vancouver:</strong></p> <ul> <li>Digital Terrain Model (DTM): <ul> <li>Spatial Resolution: 1m</li> <li>Source: Canada’s Open Government Portal</li> <li>Licence: <a href="https://open.canada.ca/en/open-government-licence-canada">Open Government Licence - Canada</a></li> <li>File name: Vancouver_DTM_1m.tif<br> </li> </ul> </li> <li>Digital Surface Model (DSM): <ul> <li>Spatial Resolution: 1m</li> <li>Source: Canada’s Open Government Portal</li> <li>Licence: <a href="https://open.canada.ca/en/open-government-licence-canada">Open Government Licence - Canada</a></li> <li>File name: Vancouver_DSM_1m.tif<br> </li> </ul> </li> <li>Greenspace Mask: <ul> <li>Spatial Resolution: 2m</li> <li>Source: Land Cover Classification 2014 - 2m LiDAR</li> <li>Licence: <a href="http://www.metrovancouver.org/data">Metro Vancouver</a></li> <li>File name: Vancouver_GreenSpace_2m.tif<br> </li> </ul> </li> <li>Landuse <ul> <li>Spatial Resolution: 2m</li> <li>Source: Land Cover Classification 2014 - 2m LiDAR</li> <li>Licence: <a href="http://www.metrovancouver.org/data">Metro Vancouver</a></li> <li>File name: Vancouver_LULC_2m.tif</li> </ul> </li> </ul>
Multi-decade land use and land cover samples for Brazil based in a stratified sampling design and visual interpretation of Landsat data (1985 — 2018)
<p>This dataset is composed by 85,152 random points throughout the Brazilian territory selected according to a stratified sampling design, based in 127 regular regions and six slope classes (<a href="https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-shuttle-radar-topography-mission-srtm-1-arc?qt-science_center_objects=0#qt-science_center_objects">SRTM</a>). Each sample was visually inspected by three independent interpreters, which associated all the land use and land cover (LULC) changes between 1985 and 2018, on a <strong>yearly basis</strong>, using as reference two <strong>Landsat</strong> images per year, a <strong>MODIS</strong> NDVI time series and high resolution images from <strong>Google Earth</strong>. </p> <p>This process was guided by a <a href="https://www.lapig.iesa.ufg.br/chave/">reference labeling protocol</a> which established the follow LULC classes:</p> <ul> <li><strong>Annual crop:</strong> Areas occupied with short to medium-term crops, usually with a vegetative cycle of less than one year, which after harvest needs to be re-planted. </li> <li><strong>Aquaculture:</strong> Artificial lakes, where aquaculture and/or salt production activities predominate</li> <li><strong>Beach and dune (Other):</strong> Sandy areas, with bright white color, where there is no vegetation predominance of any kind.</li> <li><strong>Forest formation:</strong> Vegetation types with predominance of tree species, with continuous canopy formation</li> <li><strong>Grassland formation:</strong> Grassland formations with predominance of herbaceous stratum</li> <li><strong>Mangrove (Other):</strong> Dense and Evergreen Forest formations, often flooded by tide and associated with the mangrove coastal ecosystem.</li> <li><strong>Mining (Other):</strong> Areas where clear signs of extensive mineral extractions are present, shows clear exposure of the soil by the action of heavy machinery. Only regions surrounding the AhkBrasilien (AHK) and the CPRM digital reference data were considered.</li> <li><strong>Not observed:</strong> Areas blocked by clouds or atmospheric noise, or with absence of ground observation masked out from analysis.</li> <li><strong>Other non-forest natural formations:</strong> Marshes (with fluvio-marine influence).</li> <li><strong>Other non-vegetated area (Other):</strong> Non-permeable surface areas (infrastructure, urban expansion or mining) not mapped into their classes</li> <li><strong>Pasture:</strong> Pasture areas, natural or planted, related with farming activity. In particular in the Pampa and Pantanal biomes part of the area classified as Grassland Formation also includes pasture areas.</li> <li><strong>Perennial crop:</strong> Areas occupied with crops with a long cycle (more than one year), which allow successive harvests without the need for new crop. </li> <li><strong>Rocky outcrop (Other)</strong>: Naturally exposed rocks without soil cover, often with the partial presence of rupicolous vegetation and high slope. </li> <li><strong>Salt flat (Other):</strong> "Apicuns" or Salt flats are formations often without tree vegetation, associated to a higher, hypersaline and less flooded area in the mangrove, generally in the transition between this area and the continent.</li> <li><strong>Savanna formation:</strong> Savanna formations with defined tree and shrub-herbaceous stratum</li> <li><strong>Semi-perennial crop:</strong> Cultivated areas with sugar cane</li> <li><strong>Tree plantation:</strong> Planted tree species for commercial use (e.g. Eucalyptus, Pinus and Araucaria)</li> <li><strong>Urban infrastructure:</strong> Urban areas with predominance of non-vegetated surfaces, including roads, highways and constructions.</li> <li><strong>Water:</strong> Rivers, lakes, dams, reservoir and other water bodies</li> <li><strong>Wetland:</strong> Wetlands with fluvial influence or swampy areas</li> </ul> <p>To enable a proper area estimation and accuracy assessment (<a href="https://www.tandfonline.com/doi/abs/10.1080/01431161.2014.930207">Stehman, 2014</a>) the dataset is provided with the <strong>sampling probability</strong> for each sample (<em>brazil_lulc_samples_1985_2018</em> and <em>brazil_lulc_samples_1985_2018_row_wise</em>) and the <strong>sampling weight</strong> (<em>brazil_lulc_samples_1985_2018_row_wise</em>), which was adjusted to disregard the "<strong>Not observed" </strong>class. The number of votes for the associated LULC class (visual interpretation agreement) and an indication if the sample is between two different LULC<strong> </strong>classes (<strong>border flag</strong>) are also provided.</p> <p>The samples were used to produce several <strong><a href="https://github.com/lapig-ufg/tvi-analysis">area estimation analyses</a></strong>, including land use and land cover dynamics, historical deforestation and agricultural expansion of Brazil. A publication describing in detail the methodology and the analysis is under preparation.</p>
Sample generalised Gross-Pitaevskii data for circulation statistics
<p>Sample dataset containing an instantaneous complex wave function field obtained from a three-dimensional generalised Gross-Pitaevskii simulation.</p> <p>The dataset is split into two files: one for the real part, and the other for the imaginary part of the wave function field <span class="math-tex">\(\psi(x, y, z)\)</span>.</p> <p>The dataset resolution is <span class="math-tex">\(256^3\)</span> grid points. The data is encoded as raw binary data, written in little-endian order, in double precision (64-bit floating point precision).</p>
Field data for: Enterovirus sequence data obtained from primate samples in Central Africa suggest a high prevalence of enteroviruses with possible zoonotic potential
<p>Enteroviruses infect humans and animals, can cause disease, and some may be transmitted across species barriers. We collected different types of samples from various species of Central African wildlife, including data on sampling location and tested the samples for the presence of Enterovirus RNA using a family level PCR. Specimen collection was approved by an Institutional Animal Care and Use Committee (IACUC) of the University of California Davis, and the Governments of Cameroon and the Democratic Republic of the Congo. Enterovirus RNA was detected in samples from 17 primates and 2 rodents. Some sequences were very similar while others were dissimilar to known species, highlighting the unexplored enterovirus diversity in wildlife.</p> <p>The samples and filed data were collected by field ecologists as part of the USAID funded PREDICT project (https://ohi.vetmed.ucdavis.edu/programs-projects/predict-project) and screened for enterovirus RNA using consensus PCR. Maps were generated using basic maps from Paintmaps (http://www.paintmaps.com), a free tool for educational and academic use. The dataset contains the metadata on enterovirus screening among wildlife in Cameroon and the Democratic Republic of the Congo from 2003-2014 as part of the USAID funded PREDICT project. Please refer to the article for more information on methods and references.</p>
Data from: Flattening the curve: approaching complete sampling for diverse beetle communities
<p><strong>DATA FROM:</strong></p> <p>Burner, R., J. Åstrom, T. Birkemoe, A. Sverdrup-Thygeson. 2021. Flattening the curve: approaching complete sampling for diverse beetle communities. <em>Insect Conservation and Diversity</em> <a href="https://doi.org/10.1111/icad.12540">https://doi.org/10.1111/icad.12540</a> </p> <p> </p> <p><strong>ACKNOWLEDGEMENTS</strong></p> <p>This research was funded by the Norwegian Environment Directorate as part of an ‘Agreement on monitoring hollow oaks and insects in hollow oaks’. The Norwegian University of Life Sciences (NMBU) workshop designed and produced the cross-pane flight intercept traps. Thanks to Sindre Ligaard for identifying the beetle species, and to Lindsay Burner, Ruben Roos, and Ross Wetherbee for assistance in the field. High-performance computing resources were provided by Frederick H. Sheldon and Louisiana State University (LSU HPC).</p> <p><strong>INFORMATION</strong></p> <p>This dataset contains all data necessary to reproduce the analysis in the resulting manuscript. Briefly, 110 insect traps were set for 3 months in a single forest stand in Ås, Norway in 2020. This dataset includes trap locations, number of individuals of each species captured in each trap, trap type, and forest covariates collected around the traps.</p> <p>For more detailed information see manuscript and README file.</p> <p>From abstract of manuscript:</p> <ol> <li>Insects are a hyper diverse and ecologically important group. Their high diversity, however, presents challenges in sampling methodology, because rare species are unreliably detected with low sampling effort. However, the relationship between effort and species detections, critical for effective monitoring and evaluation of population trends, is too seldom quantified.</li> <li>We sampled forest beetles for three months in a 4-ha stand of mixed deciduous forest in southeastern Norway using 110 flight intercept (four types) and Malaise traps, the highest trap density (29 traps/ha) that we have seen reported. We examined species accumulation curves to quantify the benefits of each additional trap, compared capture rates among several trap designs and trap emptying frequencies, and tested for spatial autocorrelation.</li> <li>In total we captured 566 beetle taxa (19,854 individuals) from 52 families, yet our species accumulation curve was only beginning to flatten. Trap types differed considerably in their effectiveness. Nevertheless, twenty of our most effective window traps detected 75% of all taxa in our dataset. We found no evidence of spatial correlation within the scale of the study (100 m radius), nor did trap-level forest covariates (5 m radius) explain much variation.</li> <li>This implies that low to moderate sampling effort dramatically underestimates species richness, but that a limited number of effective traps can nonetheless achieve relatively thorough sampling for some applications. Immediate trap surroundings and spacing appeared unimportant. But, insect ecologists should take particular care in selecting trap types and be cautious comparing studies that employed different trap types.</li> </ol> <p> </p>
GIRT-Data: Sampling GitHub Issue Report Templates
<p><strong>GIRT-Data</strong> is the first and largest dataset of <strong>issue report templates (IRTs)</strong> in both YAML and Markdown format. This dataset and its corresponding open-source crawler tool are intended to support research in this area and to encourage more developers to use IRTs in their repositories. The stable version of the dataset, containing <code>1_084_300</code> repositories, that <code>50_032</code> of them support IRTs.</p> <p>For more details see the GitHub page of the dataset: <a href="https://github.com/kargaranamir/girt-data">https://github.com/kargaranamir/girt-data</a></p> <p><br> The dataset is accepted for <a href="https://conf.researchr.org/track/msr-2023/msr-2023-data-showcase">MSR 2023</a> conference, under the title of "GIRT-Data: Sampling GitHub Issue Report Templates" <a href="https://scholar.google.com/scholar?q=GIRT-Data:+Sampling+GitHub+Issue+Report+Templates">Search in Google Scholar</a>.</p>
AIRSEAL Project - Data sample
<p>The attached files contain test data (main parameters measured during rotating labyrinth seals testing).<br> The .csv data is structured as follows:</p> <p> column1 = Time [s]<br> column2 = Tut [deg C]<br> column3 = PR [non dimentional]<br> column4 = N [rpm]<br> column5 = CLR [mm]<br> column6 = Qma [kg/s]<br> column7 = Swirler_angle [deg]<br> <br> The "11" in file names is related to the first configuration (which has been tested in the project): METCO casing and axial flow (0 deg swirl).<br> The "1" to "30" file indexes are related to test number (stabilized regime: pressure ratio, rotor speed and temperature).</p>
MEaSUREs blue band total column water vapor sample data for the Ozone Monitoring Instrument
<p>This dataset contains the MEaSUREs OMI Total Column Water Vapor (TCWV) data and their related data used in the paper titled “Development of the MEaSUREs blue band water vapor algorithm – Towards a long-term data record” by Wang et al. (2023). The unzipped archive contains the following three directories. </p> <ol> <li>OMI-H2O-L2/ contains the MEaSUREs Level 2 data (in molecules/cm2) in netCDF4 format for January and July 2005 and 2006. Selected supporting data are also included in each file.</li> <li>OMI-H2O-L3/ contains MRaSUREs Level 3 data (0.25 degree by 0.25 degree, in molecules/cm2) generated using the standard filtering criteria in netCDF4 format for January and July 2005 and 2006. Selected supporting data are also included.</li> <li>Model3_ncresult/ contains netCDF4 formatted files for the MEaSUREs OMI TCWV data (in mm), the AMSR_E TCWV data sampled onto the corresponding OMI pixel locations, and the LightGBM model 3 predictions for the OMI pixels.</li> </ol> <p>The linux command ‘ncdump -h filename’ can be used to examine the contents of netCDF4 files. Due to the current size limit of Zenodo, only a small subset of the MEaSUREs data is archived here. The full dataset will be released elsewhere, e.g., NASA EARTHDATA GES DISC.</p>
Mangrove Crab Sampling Data in Dongzhaigang National Nature Reserve, Haikou, Hainan Province, China
<p>This dataset contains the results of a study on mangrove crabs conducted in four seasons (Summer, SU; Autumn, AU; Winter, WI; Spring, SP) of 2020 and 2021. The samples were collected in the Dongzhaigang National Nature Reserve, Haikou, Hainan Province, China, at five sites: Sanjiang (SJ), Tashi (TS), Shanweitou (SWT), Luodou (LD), and Puqian (PQ). The primary focus is on crab species belonging to the superfamilies Ocypodoidea (ghost crabs), Grapsoidea (square crabs), and Portunoidea (swimming crabs).</p> <p>Sampling was conducted using net trapping, with three replicate plots set up for each habitat type at each site. Each plot was sampled continuously for three days. Vegetation information was recorded using dominant species as representatives, and water environmental information was collected using a WTW instrument. The parameters measured include total dissolved solids (TDS) (mg/L), dissolved oxygen (DO) (mg/L), salinity (SAL) (‰), water temperature (T) (℃), and pH. Finally, the longitude and latitude in the WGS84 coordinate system and Cartesian coordinates for each plot were recorded.</p> <p>The dataset fields are as follows:</p> <ul> <li>date: Date of sampling</li> <li>year: Year of sampling</li> <li>month: Month of sampling</li> <li>day: Day of sampling</li> <li>site: Sampling location, including TS, SJ, SWT, LD, PQ</li> <li>habitat: Habitat type, including tidal channels, tidal flats, and several vegetation types represented by mangrove trees such as Avicennia marina, Rhizophora stylosa, Bruguiera sexangular, Sonneratia apetala, and Ceriops tagal.</li> <li>plotname: Plot name</li> <li>species: Species name, as per the World Register of Marine Species (<a href="https://www.marinespecies.org/">https://www.marinespecies.org</a>)</li> <li>superfamily: Superfamily, as per the World Register of Marine Species (<a href="https://www.marinespecies.org/">https://www.marinespecies.org</a>)</li> <li>season: Season, including Summer (SU), Autumn (AU), Winter (WI), and Spring (SP)</li> <li>cname: Plot division by season, site, and habitat</li> <li>fullname: Plot division by season, site, habitat, and plot sequence number</li> <li>pname: Plot division by site, habitat, and plot sequence number</li> <li>TDS: Water total dissolved solids (mg/L)</li> <li>pH: Water pH</li> <li>DO: Water dissolved oxygen (mg/L)</li> <li>T: Water temperature (℃)</li> <li>SAL: Water salinity (‰)</li> <li>longitude: Longitude in WGS84 coordinate system</li> <li>latitude: Latitude in WGS84 coordinate system</li> <li>x: Cartesian coordinate x</li> <li>y: Cartesian coordinate y</li> </ul> <p>We thank Chengpu Jiang, Liangjun Wei and other colleagues for their assistance during the field samplings. Thanks also for the experimental conditions and sampling support provided by Hainan Dongzhaigang National Nature Reserve Authority.</p>
Raw data for the Article "Cyclopentadienone Iron Complex-Catalyzed Hydrogenation of Ketones: An Operando Spectrometric Study Using Pressurized Sample Infusion-Electrospray Ionization-Mass Spectrometry"
<p>This data set contains the raw data (NMR, LC-MS, ESI-MS, HRMS, Elemental Analysis) for the article "Cyclopentadienone Iron Complex-Catalyzed Hydrogenation of Ketones: An <em>Operando</em> Spectrometric Study Using Pressurized Sample Infusion-Electrospray Ionization-Mass Spectrometry" published in <em>Organometallics</em>, DOI:</p> <p><a href="https://doi.org/10.1021/acs.organomet.2c00341">https://doi.org/10.1021/acs.organomet.2c00341</a></p> <p>The compound names correspond to the ones used in the article and its supporting information.</p>
Size, age, telomere and ecophysiology data of Gallotia galloti lizard species sampled in Tenerife
<p>The dataset is used in the manuscript "Nina Serén, Rodrigo Megía-Palma, Tatjana Simčič, Miha Krofel, Fabio Maria Guarino, Catarina Pinho, Anamarija Žagar, Miguel A. Carretero. Functional responses in a lizard along a 3.5 km altitudinal gradient. Journal of Biogeography (under review)."</p> <p>The dataset consists of measurements of individual lizards of the species Gallotia galloti, each tagged with a unique CODE. Data include year of sampling, population name, exact elevation (in meters above sea level) and approximate elevation (rounded to the nearest hundred, in meters), and sex. Measurements were as follows: Snout Vent Length (in millimeters), Mass (in grams), AGE_Consensus (in years), Relative Telomere Length, PMA(29ºC, 33 ºC and 37ºC) (Potential metabolic activity measured at experimental conditions of 29˚C, 33ºC and 37ºC, respectively,in µLO2/mg prot/h), Catalase (in relative units U/mg protein), EWLa (accumulated evaporative water loss (in grams) and Temperature_8AM-5PM (measurements of cloacal temperature at hourly intervals starting at 8AM and ending at 5PM).</p>
Synthetic data set "Synth1" for the paper "Adaptive Sampling of 3D Spatial Correlations for Focus+Context Visualization"
<p>Synthetic data set "Synth1" for the paper "Adaptive Sampling of 3D Spatial Correlations for Focus+Context Visualization".<br>Preprint of the paper available at: <a href="https://arxiv.org/abs/2309.03308">https://arxiv.org/abs/2309.03308</a></p>
Winter sampling microclimatic data using handheld devices at a shrub gradient across Californian drylands.
During a two-week sampling period in February 2023, we collected microclimatic data, including temperature and relative humidity, at shrubs and in the open across 9 sites in California. We collected ambient and ground temperatures.
Small reservoir grab sample data, Parker, Ipswich, Lamprey, and Oyster River Watersheds, 2015 - 2020
This dataset contains water quality data from the inputs and outputs of eight reservoirs located in southeastern New Hampshire and northeastern Massachusetts. Samples were filtered in the field using Whatman GF/F filters with a nominal pore size of 0.7 µm and stored on ice until returned to the lab where they were frozen until analysis. Samples were analyzed for anions (NO3-, Cl-, SO4-, Br-) via ion chromatography on a Dionex Ion Chromatograph; NH4+ using a colorimetric method on a SmartChem Chemistry Analyzer; and total dissolved nitrogen (TDN) and dissolved organic carbon (DOC) via high-temperature oxidation on a Shimadzu TOC-V. DON was calculated as the difference between TDN and DIN (NO3- + NH4+). Samples correspond with individual sample names. Site (n = 8) indicate which reservoir each sample belongs to. Location indicates whether a sample was collected at the outflow or inflow ("UP", for reservoirs with one inflow) or "UP_1", "UP_2", or "UP_3" for sites with up to three inflows. CArea.km2 is the upstream drainage area estimated from the reservoir outflow while SArea.km2 is the surface area of each reservoir calculated via NHD+ polygons in GIS software. Temp.C is water temperature at the reservoir outflow on the day of sample collection while Discharge.m3s is the daily discharge scaled to the reservoir outflow from the nearest USGS gaging station. DIN.mgL, DON.mgL, and TDN.mgL are concentrations of dissolved inorganic nitrogen, dissolved organic nitrogen, and total dissolved nitrogen, respectively, in mg/L.
Water chemistry data including nitrate stable isotopes sampled from zero-tension lysimeters in an Iowa corn-soybean field in 2017 and 2018
These data were used in the manuscript titled "Mechanisms underlying episodic nitrate and phosphorus leaching from poorly drained agricultural soils" published in the Journal of Environmental Quality. We measured nitrate, ammonium, and phosphate concentrations in zero-tension lysimeters installed along a topographic gradient in a corn and soybean field in north-central Iowa, USA, during 2017 and 2018. We measured nitrate stable isotope compositions in a subset of lysimeter samples. Concentrations of nitrate, ammonium, and ferrous and ferric iron were measured in periodic soil extractions co-located with the lysimeters.
Field data for seasonal synoptic sampling of 100 urban streams in Boston, Massachusetts (USA) from 2021-2022
This dataset contains field measurements taken during water sampling from 100 urban stream locations in the greater Boston, Massachusetts (USA) metropolitan area. Field collection took place during four synoptic sampling events (September 2021, November 2021, April 2022, and July 2022) to capture spatial and seasonal variation in stream conditions (specific conductivity, water temperature, dissolved oxygen, pH). Filtered stream samples were analyzed for dissolved organic carbon concentration and characteristics, available in a separate dataset. These data were collected as part of the Carbon in Urban Rivers Biogeochemistry (CURB) Project. Detailed field data and site data are published separately and can be linked using the “curbid” and “synoptic_event” columns in each dataset.
Field data for seasonal synoptic sampling of 100 urban streams in Miami, Florida (USA), 2021-2022
This dataset contains field measurements taken during water sampling from 100 urban stream locations in the greater Miami, Florida metropolitan area. Field collection took place during five synoptic sampling events: Summer 2021 (Wet; July 8 to July 27), Fall 2021 (Wet; September 27 to October 7), Winter 2022 (Dry; January 3 to January 13), Spring 2022 (Dry; April 7 to April 23), and Summer 2022 (Wet; June 1 to June 13) to capture spatial and seasonal variation in stream conditions (specific conductivity, water temperature, dissolved oxygen, pH). Filtered stream samples were analyzed for dissolved organic carbon concentration and characteristics, available in a separate dataset. These data were collected as part of the Carbon in Urban Rivers Biogeochemistry (CURB) Project. Detailed field data and site data are published separately and can be linked using the “curbid” and “synoptic_event” columns in each dataset.
Field data for seasonal synoptic sampling of 100 urban streams in Portland, Oregon (USA), 2023-2024
This dataset contains field measurements taken during water sampling from 100 urban stream locations in the greater Portland, Oregon (USA) metropolitan area. Field collection took place during four synoptic sampling events (July 2023, October 2023, January/February 2024, and May 2024) to capture spatial and seasonal variation in stream conditions (specific conductivity, water temperature, dissolved oxygen, pH, ORP). Filtered stream samples were analyzed for dissolved organic carbon concentration and characteristics, available in a separate dataset. These data were collected as part of the Carbon in Urban Rivers Biogeochemistry (CURB) Project. Detailed field data and site data are published separately and can be linked using the “curbid” and “synoptic_event” columns in each dataset.
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.