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53 results for “environmental science”
Spatial variability in water chemistry of four Wisconsin aquatic ecosystems - High speed limnology Environmental Science and Technology datasets
Advanced sensor technology is widely used in aquatic monitoring and research. Most applications focus on temporal variability, whereas spatial variability has been challenging to document. We assess the capability of water chemistry sensors embedded in a high-speed water intake system to document spatial variability. We developed a new sensor platform to continuously samples surface water at a range of speeds (0 to > 45 km hr-1) resulting in high-density, meso-scale spatial data. Here, we archive data associated with an Environmental Science and Technology publication. Data include a single spatial survey of the following aquatic ecosystems: Lake Mendota, Allequash Creek, Pool 8 of the Upper Mississippi River, and Trout Bog. Data have been provided in three formats (raw, hydraulic-corrected, and tau-corrected).
Optimizing laboratory cultures of <i>Gammarus fossarum</i> (Crustacea: Amphipoda) as a study organism in environmental sciences and ecotoxicology
<p>Supplemental code and data for Alther, Krähenbühl, Bucher & Altermatt (2022) 'Optimizing laboratory cultures of <em>Gammarus fossarum</em> (Crustacea: Amphipoda) as a study organism in environmental sciences and ecotoxicology' (DOI: 10.1016/j.scitotenv.2022.158730). The repository folder contains three text files and a corresponding R script.</p> <p>Rerunning the analysis and producing figures requires two raw data files: LabdataAK_v6_210616_Daylength_input.txt and Nutrition_Exp_KaplanMeier_v1_input.txt. In order to reproduce the analysis and figures, run 'AmphipodHusbandry_20220919.R'. Make sure that your working directory is the folder containing all data files, easily achieved by (re)starting R (or R Studio) by double-clicking the R script file in the folder. The analysis script will produce all the figures from the paper, organized in a folder 'Results' and a subfolder 'Supplement'. Figures are prepared as pixel graphics (PNG).</p> <p>The R script was tested in R ver. 4.1.1 (Windows 10, version 21H1), 4.1.3 (macOS 11.6), and 4.2.0 (Ubuntu 22.04. Required packages are survival (version 3.2-13 worked), survminer (version 0.4.9 worked), and vioplot (version 0.3.7 worked).</p>
LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe – files
<p><strong>Version 1.0 - This version is the final revised one.</strong></p> <p>This is the LamaH-CE dataset accompanying the paper: Klingler et al., LamaH-CE | LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe, published at Earth System Science Data (ESSD), 2021 (<a href="https://doi.org/10.5194/essd-13-4529-2021">https://doi.org/10.5194/essd-13-4529-2021</a>).</p> <p>LamaH-CE contains a collection of runoff and meteorological time series as well as various (catchment) attributes for 859 gauged basins. The hydrometeorological time series are provided with daily and hourly time resolution including quality flags. All meteorological and the majority of runoff time series cover a span of over 35 years, which enables long-term analyses with high temporal resolution.<br> LamaH is in its basics quite sililar to the well-known CAMELS datasets for the contiguous United States (<a href="https://doi.org/10.5194/hess-21-5293-2017">https://doi.org/10.5194/hess-21-5293-2017</a>), Chile (<a href="https://doi.org/10.5194/hess-22-5817-2018">https://doi.org/10.5194/hess-22-5817-2018</a>), Brazil (<a href="https://doi.org/10.5194/essd-12-2075-2020">https://doi.org/10.5194/essd-12-2075-2020</a>), Great Britain (<a href="https://doi.org/10.5194/essd-12-2459-2020">https://doi.org/10.5194/essd-12-2459-2020</a>) and Australia (<a href="https://doi.org/10.5194/essd-13-3847-2021">https://doi.org/10.5194/essd-13-3847-2021</a>), but new features like additional basin delineations (intermediate catchments) and attributes allow to consider the hydrological network and river topology in further applications.</p> <p>We provide two different files to download: 1) Hydrometeorological time series with daily and hourly resolution, which requires decompressed about 70 GB of free disk space. 2) Hydrometeorological time series only with daily resolution, which requires 5 GB. Beyond the temporal resolution of the time series, there are no differences.</p> <p><strong>Note: </strong>It is recommended to read the supplementary info file before using the dataset. For example, it clarifies the time conventions and that <strong>NAs</strong> are indicated by the number<strong> -999</strong> in the <strong>runoff time series</strong>.</p> <p><strong>Disclaimer:</strong> We have created LamaH with care and checked the outputs for plausibility. By downloading the dataset, you agree that we nor the provider of the used source datasets (e.g. runoff time series) cannot be liable for the data provided. The runoff time series of the German federal states Bavaria and Baden-Württemberg are retrospective checked and updated by the hydrographic services. Therefore, it might be appropriate to obtain more up-to-date runoff data from Bavaria (<a href="https://www.gkd.bayern.de/en/rivers/discharge/tables">https://www.gkd.bayern.de/en/rivers/discharge/tables</a>) and Baden-Württemberg (<a href="https://udo.lubw.baden-wuerttemberg.de/public/p/pegel_messwerte_leer">https://udo.lubw.baden-wuerttemberg.de/public/p/pegel_messwerte_leer</a>). Runoff data from the Czech Republic may not be used to set up operational warning systems (<a href="https://www.chmi.cz/files/portal/docs/hydro/denni_data/Podminky_uziti.pdf">https://www.chmi.cz/files/portal/docs/hydro/denni_data/Podminky_uziti.pdf</a>).</p> <p><strong>License: </strong>This work is licensed with CC BY-SA 4.0 (<a href="https://creativecommons.org/licenses/by-sa/4.0/">https://creativecommons.org/licenses/by-sa/4.0/</a>). This means that you may freely use and modify the data (even for commercial purposes). But you have to give appropriate credit (associated ESSD paper, version of dataset and all sources which are declared in the folder "Info"), indicate if and what changes were made and distribute your work under the same public license as the original.</p> <p><strong>Additional references: </strong>We ask kindly for compliance in citing the following references when using LamaH, as an agreement to cite was usually a condition of sharing the data: BAFU (2020), CHMI (2020), GKD (2020), HZB (2020), LUBW (2020), BMLFUW (2013), Broxton et al. (2014), CORINE (2012), EEA (2019), ESDB (2004), Farr et al. (2007), Friedl and Sulla-Menashe (2019), Gleeson et al. (2014), HAO (2007), Hartmann and Moosdorf (2012), Hiederer (2013a, b), Linke et al. (2019), Muñoz Sabater et al. (2021), Muñoz Sabater (2019a), Myneni et al. (2015), Pelletier et al. (2016), Toth et al. (2017), Trabucco and Zomer (2019), and Vermote (2015). These references are listed in detail in the accompanying <a href="https://doi.org/10.5194/essd-13-4529-2021">paper</a>.</p> <p><strong>Supplements: </strong>We have created additional files after publication (therefore non peer-reviewed):<br> 1) Shapefiles for reservoirs (points) and cross-basin water transfers (lines) including several attributes as well as tables with information about the accumulated storage volume and effective catchment area (considerung artificial in- and outflows) for every runoff gauge.<br> 2) Water quality data (e.g. dissolved oxygen, water temperature, conductivity, NO3-N), which are suitable to the gauges. The data for water quality may not be used for commercial purposes.<br> If you are interessted, just send us an email with your name, affiliation and the intended purpose for the requested files to the address listed below. If you find any errors in the dataset, feel free to send us an email to: christoph.klingler@boku.ac.at</p>
Course Materials for Environmental Data Science in R: Introduction to Data Integration and Machine Learning (ENV 730)
In today's world, understanding environmental data and making informed decisions based on it is crucial for addressing complex environmental challenges. Yale School of the Environment's Environmental Data Science in R: Introduction to Data Integration and Machine Learning (ENV 730) course serves as an introduction to the integration of environmental data using R programming language, coupled with machine learning techniques. This dataset contains a zip file with all the data files used in this course, along with a README that has the metadata for those files.
Multiscale continuum figures from Tratnyek et al. (2017) "In silico environmental chemical science: Properties and processes from statistical and computational modelling"
<p>Accessible versions of selected figures from Tratnyek et al. (2017) "In silico environmental chemical science: Properties and processes from statistical and computational modelling" Environ. Sci. Processes Impacts 19(3): 188-202. DOI: 10.1039/C7EM00053G.</p> <p>The Abstract Art figure shows a classification of variables for predictive/diagnostic models used in silico environmental chemical science, in terms of system scales and variable types. Figure 3 shows a continuum of system scales encompassing the whole scope of predictive/diagnostic modelling for in silico environmental chemical sciences, juxtaposing earth and biological scales.</p> <p>The published version of Figure 3 is tall, for two-column page-layouts, but a wide version of Figure 3 is provided for landscape oriented formats. The 300 dpi versions of each figure should be adequate resolution for most purposes, and therefore are recommended. The large versions of the figures may take significant time to download, but may be useful for high resolution applications.</p> <p>This work is from the perspectives/review paper at the beginning of a themed issue on "Quantitative Structure-Activity Relationships (QSARs) and Computational Chemistry Methods in the Environmental Chemical Sciences", published in the March 2017 issue of the Royal Society of Chemistry journal Environmental Sciences: Process and Impacts. The whole collection of papers can be accessed at rsc.li/qsars.</p>
Data for: Luo et al., Expiratory aerosol pH: the overlooked driver of airborne virus inactivation, Environmental Science and Technology, 10.1021/acs.est.2c05777
<p><strong>Experimental data </strong></p> <p>This folder contains the experimental data to the figures shown in the main manuscript and Supporting Information.</p> <p>Figures 1 and S3 (inactivation curves for IAV, SARS-CoV-2 and HCoV-229E)</p> <p>Figure 1 (rate constants)</p> <p>Figure 2 (EDB analysis of SLF)</p> <p>Figure S1A (zetasizer analysis to measure virus aggregation)</p> <p>Figure S1B (renilla and plaque assay data for viruses exposed to pH 5, 6 and 7)</p> <p>Figure S4A (EDB analysis of different SLF samples; raw data)</p> <p>Figure S4Amean (EDB analysis of different SLF samples; mean values)</p> <p>Figure S5 (EDB analysis of nasal mucus)</p> <p>Figure S8 (EDB analysis of slow crystal growth stage of SLF and nasal mucus)</p> <p>Figure S13 and S14 (literature data on inactivation of IAV and SARS-CoV-2 in aerosol particles)</p> <p> </p> <p> </p>
Lake ice surveys, 1874-2022, Adirondack Long-Term Ecological Monitoring Program Project No. 8 by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York. Environmental Data Initiative.
The objective of this dataset is to document ice-in and ice-out dates on several lakes on the State University of New York College of Environmental Science and Forestry's Huntington Wildlife Forest (HWF). Lakes include: Arbutus, Catlin, Deer, Military, Rich, Wolf and Lodo Pond; some records exist for Long Pond and other water bodies but they are not included here except in some comment fields.
Return on Investment Metrics for Data Repositories in Earth and Environmental Sciences
Despite a growing recognition of the importance of data to the economy and to science, investment in repositories to manage and disseminate that data in easily accessible and understandable ways is scarce. Keeping repository services active and up-to-date for a long time period is difficult due to this funding situation. As a result, repositories must continually provide proof of their value, their Return on Investment (ROI) to their sponsors; yet doing so has always been difficult, problematic and not always successful. In this work, an analysis of approaches for assessing the ROI of several scientific data repositories has identified various techniques that repositories use to report on the impact and value of their data products and services. A survey of selected repositories rated the set of metrics identified and rated each by its importance as well as the ease with which the metric could be measured. The discussion is broken down into considerations for calculating costs, perceived value of repositories and suggested metrics that would allow a repository to calculate an ROI. The authors, representatives of environmental data repositories, concluded that easily obtainable data use metrics, such as data downloads, etc., have limited value while more informative analyses would require additional resources.
Illustrations from the Environmental Data Science Book: Shared under CC-BY 4.0 for reuse
<p>Illustrations as part of the <em>Environmental Data Science</em> book.</p> <p>When using any of the images, please include the following attribution with the specific DOI as listed on the particular Zenodo page:</p> <blockquote> <p>This illustration is created by Scriberia with The Turing Way community. Used under a CC-BY 4.0 licence. DOI: <a href="https://doi.org/10.5281/zenodo.7030142">10.5281/zenodo.7030142</a></p> </blockquote> <p>When using any of the images, please include the following attribution with the specific DOI as listed on the particular Zenodo page:</p> <p>You can cite all versions by using the DOI <a href="https://doi.org/10.5281/zenodo.7030142">10.5281/zenodo.7030142</a>. This DOI represents all versions, and will always resolve to the latest one.</p> <p><em>This work was supported by Wave 1 of The UKRI Strategic Priorities Fund under the EPSRC Grant EP/W006022/1, particularly the Environment & Sustainability theme within that grant & The Alan Turing Institute.</em></p>
Agriculture - Environmental sciences 3
<p>Original data comes from a project which takes or took place as part of the DFG priority program “Exploratories for large-scale and long-term functional biodiversity research”. The data is stored together with descriptive metadata, in combination called a dataset, in the project repository (<a href="https://www.bexis.uni-jena.de">https://www.bexis.uni-jena.de</a>). Species information was extracted from that original dataset. The second paragraph is part of the metadata of the original dataset. Bryophytes confirm a species rich plant group in ecosystems such as forests. We assessed bryophyte species diversity in relationship to forest-management types on 1050 forest plots.</p> <p>Müller J (2016). Bryophyte diversity in forests. Biodiversity Exploratories. Occurrence dataset <a href="https://doi.org/10.15468/svdnib">https://doi.org/10.15468/svdnib</a> accessed via GBIF.org</p>
Agriculture - Environmental sciences
<p>Original data comes from a project which takes or took place as part of the DFG priority program “Exploratories for large-scale and long-term functional biodiversity research”. The data is stored together with descriptive metadata, in combination called a dataset, in the project repository (<a href="https://www.bexis.uni-jena.de">https://www.bexis.uni-jena.de</a>). Species information was extracted from that original dataset. The second paragraph is part of the metadata of the original dataset."Sammelarten" sind durch agg= Aggregate oder total gekennzeichnet. Sie umfassen jene Taxa, die teilweise bis zur Kleinart, Subspecies oder Varietas bestimmt wurden, teilweise aber auch nur auf Gattungs- (… spec. (indet.)) oder Artebene. Taxonomie nach Wisskirchen,Haeupler (1998)Standardliste für Deutschland. Lebensformen nach Raunkiaer, C. (1910): Statistik der Lebensformen als Grundlage für die biologische Pflanzengeographie.-Beih. Biol. Cbl. 27 II, 170 - 206d. Rote Liste: KORNECK, D., SCHNITTLER, M., VOLLMER, I. (1996): Rote Liste der Farn- und Blütenpflanzen (Pteridophyta et Spermatophyta) Deutschlands. – LUDWIG, G., SCHNITTLER, M. [Hrsg.]: Rote Listen gefährdeter Pflanzen Deutschlands. – Schriftenr. Vegetationskd. 28: 21–187, Bundesamt für Naturschutz, Bonn.’ Socher S (2016). vegetation releves on grassland gridplots in 2007. Biodiversity Exploratories. Occurrence dataset <a href="https://doi.org/10.15468/vhxdeu">https://doi.org/10.15468/vhxdeu</a> accessed via GBIF.org</p>
Agriculture - Environmental sciences 2
<p>Original data comes from a project which takes or took place as part of the DFG priority program “Exploratories for large-scale and long-term functional biodiversity research”. The data is stored together with descriptive metadata, in combination called a dataset, in the project repository (<a href="https://www.bexis.uni-jena.de">https://www.bexis.uni-jena.de</a>). Species information was extracted from that original dataset. The second paragraph is part of the metadata of the original dataset.Vegetation relevés of all 400 m² GPs in forests.</p> <p>Boch S (2016). Vascular plant diversity in forests. Biodiversity Exploratories. Occurrence dataset <a href="https://doi.org/10.15468/ckaefy">https://doi.org/10.15468/ckaefy</a> accessed via GBIF.org</p>
Linked collectors and determiners for: Estonian University of Life Sciences Institute of Agricultural and Environmental Sciences Mycological Herbarium.
Natural history specimen data linked to collectors and determiners held within, "Estonian University of Life Sciences Institute of Agricultural and Environmental Sciences Mycological Herbarium". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/1f917113-dd55-4000-9f80-6266fab1af03">https://bionomia.net/dataset/1f917113-dd55-4000-9f80-6266fab1af03</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/1f917113-dd55-4000-9f80-6266fab1af03">https://gbif.org/dataset/1f917113-dd55-4000-9f80-6266fab1af03</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Estonian University of Life Sciences Institute of Agricultural and Environmental Sciences Entomological Collection.
Natural history specimen data linked to collectors and determiners held within, "Estonian University of Life Sciences Institute of Agricultural and Environmental Sciences Entomological Collection". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/1af83152-24f7-4df7-afbc-b213b62175bb">https://bionomia.net/dataset/1af83152-24f7-4df7-afbc-b213b62175bb</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/1af83152-24f7-4df7-afbc-b213b62175bb">https://gbif.org/dataset/1af83152-24f7-4df7-afbc-b213b62175bb</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Estonian University of Life Sciences Institute of Agricultural and Environmental Sciences Department of Plant Protection.
Natural history specimen data linked to collectors and determiners held within, "Estonian University of Life Sciences Institute of Agricultural and Environmental Sciences Department of Plant Protection". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/91afc73f-a11a-4e8b-9313-ed056697cf05">https://bionomia.net/dataset/91afc73f-a11a-4e8b-9313-ed056697cf05</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/91afc73f-a11a-4e8b-9313-ed056697cf05">https://gbif.org/dataset/91afc73f-a11a-4e8b-9313-ed056697cf05</a>. Formatted as a Frictionless Data package.
Benefits and limitations of environmental magnetism for completing citizen science on air quality: a case study in a street canyon.
<p>Inside a street canyon in Montpellier (France) a total of 72 deposimeters were deployed in 29 households for a period of 3 months to measure local air quality. This street canyon was chosen because dwellers were already mobilized against the street traffic, and because they were in conflict on this issue with policy makers. The project aimed to include all the stakeholders through co-construction. The closure of the street during the metrological campaign and the absence of agreement curbed their involvement and motivation. However, the feedbacks from the citizen partners promote the fact that this study supported their claims and brought them a deeper understanding on the micro-scale air quality monitoring. Indeed, it is increasingly difficult for citizens, who seemed specifically interested in what is happening right outside their front door, to understand this measure with the emergence of ever more low-cost sensors. For that reason, we examined the citizen’s degree of confidence in magnetic monitoring of air quality and how can this technique be useful in their claims. The results show that magnetism can be a measurement technique favorable to citizen participation because it provides a large amount of data at the micro-scale of the street level, while the data from the certified associations for monitoring air quality requires a spatial interpolation to map variations on a neighborhood scale. In this study, we proposed a magnetic air quality index to standardize and democratize the magnetic monitoring of air quality to facilitate the dialogue with all stakeholders.</p>
Dataset for "Exposure and environmental engagement: A pilot integrating wearable sensors, air quality and citizen science"
<p>The dataset contains anonymised readings of 7 citizens taking air quality measurements using PlumeLabs Flow 2 monitor. Data is for Falmouth/Penryn, and Bristol and it was collected between January 26, 2022 and March 9, 2022.</p> <p>CSV file:</p> <ul> <li>latitude: unit degrees, positive values indicate North hemisphere.</li> <li>longitude, unit degrees, positive values indicate East.</li> <li>AQI: PlumeLabs' Air Quality Index.</li> <li>site: A refers to Falmouth/Penryn(UK), B refers to Bristol (UK).</li> <li>count: auxiliary variable that indicates that the record was comprised of a single reading.</li> </ul> <p>Jupyter notebook: The air quality analysis was conducted with Python 3.9.16 alongside numpy 1.24.3, pandas 2.0.2, matplotlib 3.7.1, and cartopy 0.21.1 (background tiles by OpenStreetMaps).</p>
Small mammals surveys, 1981 - 1996, Adirondack Long-Term Ecological Monitoring Program Project No. 10 by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York. Environmental Data Initiative
Small mammals are important in forested ecosystems: they are key predators on seeds and invertebrates, provide food for larger predators and act as disease vectors. The objective of this study was to document small mammal abundance and population changes in managed and unmanaged forests of Huntington Wildlife Forest (HWF). Seven sites were sampled from 1981-1996. Fifty traps per site (250 total) were deployed for 4 nights and checked in the mornings. All captured small mammals were identified, sexed, weighed, and measured for reproductive condition, tagged, and brought into the lab for processing. Females with embryos or placental scars were noted in the lab. Over a five-year period, 671 deer mice; 261 woodland jumping mice, 594 southern redbacked voles, 248 short-tailed shrews, 373 masked shrews, 75 smoky shrews and small numbers of other species were captured and sexed/aged. According to Prachar and Sage (1988), weights of deer mice, redbacked voles, woodland jumping mice, short-tailed shrews, masked shrews and smoky shrews differed among years and age classes for 1983-1987. Weights differed between sexes for mice and voles but not shrews. Placental scar/embryo counts of mice and voles did not differ among years, habitats, mammal age classes or sexes. Reproductive rates of shrews exhibited patterns of fluctuation from 1983-1987.
Songbird surveys , 1952 - 1964, 1983 - 2008 Adirondack Long-Term Ecological Monitoring Program Project No. 2 Breeding Birds by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York. Environmental Data Initiative.
Study objectives were to (1) Document long-term trends in relative abundance and diversity of breeding forest birds (songbirds) in forest stands with different harvest histories and (2) Identify bird species that can be used as indicators of habitat change or degradation. Declines in neotropical migrants have been linked to changes in habitat quantity and quality across species' range. Songbirds that nest and forage in different habitat types or at different heights in the forest canopy may not be affected equally by forest change or management. We detected breeding songbirds using point-counts at Huntington Wildlife Forest (HWF) in the central Adirondack Mountains of New York during 1983-2000 and modeled on an original songbird point count dataset from Webb et al. (1977). Relative abundance (RA, the number of individual birds/count) was measured in sites with differing management histories, from an unmanaged >300-year-old stand to a stand cut with the shelterwood/overstory removal method just prior to sampling in 1983). Over eighty bird species were detected during the study duration. Songbird ecology and habitat characteristics can be used to understand long-term changes in relative abundance as related to forest change.
Data from: Designing data science workshops for data-intensive environmental science research
<p>Over the last 20 years, statistics preparation has become vital for a broad range of scientific fields, and statistics coursework has been readily incorporated into undergraduate and graduate programs. However, a gap remains between the computational skills taught in statistics service courses and those required for the use of statistics in scientific research. Ten years after the publication of "Computing in the Statistics Curriculum,'' the nature of statistics continues to change, and computing skills are more necessary than ever for modern scientific researchers. In this paper, we describe research on the design and implementation of a suite of data science workshops for environmental science graduate students, providing students with the skills necessary to retrieve, view, wrangle, visualize, and analyze their data using reproducible tools. These workshops help to bridge the gap between the computing skills necessary for scientific research and the computing skills with which students leave their statistics service courses. Moreover, though targeted to environmental science graduate students, these workshops are open to the larger academic community. As such, they promote the continued learning of the computational tools necessary for working with data, and provide resources for incorporating data science into the classroom.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.