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1,577 results for “fall”
Fall 2017 plant monitoring survey -- biomass calculated from shoot height and flowering status of plants in permanent plots at GCE sampling sites 1-10
The biomass of plants surveyed in permanent plots at 10 GCE LTER sampling sites in October 2017 was estimated based on allometric relationships between biomass and shoot height and flowering status derived for each site, zone, and species in October 2002 and October 2008. Biomass was calculated for dominant species, including Spartina alterniflora, S. cynosuroides, Juncus roemerianus, and Zizaniopsis miliacea, as well as rarer species including Scirpus spp, Panicum spp. And Typha angustifolia. This data set is based on GCE plant monitoring survey data set PLT-GCEM-1711a, and allometric relationships were based on GCE data sets PLT-GCEM-0211b, PLT-GCEM-0711, and PLT-GCEM-2011.
Mollusc population abundance monitoring: Fall 2017 mid-marsh and creekbank infaunal and epifaunal mollusc abundance based on collections from GCE marsh, monitoring sites 1-10
This data set is the Fall 2017 estimate of infaunal and epifaunal mollusc abundance at the GCE-LTER marsh sites used for population monitoring. Species abundance was determined by hand-collecting all the infaunal and epifaunal molluscs from within quadrats of known area in mid-marsh and creekbank zones (n = 4 quadrats per zone) at all sites. The molluscs were returned to the lab, fixed in ethanol, transferred to and preserved in ethanol, counted and measured (size data is reported separately). The counts were converted to number per square meter. Gastropod species are listed first, followed by bivalve species. Size distribution data for these collections may be found in the GCE-LTER data set INV-GCEM-1807a.
Fall 2017 crab population monitoring: mid-marsh and creek bank abundance based on crab hole counts at GCE marsh, monitoring sites 1-10
This data set is the Fall 2017 estimate of crab densities at the GCE-LTER marsh sites used for population monitoring. Crab abundance was determined by counting the number of crab holes within a 625 cm^2 quadrat and converting the counts to number per square meter. Counts were made in the mid-marsh and creek bank zones (n = 4 per zone) at GCE sites 1 through 10. Note that this census method does not differentiate which species made a particular hole and therefore only estimates total burrowing crab abundance, potentially including species Uca pugnax, Uca minax, Uca pugilator, Armases cinereum, Eurytium limosum and Sesarma reticulatum. Crab holes that are not actively maintained are quickly covered by tidal activity and other sediment disturbances, therefore plugged holes were assumed to be unoccupied and excluded from the counts.
Fall 2018 plant monitoring survey -- shoot height and flowering status of plants in permanent plots at GCE sampling sites 1-10
A quadrat survey was conducted in October 2018 to measure the species and size distribution of plants at 10 GCE LTER sampling sites. The quadrats were established as permanent plots at GCE sampling sites in October 2000 by placing wooden stakes at random locations across two nominal zones at each site, designated based on marsh structure (creekbank and high marsh). New plots were added each year as necessary to replace those lost due to catastrophic wrack disturbance or creek bank erosion. The plots were visually surveyed and the species, shoot height, and flowering status was recorded individually for each shoot over 10 cm in height present in each plot. Observations from plots exhibiting signs of disturbance were noted in a separate data set (PLT-GCEM-1801). This survey will be repeated annually to assess changes in plant distribution and biomass in relation to environmental changes documented by other GCE LTER monitoring efforts.
Fall 2018 plant monitoring survey -- biomass calculated from shoot height and flowering status of plants in permanent plots at GCE sampling sites 1-10
The biomass of plants surveyed in permanent plots at 10 GCE LTER sampling sites in October 2018 was estimated based on allometric relationships between biomass and shoot height and flowering status derived for each site, zone, and species in October 2002 and October 2008. Biomass was calculated for dominant species, including Spartina alterniflora, S. cynosuroides, Juncus roemerianus, and Zizaniopsis miliacea, as well as rarer species including Scirpus spp, Panicum spp. And Typha angustifolia. This data set is based on GCE plant monitoring survey data set PLT-GCEM-1811a, and allometric relationships were based on GCE data sets PLT-GCEM-0211b, PLT-GCEM-0711, and PLT-GCEM-2011.
Fall 2018 crab population monitoring: mid-marsh and creek bank abundance based on crab hole counts at GCE marsh, monitoring sites 1-10
This data set is the Fall 2018 estimate of crab densities at the GCE-LTER marsh sites used for population monitoring. Crab abundance was determined by counting the number of crab holes within a 625 cm^2 quadrat and converting the counts to number per square meter. Counts were made in the mid-marsh and creek bank zones (n = 4 per zone) at GCE sites 1 through 10. Note that this census method does not differentiate which species made a particular hole and therefore only estimates total burrowing crab abundance, potentially including species Uca pugnax, Uca minax, Uca pugilator, Armases cinereum, Eurytium limosum and Sesarma reticulatum. Crab holes that are not actively maintained are quickly covered by tidal activity and other sediment disturbances, therefore plugged holes were assumed to be unoccupied and excluded from the counts.
Mollusc population abundance monitoring: Fall 2018 mid-marsh and creekbank infaunal and epifaunal mollusc abundance based on collections from GCE marsh, monitoring sites 1-10
This data set is the Fall 2018 estimate of infaunal and epifaunal mollusc abundance at the GCE-LTER marsh sites used for population monitoring. Species abundance was determined by hand-collecting all the infaunal and epifaunal molluscs from within quadrats of known area in mid-marsh and creekbank zones (n = 4 quadrats per zone) at all sites. The molluscs were returned to the lab, fixed in ethanol, transferred to and preserved in ethanol, counted and measured (size data is reported separately). The counts were converted to number per square meter. Gastropod species are listed first, followed by bivalve species. Size distribution data for these collections may be found in the GCE-LTER data set INV-GCEM-1907a.
Mollusc population size distribution monitoring: Fall 2018 mid-marsh and creekbank infaunal and epifaunal mollusc size distributions based on collections from GCE marsh monitoring sites 1-10
This data set is the Fall 2018 report of infaunal and epifaunal mollusc species size distributions at the GCE-LTER marsh sites used for population monitoring. Infaunal and epifaunal molluscs were hand-collected from within quadrats of known area from mid-marsh and creekbank zones (n = 4 quadrats per zone) at all sites. The molluscs were returned to the lab, preserved in ethanol, measured and counted (count data is reported separately). Length of each measurable individual was determined using calipers or an ocular micrometer mounted in a stereomicroscope. Species abundance and density data for these collections may be found in the GCE-LTER data set INV-GCEM-1907. Numbers of individuals of each species in the abundance data file may not correspond exactly to the numbers of individuals in the size data file because some individuals may not have been measureable.
Fall 2019 plant monitoring survey -- shoot height and flowering status of plants in permanent plots at GCE sampling sites 1-10
A quadrat survey was conducted in October 2019 to measure the species and size distribution of plants at 10 GCE LTER sampling sites. The quadrats were established as permanent plots at GCE sampling sites in October 2000 by placing wooden stakes at random locations across two nominal zones at each site, designated based on marsh structure (creekbank and high marsh). New plots were added each year as necessary to replace those lost due to catastrophic wrack disturbance or creek bank erosion. The plots were visually surveyed and the species, shoot height, and flowering status was recorded individually for each shoot over 10 cm in height present in each plot. Observations from plots exhibiting signs of disturbance were noted in a separate data set (PLT-GCEM-1801). This survey will be repeated annually to assess changes in plant distribution and biomass in relation to environmental changes documented by other GCE LTER monitoring efforts.
Fall 2019 plant monitoring survey -- biomass calculated from shoot height and flowering status of plants in permanent plots at GCE sampling sites 1-10
The biomass of plants surveyed in permanent plots at 10 GCE LTER sampling sites in October 2019 was estimated based on allometric relationships between biomass and shoot height and flowering status derived for each site, zone, and species in October 2002 and October 2008. Biomass was calculated for dominant species, including Spartina alterniflora, S. cynosuroides, Juncus roemerianus, and Zizaniopsis miliacea, as well as rarer species including Scirpus spp, Panicum spp. And Typha angustifolia. This data set is based on GCE plant monitoring survey data set PLT-GCEM-1911a, and allometric relationships were based on GCE data sets PLT-GCEM-0211b, PLT-GCEM-0711, and PLT-GCEM-2011.
Fall 2019 crab population monitoring: mid-marsh and creek bank abundance based on crab hole counts at GCE marsh, monitoring sites 1-10
This data set is the Fall 2019 estimate of crab densities at the GCE-LTER marsh sites used for population monitoring. Crab abundance was determined by counting the number of crab holes within a 625 cm^2 quadrat and converting the counts to number per square meter. Counts were made in the mid-marsh and creek bank zones (n = 4 per zone) at GCE sites 1 through 10. Note that this census method does not differentiate which species made a particular hole and therefore only estimates total burrowing crab abundance, potentially including species Uca pugnax, Uca minax, Uca pugilator, Armases cinereum, Eurytium limosum and Sesarma reticulatum. Crab holes that are not actively maintained are quickly covered by tidal activity and other sediment disturbances, therefore plugged holes were assumed to be unoccupied and excluded from the counts.
Mollusc population abundance monitoring: Fall 2019 mid-marsh and creekbank infaunal and epifaunal mollusc abundance based on collections from GCE marsh, monitoring sites 1-10
This data set is the Fall 2019 estimate of infaunal and epifaunal mollusc abundance at the GCE-LTER marsh sites used for population monitoring. Species abundance was determined by hand-collecting all the infaunal and epifaunal molluscs from within quadrats of known area in mid-marsh and creekbank zones (n = 4 quadrats per zone) at all sites. The molluscs were returned to the lab, fixed in ethanol, transferred to and preserved in ethanol, counted and measured (size data is reported separately). The counts were converted to number per square meter. Gastropod species are listed first, followed by bivalve species. Size distribution data for these collections may be found in the GCE-LTER data set INV-GCEM-2007a.
Mollusc population size distribution monitoring: Fall 2019 mid-marsh and creekbank infaunal and epifaunal mollusc size distributions based on collections from GCE marsh monitoring sites 1-10
This data set is the Fall 2019 report of infaunal and epifaunal mollusc species size distributions at the GCE-LTER marsh sites used for population monitoring. Infaunal and epifaunal molluscs were hand-collected from within quadrats of known area from mid-marsh and creekbank zones (n = 4 quadrats per zone) at all sites. The molluscs were returned to the lab, preserved in ethanol, measured and counted (count data is reported separately). Length of each measurable individual was determined using calipers or an ocular micrometer mounted in a stereomicroscope. Species abundance and density data for these collections may be found in the GCE-LTER data set INV-GCEM-2007. Numbers of individuals of each species in the abundance data file may not correspond exactly to the numbers of individuals in the size data file because some individuals may not have been measureable.
Fall 2020 grasshopper monitoring -- mid-marsh grasshopper abundance and species diversity at eight GCE LTER sampling sites
Grasshopper abundance and species diversity were investigated at eight sampling sites within the Georgia Coastal Ecosystems (GCE) LTER study area in July 2020. Visual surveys were conducted along 8 2m by 10m transects randomly allocated within the mid-marsh zone at each site. All grasshoppers observed within each transect were counted and identified to species, if possible. This survey was conducted as part of the GCE invertebrate monitoring program, and will be performed annually to assess long-term changes in relative species abundances across the GCE study area.
Fall 2021 grasshopper monitoring -- mid-marsh grasshopper abundance and species diversity at eight GCE LTER sampling sites
Grasshopper abundance and species diversity were investigated at eight sampling sites within the Georgia Coastal Ecosystems (GCE) LTER study area in August 2021. Visual surveys were conducted along 8 2m by 10m transects randomly allocated within the mid-marsh zone at each site. All grasshoppers observed within each transect were counted and identified to species, if possible. This survey was conducted as part of the GCE invertebrate monitoring program, and will be performed annually to assess long-term changes in relative species abundances across the GCE study area.
Dataset for: Quantifying the rise and fall of scientific fields
<p>This dataset supports the paper:</p> <p><br><strong>Singh CK, Barme E, Ward R, Tupikina L, Santolini M (2022)</strong><br><em>Quantifying the rise and fall of scientific fields</em>.<br><em>PLOS ONE</em> 17(6): e0270131. <a href="https://doi.org/10.1371/journal.pone.0270131" target="_new" rel="noopener">https://doi.org/10.1371/journal.pone.0270131</a></p> <p>It provides the processed metadata and relational mappings derived from the arXiv preprint repository used to quantify the temporal dynamics of 175 scientific fields across Physics, Mathematics, Computer Science, Quantitative Biology, and Quantitative Finance.</p> <h2>Files Information</h2> <h3><code>arXiv_data_with_Rescaled_times.csv</code></h3> <p>CSV file containing article-level features used for analyzing field evolution, including rescaled time variables derived from Gumbel distribution fits.</p> <p>Each row represents an article and includes the following columns:</p> <ul> <li> <p><strong>id</strong>: arXiv ID</p> </li> <li> <p><strong>categories</strong>: arXiv field tags (e.g., 'hep-th')</p> </li> <li> <p><strong>doi</strong>: DOI if the article has been published</p> </li> <li> <p><strong>created</strong>: Date of first submission to arXiv</p> </li> <li> <p><strong>authors</strong>: List of last names of authors</p> </li> <li> <p><strong>authors_orcid</strong>: ORCID ID of authors wherever possible </p> </li> <li> <p><strong>NumCitationsArxiv</strong>: Number of arXiv articles citing this article</p> </li> <li> <p><strong>NumReferencesArxiv</strong>: Number of arXiv articles this article cites</p> </li> <li> <p><strong>year</strong>: Year of submission</p> </li> <li> <p><strong>Rescaled Times</strong>: Rescaled time values based on Gumbel parameters per field</p> </li> <li> <p><strong>Min RT</strong>: Minimum rescaled time across the article’s fields</p> </li> </ul> <h3><code>article_metadata.tsv</code></h3> <p>Tab-separated file with supplementary metadata parsed from arXiv and journal records:</p> <ul> <li> <p><strong>id</strong>: arXiv ID (e.g., "0704.0001")</p> </li> <li> <p><strong>journal.ref</strong>: Journal reference string (if published)</p> </li> <li> <p><strong>doi</strong>: DOI of published version (if available)</p> </li> <li> <p><strong>num.versions</strong>: Number of arXiv versions submitted</p> </li> <li> <p><strong>num.pages</strong>: Estimated number of pages (from PDF parsing, may be NA)</p> </li> <li> <p><strong>num.figures</strong>: Estimated number of figures (from PDF parsing, may be NA)</p> </li> </ul> <h3><code>orcid_ids_to_articles.json</code></h3> <p>JSON list of triples associating author ORCID IDs to arXiv article IDs, allowing disambiguation of authors.</p> <p>Each entry links a disambiguated author (via ORCID) to an arXiv preprint, in the form:</p> <div> <div dir="ltr"><code>{</code><br><code> "certainty": 1, </code></div> <div dir="ltr"><code> "predicate": "is_author_of", </code></div> <div dir="ltr"><code> "subject": { </code><br><code> "@id": "http://orcid.org/0000-0001-5000-1018", </code></div> <div dir="ltr"><code> "type": "ORCID_iD", </code><br><code> "value": "0000-0001-5000-1018" </code><br><code> }, </code></div> <div dir="ltr"><code> "object": { </code></div> <div dir="ltr"><code> "@id": "http://arxiv.org/art/1902.00500", </code></div> <div dir="ltr"><code> "type": "arXiv_article", </code><br><code> "value": "1902.00500" </code><br><code> } </code><br><code>} </code></div> </div> <p>Used for author trajectory reconstruction across fields.</p> <h3><code>internal-citations.json</code></h3> <p>JSON dictionary representing the <strong>arXiv internal citation network</strong>.</p> <ul> <li> <p><strong>Keys</strong>: arXiv IDs of citing articles</p> </li> <li> <p><strong>Values</strong>: Lists of arXiv IDs cited by the article</p> </li> </ul> <p>Example:</p> <div> <div dir="ltr"><code>{ <br> "hep-lat/0403005": ["nucl-th/9911018", "hep-ph/9808398", ...], <br> "hep-lat/0403014": ["hep-lat/0310012", "hep-lat/9702016", ...] <br>} </code></div> </div> <p>This file enables construction of the citation network used to compute the <strong>Disruptive Index</strong> and other bibliometric indicators.</p> <h2>Citation</h2> <p>Please cite the original article if using this dataset:</p> <blockquote> <p>Singh CK, Barme E, Ward R, Tupikina L, Santolini M (2022)<br><em>Quantifying the rise and fall of scientific fields</em>.<br><em>PLOS ONE</em> 17(6): e0270131. <a href="https://doi.org/10.1371/journal.pone.0270131" target="_new" rel="noopener">https://doi.org/10.1371/journal.pone.0270131</a></p> </blockquote> <p>Also cite the dataset itself:</p> <blockquote>Chakresh Kumar Singh, Emma Barme, Robert Ward, Liubov Tupikina, & Marc Santolini. (2022). <br><em>Dataset for: Quantifying the rise and fall of scientific fields (Version v2) [Data set].</em> <br>Zenodo. <a href="https://doi.org/10.5281/zenodo.16738242" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.16738242</a></blockquote> <h2>License</h2> <p>This dataset is licensed under the <strong>Creative Commons Attribution 4.0 International License (CC BY 4.0)</strong>.<br>You are free to use, share, and adapt the materials with proper attribution.</p>
MASCDB, a database of images, descriptors and microphysical properties of individual snowflakes in free fall
<p><strong>Dataset overview</strong></p> <p>This dataset provides data and images of snowflakes in free fall collected with a <a href="https://amt.copernicus.org/articles/5/2625/2012/">Multi-Angle Snowflake Camera (MASC)</a> The dataset includes, for each recorded snowflakes:</p> <ol> <li>A triplet of gray-scale images corresponding to the three cameras of the MASC</li> <li>A large quantity of geometrical, textural descriptors and the pre-compiled output of published retrieval algorithms as well as basic environmental information at the location and time of each measurement.</li> </ol> <p>The pre-computed descriptors and retrievals are available either individually for each camera view or, some of them, available as descriptors of the triplet as a whole. A non exhaustive list of precomputed quantities includes for example:</p> <ul> <li>Textural and geometrical descriptors as in <a href="https://amt.copernicus.org/articles/10/1335/2017/"><em>Praz et al 2017</em></a></li> <li>Hydrometeor classification, riming degree estimation, melting identification, as in <a href="https://amt.copernicus.org/articles/10/1335/2017/"><em>Praz et al 2017</em></a></li> <li>Blowing snow identification, as in <a href="https://tc.copernicus.org/articles/14/367/2020/"><em>Schaer et al 2020 </em></a></li> <li>Mass, volume, gyration estimation<em>, as in <a href="https://amt.copernicus.org/preprints/amt-2021-176/">Leinonen et al 2021</a></em></li> </ul> <p><strong>Data format and structure</strong></p> <p>The dataset is divided into four <em>.parquet</em> file (for scalar descriptors) and a <em>Zarr</em> database (for the images). A detailed description of the data content and of the data records is available <a href="https://pymascdb.readthedocs.io/en/latest/data.html#data">here</a>.</p> <p><strong>Supporting code</strong></p> <p>A python-based API is available to manipulate, display and organize the data of our dataset. It can be found on <a href="https://github.com/ltelab/pymascdb">GitHub</a>. See also the code documentation on <a href="https://pymascdb.readthedocs.io/en/latest/index.html">ReadTheDocs</a>.</p> <p><strong>Download notes</strong></p> <ul> <li>All files available here for download should be stored in the same folder, if the python-based API is used</li> <li><em>MASCdb.zarr.zip</em> must be unzipped after download</li> </ul> <p><strong>Field campaigns</strong></p> <p>A list of campaigns included in the dataset, with a minimal description is given in the following table</p> <table> <tbody> <tr> <td><strong>Campaign_name</strong></td> <td><strong>Information</strong></td> <td> <p><strong>Shielded / Not shielded</strong></p> <p><em>DFIR = Double Fence Intercomparison Reference</em></p> </td> </tr> <tr> <td> <p><em>APRES3-2016 & APRES3-2017</em></p> </td> <td>Installed in Antarctica in the context of the APRES3 project. See for example <a href="https://essd.copernicus.org/articles/10/1605/2018/essd-10-1605-2018.html">Genthon et al, 2018</a> or <a href="https://tc.copernicus.org/articles/11/1797/2017/">Grazioli et al 2017</a></td> <td>Not shielded</td> </tr> <tr> <td><em>Davos-2015</em></td> <td>Installed in the Swiss Alps within the context of <a href="https://public.wmo.int/en/resources/meteoworld/spice-%E2%80%93-improving-snowfall-measurements">SPICE</a> (Solid Precipitation InterComparison Experiment)</td> <td>Shielded (DFIR)</td> </tr> <tr> <td><em>Davos-2019</em></td> <td>Installed in the Swiss Alps within the context of <a href="https://www.envidat.ch/group/about/raclets-field-campaign">RACLETS</a> (<em>Role of Aerosols and CLouds Enhanced by Topography on Snow</em>)</td> <td>Not shielded</td> </tr> <tr> <td><em>ICEGENESIS-2021</em></td> <td>Installed in the Swiss Jura in a MeteoSwiss ground measurement site, within the context of ICE-GENESIS. See for example <a href="https://doi.org/10.1175/BAMS-D-21-0184.1">Billault-Roux et al, 2023</a></td> <td>Not shielded</td> </tr> <tr> <td><em>ICEPOP-2018</em></td> <td>Installed in Korea, in the context of ICEPOP. See for example <a href="https://doi.org/10.5194/essd-13-417-2021">Gehring et al 2021</a>.</td> <td>Shielded (DFIR)</td> </tr> <tr> <td><em>Jura-2019 & Jura-2023</em></td> <td>Installed in the Swiss Jura within a MeteoSwiss measurement site</td> <td>Not shielded</td> </tr> <tr> <td><em>Norway-2016</em></td> <td>Installed in Norway during the High-Latitude Measurement of Snowfall (HiLaMS). See for example <a href="https://doi.org/10.1175/BAMS-D-21-0007.1">Cooper et al, 2022</a>.</td> <td>Not shielded</td> </tr> <tr> <td><em>PLATO-2019</em></td> <td>Installed in the "Davis" Antarctic base during the <a href="https://www.osti.gov/biblio/1524773">PLATO</a> field campaign</td> <td>Not shielded</td> </tr> <tr> <td><em>POPE-2020</em></td> <td>Installed in the "Princess Elizabeth Antarctica" base during the POPE campaign. See for example <a href="https://essd.copernicus.org/articles/15/1115/2023/essd-15-1115-2023.html">Ferrone et al, 2023</a>.</td> <td>Not shielded</td> </tr> <tr> <td><em>Remoray-2022</em></td> <td>Installed in the French Jura.</td> <td>Not shielded</td> </tr> <tr> <td><em>Valais-2016</em></td> <td>Installed in the Swiss Alps in a ski resort.</td> <td>Not shielded</td> </tr> <tr> <td>ISLAS-2022</td> <td>Installed in Norway during the <a href="https://www.uib.no/en/rg/meten/150202/islas2022-field-campaign">ISLAS campaign</a></td> <td>Not shielded</td> </tr> <tr> <td>Norway-2023</td> <td>Installed in Norway during the MC2-ICEPACKS campaign</td> <td>Not shielded</td> </tr> </tbody> </table> <p> </p> <p><strong>Version</strong></p> <p>1.1 - Two new campaigns ("ISLAS-2022", "Norway-2023") added.</p> <p>1.0 - Two new campaigns ("Jura-2023", "Norway-2016") added. Added references and list of campaigns.</p> <p>0.3 - a new campaign is added to the dataset ("Remoray-2022")</p> <p>0.2 - rename of variables. Variable precision (digits) standardized</p> <p>0.1 - first upload</p>
Interagency Ecological Program: Zooplankton and water quality data in the San Francisco Estuary collected by the Summer Townet and Fall Midwater Trawl monitoring programs.
The Interagency Ecological Program’s (IEP) Summer Townet Survey (STN) and Fall Midwater Trawl (FMWT) are two long-term monitoring projects conducted by the California Department of Fish and Wildlife (CDFW) to monitor fish abundance and distribution trends in the San Francisco Estuary (SFE) since 1959 and 1967, respectively. Starting in 2005, zooplankton monitoring was added and paired with fish tows to investigate food availability for young fishes. Food limitation has been a long-term issue and a focus of the Pelagic Organism Decline (POD) studies that began in 2005. By 2011, STN routinely conducted zooplankton monitoring at 40 stations, and FMWT at 32 stations in the upper SFE from Carquinez Strait to the Sacramento Deep Water Ship Channel and into the South Delta. STN samples every other week from June to August and FMWT samples once monthly from September to December. Both projects collect mesozooplankton samples using a modified Clarke-Bumpus (CB) net to target copepods and cladocerans, and FMWT also samples macrozooplankton (i.e. mysids and amphipods) using a mysid net. Flowmeters are used to measure the volume sampled to determine zooplankton catch per unit effort. Environmental variables such as water temperature, turbidity, secchi, and electrical conductivity are collected with each zooplankton sample. Concurrent fish and zooplankton tows conducted by STN and FMWT have allowed for comparisons of fish diet to the available zooplankton prey at the time of collection.
General Lake Model-Aquatic EcoDynamics model parameter set for Falling Creek Reservoir, Vinton, Virginia, USA 2013-2019
The General Lake Model (GLM), an open-source, one-dimensional hydrodynamic model, was used to simulate physical, chemical, and biological variables in Falling Creek Reservoir, Vinton, Virginia, USA between 15 May 2013 and 31 December 2019. GLM (v.3.2.0a3) was coupled to the Aquatic EcoDynamics (AED) module library via the Framework for Aquatic Biogeochemical Modeling (FABM). GLM-AED requires three configuration files to run the model. First, the glm3.nml file configures lake metadata (including hypsometry), meteorological driver data, stream inflow and outflow driver data files, and physical response variables (mixing parameters and sediment heat zones). Second, the aed2_20220111_2DOCpools.nml file configures various biogeochemical modules for the simulation of oxygen, carbon, silica, nitrogen, phosphorus, organic matter, and phytoplankton. Third, the aed2_phyto_pars_4Jan2022.nml file configures all parameters pertaining to phytoplankton dynamics. Meteorological data, two surface stream inflow files, a submerged oxygenation inflow file, and outflow file used in this calibration are also included.
Long-term species-level measurements of fall season aboveground net primary production in the Monsoon Rainfall Manipulation Experiment (MRME), Sevilleta National Wildlife Refuge, New Mexico, USA
Anticipated intensification of the North American Monsoon in the southwestern United States is predicted to shift growing season rainfall patterns, historically characterized by frequent small rain events, to a more extreme precipitation regime consisting of fewer, but larger rain events. Atmospheric nitrogen deposition is also increasing throughout this dryland region as a result of anthropogenic activities. Alterations in rainfall size and frequency, along with changes in nitrogen availability, are likely to have significant consequences for aboveground net primary production (ANPP) and plant community dynamics in drylands, where ecological processes are limited by water and nitrogen availability. This data package accompanies an associated manuscript in which we used fourteen years (2007-2020) of growing season ANPP measurements from the long-term Monsoon Rainfall Manipulation Experiment (MRME), located in the Sevilleta National Wildlife Refuge, to investigate how changes in rainfall regimes, along with chronic nitrogen enrichment, impact ANPP in a northern Chihuahuan Desert grassland.
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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.