Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
4,175
datasets available to search
ShareScore release 0.7.1
Dataset results
4,175 results for “university”
A droplet digital polymerase chain reaction assay to detect rare helminth parasites infecting natural host populations (Vancouver Island 2023, University of Wisconsin Madison Laboratory colony 2024)
Helminth infections represent a significant challenge to human, livestock, and wildlife health, yet they remain relatively under-studied, especially in terms of their ecological impacts. Better understanding of how these parasites spread in wildlife populations could improve our ability to predict and manage disease transmission across various species. Traditional detection methods, such as visually identifying parasites in environmental samples or infected hosts, often fall short, especially during the early stages of infection when parasite loads are minimal. In this study, we introduce a highly sensitive and precise droplet digital PCR (ddPCR) assay that quantifies helminth DNA in aquatic habitats, focusing on the 18S rRNA gene as a marker. These data utilize the model host-parasite system between the tapeworm Schistocephalus solidus, and its cyclopoid copepod host, Acanthocyclops robustus. The molecular assays are built around creating an infection standard in the lab, where copepods were singly infected with a single tapeworm parasite. We extracted DNA from 100 infected adults and used this as a standard to translate gene copy numbers from the ddPCR reactions to actual animal values. After creating a known lab standard, we then use the generated probes and primers to detect (and quantify!) infection burdens in field samples, which include both water filter samples (eDNA) and zooplankton tows from several lakes around Vancouver Island, B.C. The data presented here include well-specific data from ddPCR runs (amplitude of individual level oil droplets in the reaction) as well as each ddPCR analysis in its entirety. In order to prove the specificity of probes and probe-primers, we include here ddPCR runs of closely related helminth species, Schistocephalus cotti and Schistocephalus pungitii. We also consider the binding to another genera of copepod, the calanoid Eurytomora. All of the data wrangling, analysis, and data visualization are included as .Rmd files in th
Data from the Forest Resilience Threshold Experiment, University of Michigan Biological Station, 2024
During the 2024 field season, data collection efforts led by the FoRTE crew centered on understanding forest ecosystem dynamics and carbon cycling processes in a temperate forest landscape. Comprehensive datasets were gathered to evaluate structural and functional responses across multiple forest strata. Measurements included diameter at breast height (DBH) for canopy, subcanopy, and seedling layers, alongside a detailed subcanopy census to assess understory composition and diversity. Soil respiration (Rs) was monitored to quantify carbon fluxes, while fern density and distribution were documented to explore their role in forest microclimates and nutrient cycling. Photosynthetically active radiation (PAR) readings provided insights into light availability and its impact on primary production. Advanced remote sensing tools, including LiDAR and normalized difference vegetation index (NDVI), were employed to characterize canopy structure, vegetation health, and spatial heterogeneity. These diverse datasets collectively contribute to a robust framework for analyzing forest resilience, recovery, and carbon sequestration potential following disturbance, advancing our understanding of ecosystem processes in the face of environmental change.
Landscape of fear and safety summer 2025 data from University of Michigan Biological Station stream research facilities
Predator prey interactions are often driven by sensory cues and these cues play a role in non-consumptive effects. We are interested in the role that chemical cues (from predators) play in resource use by one of fish common prey, crayfish. We created flow through mesocosms and populated them with crayfish and various configurations of shelters and food. Then we presented to the crayfish predator cues (from large mouth bass) and measured behavioral responses from midnight to 4 am.
University of Oregon Great Basin Vegetation Cover Study, Southeastern Oregon, 2023.
This data package contains qualitative and quantitative environmental and vegetation survey data used to investigate connections between cattle grazing intensity and vegetation cover in the Great Basin region of Eastern Oregon. The experimental design utilized a piosphere approach to select plots of different grazing intensity in pastures stratified by recent burn history. Surveys were completed in 340 plots around 43 water sources in the summer of 2023. Surveys consisted of recording site characteristics (e.g. slope, aspect, human impacts) as well as measuring plant species cover, cattle and other ungulate dung counts, biocrust development, and soil stability and texture. Plants were generally identified to species where possible, allowing for comparisons between species as well as identification of trends among functional groups (shrubs, annual grasses, perennial grasses, annual forbs, perennial forbs, trees).
Forest tree, woody debris, root ingrowth, soil respiration and characterization data from long-term research plots for LTREB at the University of Michigan Biological Station
The NSF-funded project "LTREB: Drivers of temperate forest carbon storage from canopy closure through successional time" (2014-2024) supports research to meet the following goals: 1) elucidate mechanisms responsible for changes in C storage over decades to centuries; 2) link processes leading to persistence and resilience of forest C storage following disturbance; 3) quantify the effects of potential drivers such as forest structure, N availability, climate change, and atmospheric deposition on decadal and longer-term trajectories of C storage. Field activities for this research are conducted at the University of Michigan Biological Station (UMBS) on a pair of chronosequences and several old reference forests. Synthesis activities utilize data collected from these field sites in support of the LTREB project, as well as data synthesized from other sources (e.g., long-term UMBS plot data, AmeriFlux data, FIA data) all intended to address the core questions of the LTREB project. This dataset has been compiled and expanded over a series of versions, with new data types and observations appended periodically. Presently, the dataset includes observations from tree inventory censuses, woody debris sampling, fine root ingrowth cores, soil respiration measurements, and two sets of soil collections aimed at quantifying a range of physical, chemical, and biological properties of soil.
Gender and ethnic diversity of members of US university natural resource program external advisory board members, 2017-2022
This dataset contains deidentified demographics information for the members of external advisory boards that serve university natural resource programs. Data collected in 2017 and 2022 represents a sample of land-grant, National Association of University Forestry Program-affiliated, TIMES-ranked universities and colleges. Each row represents a member of an advisory board. Data collection was completed in two years: 2017 and 2022. Data was collected from department webpages and lists of advisory board members provided by department personnel. As needed, information was augmented through internet searches for public LinkedIn pages, organizational pages, local news stories, etc. Data include a unique respondent ID, a code for the university they are from, their employer affiliation (e.g., NGO, federal government, NR business, etc.) and their gender and ethnicity measured as binary variables.
A Decade of Progress: Open Data Practices in Bioscience at the University of Edinburgh
<p><strong>General Information:</strong></p> <p>This reposotory contains the outcomes of a project executed at the Biosciences Institutes of the University of Edinburgh. This research project assesses the openness and FAIRness (Findable, Accessible, Interoperable, and Reusable) of data linked to publications from these institutes. Here, you will find datasets, analytical codes, and figures that detail our project’s methodology and results aiming to enhance data-sharing practices and promote the adherence to FAIR principles within and beyond our community. </p> <p>This repository is linked to a publication that has been submitted to: Proceedings of the Royal Sociaty B - Biological Sciences</p> <p>The main project: You can find the main repository and workspace of this project on Github containing the data and code of this project and all the previous related projects: <a href="https://github.com/BioRDM/InsightsOfOpenPracticesInBiosciences">Here</a></p> <p><strong>The Protocol:</strong></p> <p>The protocol for this project can be found on Protocol.io, where detailed step-by-step guidelines are provided to ensure that the research methods are transparent and reproducible. <a href="https://www.protocols.io/view/a-protocol-for-assessing-open-data-practices-honou-kxygxyxmdl8j/v2" rel="nofollow">https://www.protocols.io/view/a-protocol-for-assessing-open-data-practices-honou-kxygxyxmdl8j/v2</a></p> <p>The main project</p> <p><strong>Contact us:</strong></p> <p>for General Queries, Collaboration and Data Management: <em>bio_rdm@ed.ac.uk (<a href="https://biology.ed.ac.uk/research/facilities/research-data-management">BioRDM</a>) </em>or the Principal Investigator and Corresponding Author: Andrew Millar (<em>andrew.millar@ed.ac.uk</em>) - Orcid: 0000-0003-1756-3654</p> <p><strong>Data Collection</strong></p> <p>The Dataset of this project was collected in two different periods by the honour students (Creasey, de Ugarte, Strevens, Usman, Yun Wong) in our department:<br>- Project one from Januray 2023 to June 2023<br>- Project two from January 2024 to June 2024</p>
S21 | UATHTARGETS | University of Athens Target List
<p>This is the collection associated with list S21 UATHTARGETS on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S21 | UATHTARGETS | <strong>University of Athens Target List </strong></p> <p>Update 22/3/2020: added InChIKey file. Update 8/2/2022: new files from Dec 2021 with new compounds, NORMAN ID, classification and comments (provided by Maristina Nika). (v0.2.1 - attempted fix of CSV headers; v0.2.2 many small fixes flagged via PubChem deposit)</p> <p>Additional grant acknowledgement: Aristeia-Excellence: Transformation products of emerging pollutants in the aquatic environment (TREMEPOL project), 2012-2015, European Social Fund-Ministry of Education, <a href="http://tremepol.chem.uoa.gr/">http://tremepol.chem.uoa.gr/</a></p>
Data from Phenocam (PHE) measurements at Berlin-Technical University of Berlin (BETUCC) from 2023-06-01 to 2023-12-31 [RAW]
<p>Original phenocam images separated into near-infrared (NIR) and visible (VIS).</p>
Data of European University Association (EUA) Open Access Survey 2017-2018
<p>This database refers to the data collected by the European University Association (EUA) for its Open Access Survey 2017-2018, which gathered responses from universities and higher education institutions across Europe. The full report published by the association is available at <a href="https://eua.eu/resources/publications/826:2017-2018-eua-open-access-survey-results.html">https://eua.eu/resources/publications/826:2017-2018-eua-open-access-survey-results.html</a>.</p> <p>The data included in this database refers only to those universities and higher education institutions that accepted their data to be available in open access (n=266). All information that could lead to the identification of individual universities and higher education institutions was removed from the database. The following files are available:</p> <ul> <li>Questionnaire</li> <li>Database in the following formats: .sav (IBM SPSS Statistics), .xlsx (Microsoft Excel) and .csv</li> <li>Codebook: includes information on all the variables and their coding.</li> </ul>
Dataset for the IntoValue 1 + 2 studies on results dissemination from clinical trials conducted at German university medical centers completed between 2009 and 2017
<p>The IntoValue dataset contains clinical trials conducted at one of 35 German UMCs and registered on ClinicalTrials.gov or the German Clinical Trials Registry (DRKS). All trials were reported as complete between 2009 and 2017 on the trial registry at the time of data collection. The dataset also includes a results publication found via manual searches; if multiple results publications were found, the earliest was included.</p> <p>Trials were associated with a German UMC by searching for trials with a UMC listed as responsible party or lead sponsor, or with a principle investigator (PI) from a UMC ('lead_city'). Version 1 additionally includes trials with a UMC only as a facility (`facility_city`). A lookup table of regular expressions used to identify German UMCs is available at <a href="https://github.com/quest-bih/IntoValue2/blob/master/data/1_sample_generation/city_search_terms.csv">https://github.com/quest-bih/IntoValue2/blob/master/data/1_sample_generation/city_search_terms.csv</a>.</p> <p>Trials include all interventional studies and are not limited to investigational medical product trials, as regulated by the EU's Clinical Trials Directive or Germany's Arzneimittelgesetz (AMG) or Novelle des Medizinproduktegesetzes (MPG).</p> <p>DRKS data were searched (pre-filtered for completion years and study status as well as Germany as 'Country of recruitment') and downloaded as CSVs from the DRKS website (<a href="https://www.drks.de/">https://www.drks.de/</a>). ClinicalTrials.gov data were downloaded downloaded as pipe files from Clinical Trials Transformation Initiative (CTTI) Aggregate Content of ClinicalTrials.gov (AACT) (<a href="https://aact.ctti-clinicaltrials.org/pipe_files">https://aact.ctti-clinicaltrials.org/pipe_files</a>). DRKS and ClinicalTrials.gov use different terminology for various trial aspects, such as phase and masking; these different levels are captured in the data dictionary as `levels_drks` and `levels_ctgov`. For later analyses requiring parity across registries, levels for some variables were collapsed and a lookup table is provided in `iv_data_lookup_registries.csv`.</p> <p>These data were generated and used for two publications (Wieschowski et al., 2019; Riedel et al. 2021) and therefore comprises two versions (indicated as `iv_version`).</p> <p>For version 1, registry data was collected on April 17, 2017 from ClinicalTrials.gov and on July 27, 2017 for DRKS and was limited to trials with a completion date on DRKS and primary completion date on ClinicalTrials.gov between 2009 and 2013. Version 1 manual searches for results publications were conducted from 2017-07-01 to 2017-12-01.<br> For version 2, registry data was collected on June 3, 2020 and was limited to trials with a completion date on DRKS and ClinicalTrials.gov between 2014 and 2017. Version 2 manual searches for results publications were conducted from 2020-07-01 to 2020-09-01.</p> <p>Raw registry data for versions 1 and 2 is available in `raw-registries.zip`.</p> <p>Publication identifiers (DOI, PMID, URL) were manually entered during the publication search and then further enhanced using the API of Internet Archive's open-source Fatcat catalog of research publications, to add PMIDs based on DOIs, and vice versa.</p> <p>Manual search steps differed slightly in the two versions and are indicated and described in `identification_step`.<br> Version 1 includes trials with a German UMC as either a `lead_city` or a `facility_city`, whereas version 2 is limited to trials a German UMC as a `lead_city`.</p> <p>Each row indicates a single trial registration. Due to changes in completion dates, some trials are duplicated between versions as indicated in `is_dupe`. Cross-registered trials were manually deduplicated, and some cross-registered duplicates remain (e.g., DRKS00004156 and NCT00215683) and are not indicated in the dataset.</p> <p>All dates are provided as `yyyy-mm-dd`.</p> <p>Additional documentation on each variable (type, description, levels) is provided in `iv_data_dictionary.csv`.</p> <p>Additional information on the project and methods for generating the dataset is available in associated publications and at the project's OSF page (<a href="https://osf.io/98j7u/">https://osf.io/98j7u/</a>). Code for the project is available at <a href="https://github.com/quest-bih/IntoValue2">https://github.com/quest-bih/IntoValue2</a>.</p> <p><strong>References:</strong></p> <p>Wieschowski, S., Riedel, N., Wollmann, K., Kahrass, H., Müller-Ohlraun, S., Schürmann, C., Kelley, S., Kszuk, U., Siegerink, B., Dirnagl, U., Meerpohl, J., & Strech, D. (2019). Result dissemination from clinical trials conducted at German university medical centers was delayed and incomplete. Journal of Clinical Epidemiology, 115, 37–45. <a href="https://doi.org/10.1016/j.jclinepi.2019.06.002">https://doi.org/10.1016/j.jclinepi.2019.06.002</a></p> <p>Riedel, N., Wieschowski, S., Bruckner, T., Holst, M. R., Kahrass, H., Nury, E., Meerpohl, J. J., Salholz-Hillel, M., & Strech, D. (2021). Results dissemination from completed clinical trials conducted at German university medical centers remained delayed and incomplete. The 2014-2017 cohort. Journal of Clinical Epidemiology, 0(0). <a href="http://doi.org/10.1016/j.jclinepi.2021.12.012">https://doi.org/10.1016/j.jclinepi.2021.12.012</a><br> </p>
US_UMB and US_UMd Ameriflux towers biometric plot data at the University of Michigan Biological Station, Pellston, MI (1997 to 2024)
These are the annual leaf litterfall carbon fluxes and average soil respiration measurements for the two flux towers (reference, aka 'AmeriFlux' and treatment, aka 'FASET') at UMBS.
Nearshore high-frequency temporal water quality observations and process-based modeling of aquatic ecosystem metabolism in Lake Tahoe completed by members of the Blaszczak Lab at the University of Nevada Reno, 2021-2023
The overarching goal of this project was to develop a process-based understanding of how watershed-to-lake connections drive nearshore productivity dynamics in a large oligotrophic mountain lake (Lake Tahoe). We addressed this goal through a combined approach of high-frequency sensor deployment and maintenance, ecosystem metabolism modeling, laboratory incubations, and routine monitoring of water chemistry and other parameters. The data we collected as part of this project and the ecosystem metabolism estimates we generated demonstrate how variable ecosystem productivity is in time and space in the nearshore of Lake Tahoe. Although maintenance of the sensor arrays during the exceptional winter of 2023 was challenging, we were able to capture the data necessary to estimate a complete time series of metabolic activity across two years with very different hydroclimatic conditions. Throughout this project we accomplished the following: 1. We generated over two years of daily estimates of ecosystem metabolism (gross primary productivity, ecosystem respiration, and net ecosystem productivity) from multiple locations on both the east and west shores of the lake and from areas in close proximity to and far away from stream water inflows. 2. We measured ammonium (NH4+) and nitrate (NO3-) concentrations in surface water samples from both Glenbrook and Blackwood creeks and the nearshore of Lake Tahoe for over two years. 3. We quantified rates of NH4+ and NO3- uptake in benthic samples of the dominant substrate type collected during peak streamflow, the receding limb, and baseflow conditions in 2023 from multiple locations in the nearshore using established laboratory incubation methods. 4. Finally, we used a combination of time series models and structural equation modeling to integrate our results and improve understanding of the direct and indirect effects of hydroclimatic variability on observed patterns in ecosystem metabolism in the nearshore. See this git code repository
Nutrient amendment effects on phytoplankton, water chemistry, and cyanotoxins in the 2018 Large-Scale Mesocosm Experiment at the University of Kansas Field Station
This dataset includes water physicochemical parameters, phytoplankton community composition, and cyanobacteria metabolites collected during a 21-day nutrient amendment experiment conducted from 23 July to 13 August 2018 at the University of Kansas Biological Station, Lawrence, KS, United States (39.049674°N, 95.190777°W). The experiment was performed using 18 large-scale, closed-bottom fiberglass tanks (volume: 11,000 L; height: 1.25 m; diameter: 3 m). Three tanks served as ambient controls (CON), while the others received one of the following nutrient treatments: nitrogen only (280 µM) as either ammonium chloride (NH4) or sodium nitrate (NO3); nitrogen (280 µM) plus phosphorus (200 µM) as either ammonium chloride + dipotassium phosphate (NHP) or sodium nitrate + dipotassium phosphate (NOP); and phosphorus only (200 µM) as dipotassium phosphate (P). Each tank received an initial nutrient dose on Day 0.5, followed by weekly additions of 20% of the initial amendment to maintain treatment conditions. All data were quality controlled to correct basic errors and to remove measurements outside the manufacturer’s standard operational ranges.
Clear Lake water quality monitoring data from 2019 to 2023 by the University of California, Davis
A major barrier to effective water quality restoration at Clear Lake is the absence of quantitative data on the anticipated response to restoration projects. In-lake monitoring (in-situ measurements) is needed to understand better the processes contributing to poor water quality. This data package contains the in-situ measurements collected by the University of California, Davis at Clear Lake between 2019 and 2023, which include: continous stream properties at three locations (Middle, Scott, and Kelsey Creeks); continuous meteorological variables at seven locations around the perimeter of the lake; continuous lake temperature and dissolved oxygen at multiple depths and locations across the lake (six permanent water quality stations); continuous lake surface temperature in the shoreline; and discreate samples to measure nutrient concentrations and phytoplankton biovolumes and species identification throughout the water column and across all three lake basins every 6-8 weeks.
Landscape Ecosystem Classification Soils and Vegetation Plots Data at the University of Michigan Biological Station, Pellston, Michigan from 1987 to 2015 remeasurements
Landscape ecosystems are a means of understanding the spatial patterns of and the functional interrelationships in forest ecosystems. Landscape ecosystem research is a multifactor, holistic approach to identifying, classifying, describing, and mapping terrain ecosystems. Abiotic and biotic factors are integrated in the field to distinguish repeating units similar in ecological structure and function. Landscape ecosystems are identified by simultaneous integration of physiographic, soil, and vegetation information. The more stable components--physiography and soil--largely determine local climate, and water and nutrient relations, and thus the interrelationships of physiography and soil form the foundation of a landscape ecosystem classification. Vegetation is seen as a phytometer that integrates the many abiotic factors and their interactions, and therefore reflects differences in ecosystem structure and function. When the three main ecosystem factors are analyzed simultaneously, one can perceive interrelationships that result in ecologically meaningful differences among segments of the ecosphere. Landscape ecosystems are spatial; they are volumetric, multi-dimensional segments of earth, whose components include soil, water, atmosphere, solar radiation, and biota. These segments can be identified, classified, described, and mapped at various scales. From the years of 1988 to 2001, various graduate students of Burton V. Barnes completed their masters thesis and dissertations in this pursuit. The attached data set is a culmination of these individual work. Each plot has measurements at various scales within the 10 by 30 meet plot. A stratified random design was used to locate plot locations. The random design was stratified by major and minor landforms in the region. All trees within the plot where identified and dbh was measured. All individual shrubs where identified and abundance was counted within the entire plot. Soils pits locations for each plot where selected
Physarum polycephalum Repeated Maze Honors Thesis, Tulane University, SE Louisiana 2024-2025
We conducted an experimental maze study from January to March, 2025, on Physarum polycephalum at Tulane University in New Orleans, Louisiana. We collected data on changes in locomotive behavior as P. polycephalum repeatedly solved the same maze. From daily photographs of growth, we recorded path choice, contamination presence and location, number of times P. polycephalum grew directly over maze walls, how many of the four dead-ends P. polycephlaum grew down, and efficiency through surface area covered. Data collection is complete. We found a significant increase in efficiency, and a significant decrease in both dead-end paths and wall jumping, as maze repetitions increased. This points to evidence of information storage and retrieval, and therefore cognitive processes such as memory, within the single-cellular protist Physarum polycephalum.
Phenological Responses of Equisetum arvense to Early-season Soil and Air Warming in Reseach Plots on the University of Alaska Fairbanks North Campus - May through September 2023
This dataset contains the phenological stages of Equisetum arvense (horsetails) subjected to snow removal in spring (which led to ground warming) and to air warming using open topped chambers in a two by two factorial experiment. The main dataset contains the phenological phases of plants in plots of subjected to the four treatments between 1 May and 29 September 2023, and height data. Supplementary files contain temperature data for dataloggers placed in the soil and in the air and file information.
Rainfall Stable Isotopes collected at Florida International University-MMC (FCE LTER), Miami, Florida, USA, October 2007 - ongoing
δ18O and δ2H values for precipitation collected at the Modesto A. Maidique Campus of Florida International University (FIU) relative to Vienna Standard Mean Ocean Water. Rainfall was collected from the roof of AHC-1 building (25.75772 ºN, 80.37108 ºW) from October 2007 to November 2022 using an Aerochemitrics wet/dry collector. Since December 2022, rainfall has been collected using a Palmex Rain Collector located in the FIU Organic Garden (25.75490 ºN, 80.37985 ºW). Oxygen and hydrogen isotope ratios were measured on a Los Gatos Research DLT-100 Liquid-Water Isotope Analyzer in the Hydrogeology laboratory at FIU since the project inception.
warmXtrophic: plant community responses to the individual and interactive effects of climate warming and herbivory across multiple years at Kellogg Biological Station Long-Term Ecological Research Sites (KBS LTER), Michigan, USA, and University of Michigan Biological Station (UMBS), Michigan, USA.
Climate change has both direct and indirect effects on ecological communities. Whereas most climate change ecology experiments manipulate abiotic drivers to measure direct effects of climate on species or communities, fewer quantify the indirect effects through biotic interactions, especially over multiple sites and years. In this factorial experiment we manipulate temperature through open-top chambers, and the level of insect herbivory through insecticide. At two early successional field sites separated by 3 degrees of latitude and 3°C of mean annual temperature (University of Michigan Biological Station, Pellston, MI and Kellogg Biological Station, Hickory Corners, MI), 6 replicate 1-m2 plots per treatment were installed in May 2015. 12 plots per site are at ambient temperature, 12 are warmed with year-round non-UV filtering polycarbonate and wood frame construction OTCs for tall-stature plants (Welshofer et al. 2018 MEE). Insecticide reduces insect herbivory in half the plots (Welshofer et al. 2018 Oecologia). Over the course of the experiment, OTCs warmed the plant communities by 1.9°C-3.0°C on average over the growing season. Each year, through 2021, plant traits and community responses were measured at the species level: plant phenology (green-up, flowering, flowering duration, seed set); plant percent cover (aerial % cover of the 1m2 plot); plant traits (specific leaf area, C and N content), herbivory damage to leaves, and plant species biomass (only in 2021). Further methodological details are found within each response variable metadata. This experiment is ongoing and further data package updates are planned. L0 data is available upon request. R scripts can be found here: https://github.com/SpaCE-Lab-MSU/warmXtrophic. The biotic and abiotic community context and relative strengths of direct vs. indirect effects may yield ecological surprises under climate change unless addressed together. Large-scale experiments like this one can improve our ability to unde
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.