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9,113 results for “Sampling”
Fig. 1. Sampling map. A in Seven scuticociliates (Protozoa, Ciliophora) from Alabama, USA, with descriptions of two parasitic species isolated from a freshwater mussel Potamilus purpuratus
Fig. 1. Sampling map. A. Chewacla Creek, Auburn, Alabama (32º36′56″ N, 85º28ʹ58″ E). B. Orange Beach, Alabama (30º16′44″ N, 87º33′35″ E).
FMR1 Iso-Seq: per sample intermediate files
<p><em>FMR1</em> premutation carriers (55-200 CGG repeats) are at risk for developing Fragile X-associated Tremor/Ataxia Syndrome (FXTAS), an adult onset neurodegenerative disorder. In addition, 20 % of female carriers will develop Fragile X-associated Primary Ovarian Insufficiency (FXPOI), in addition to a number of clinical problems affecting premutation carriers throughout their life span. Marked elevation in <em>FMR1</em> mRNA levels have been observed with premutation alleles resulting in RNA toxicity, the leading molecular mechanism proposed for the <em>FMR1</em> associated disorders observed in premutation carriers.</p> <p>The <em>FMR1</em> gene, undergoes alternative splicing and we have recently reported that the relative abundance of all <em>FMR1</em> mRNA isoforms is significantly increased in premutation carriers.</p> <p>In this study, we further investigated the transcriptional <em>FMR1</em> isoforms distribution pattern in different tissues and identified a total of 49 isoforms, some of which observed only in premutation carriers and which might play a role in the pathogenesis of FXTAS.</p> <p>Further, we investigated the distribution pattern and expression levels of the <em>FMR1</em> isoforms in asymptomatic premutation carriers and in those with FXTAS and found no significant difference between the two groups.</p> <p>Our findings suggest that the characterization of the expression levels of the different <em>FMR1</em> isoforms is fundamental for understanding the regulation of the <em>FMR1</em> gene as imbalance in their expression could lead to an altered functional diversity with neurotoxic consequences. Their characterization will also help to elucidating the mechanism(s) by which “toxic gain of function” of the <em>FMR1</em> mRNA may play a role in FXTAS and/or in the other <em>FMR1</em>-associated conditions.</p>
EOL Sample maps
<ol> <li><em>Gadus chalcogrammus</em> (species) </li> <li><em>Gadus macrocephalus</em> (species) </li> <li><em>Gadus morhua</em> (species) </li> <li><em>Gadus</em> (genus) </li> <li>Gadidae (family)</li> <li>Gadiformes (order) </li> </ol>
EARLS: European aggregated reconstruction for large-sample studies
<p>EARLS is an openly available pan-European runoff–reconstruction dataset.</p> <p>As of now it is structured in the following way: </p> <div> <ul> <li>The `<em>coordinates.csv`</em> file contains basin outlet information with 4 columns: basin id (idx), type, and estimated latitude (lat) and longitude (lon) of the outlet.</li> <li>The `<em>license.md`</em> file contains information about the licensing.</li> <li>The `<em>shapefile` </em>folder includes a shapefile with all basin boundaries (see: <a href="https://www.hydrosheds.org/products/hydrobasins">HydroBASINS</a>).</li> <li>The `<em>reconstructions`</em> folder contains CSV files. Each file is named after the basin id and has at least two columns: date and simulation. The simulations are given in mm. Additional columns can be used to provide more information. For the current EARLS we added two additional columns that provide the remaining parameters for the uncertainty estimation.</li> <li>The `<em>model-card`</em> folder contains 2 files: `<em>model-card.html`</em>, and `<em>earls-crest.png`</em>. The html document includes the png as logo and renders a model card. A <a href="https://arxiv.org/abs/1810.03993">model card </a>is a short summary of the model genesis, designed to increase transparency by communicating information about trained models to broad audiences. We include all three files in the dataset so that future extensions can adapt them with maximal ease. We will also host the markdown files on the main home so that the permanent identifier within the model card can be used to access the data from there.</li> <li>Additional data/folders are optional, but can be used to provide background information. For instance, the EARLS contains an `<em>inputs`</em> folder, which comprises the basin-aggregated dynamic and static inputs: <br> <ul> <li>For the dynamic inputs (derived from <a href="https://www.ecad.eu/download/ensembles/download.php">E-OBS</a>) we use precipitation in mm per day, daily minimum/maximum/average temperature in °C.</li> <li>For the static inputs (derived form <a href="https://www.hydrosheds.org/hydroatlas">HydroATLAS</a>) we use basin area in square kilometers, average elevation in meter, average slopes in degrees, average stream gradient in decimeter per kilometer, average long-term air temperature in degrees Celsius, minimum long-term air temperature in °C, maximum long-term air temperature in °C, a global aridity index, a global climate moisture index, average fraction of sand in %, average fraction of clay in %, average fraction of silt in %, and average organic carbon content in tons per hectar.</li> </ul> </li> </ul> <h2>Changelog</h2> <p><strong>v0.3</strong></p> <ul> <li>Introduced a changelog. yay. </li> <li>Little corrections (spelling mistakes etc.) and nicer formatting in the technical data description. </li> <li>Corrected streamflow and variance normalization from hours to daily (affected versions: v0.0 and v.0.2; thanks to Corinna Frank).</li> <li>Corrected technical description of the area from m2 to km2 (thanks to Corrina Frank).</li> <li>Introduced an example data-file with a single basin (thanks to Juliane Mai).</li> </ul> </div>
Caravan - A global community dataset for large-sample hydrology
<p><strong>This is the </strong><strong>accompanying dataset to the following paper <a href="https://www.nature.com/articles/s41597-023-01975-w">https://www.nature.com/articles/s41597-023-01975-w</a></strong></p> <p><em>Caravan</em> is an open community dataset of meteorological forcing data, catchment attributes, and discharge daat for catchments around the world. Additionally, Caravan provides code to derive meteorological forcing data and catchment attributes from the same data sources in the cloud, making it easy for anyone to extend Caravan to new catchments. The vision of Caravan is to provide the foundation for a truly global open source community resource that will grow over time.</p> <p>If you use Caravan in your research, it would be appreciated to not only cite Caravan itself, but also the source datasets, to pay respect to the amount of work that was put into the creation of these datasets and that made Caravan possible in the first place.</p> <p><strong>All current development and additional community extensions can be found at <a href="https://github.com/kratzert/Caravan">https://github.com/kratzert/Caravan</a><br></strong><br><strong>IMPORTANT: Due to size limitations for individual repositories, the netCDF version and the CSV version of Caravan (since Version 1.6) are split into two different repositories. You can find the CSV version at <a href="https://zenodo.org/records/15530021">https://zenodo.org/records/15530021</a></strong></p> <p>Channel Log:</p> <ul> <li><strong>23 May 2022: Version 0.2</strong> - Resolved a bug when renaming the LamaH gauge ids from the LamaH ids to the official gauge ids provided as "govnr" in the LamaH dataset attribute files.</li> <li><strong>24 May 2022: Version 0.3</strong> - Fixed gaps in forcing data in some "camels" (US) basins.</li> <li><strong>15 June 2022: Version 0.4</strong> - Fixed replacing negative CAMELS US values with NaN (-999 in CAMELS indicates missing observation).</li> <li><strong>1 December 2022: Version 0.4 </strong>- Added 4298 basins in the US, Canada and Mexico (part of HYSETS), now totalling to 6830 basins. Fixed a bug in the computation of catchment attributes that are defined as pour point properties, where sometimes the wrong HydroATLAS polygon was picked. Restructured the attribute files and added some more meta data (station name and country).</li> <li><strong>16 January 2023: Version 1.0</strong> - Version of the official paper release. No changes in the data but added a static copy of the accompanying code of the paper. For the most up to date version, please check https://github.com/kratzert/Caravan</li> <li><strong>10 May 2023: Version 1.1</strong> - No data change, just update data description.</li> <li><strong>17 May 2023: Version 1.2</strong> - Updated a handful of attribute values that were affected by a bug in their derivation. See https://github.com/kratzert/Caravan/issues/22 for details.</li> <li><strong>16 April 2024: Version 1.4</strong> - Added 9130 gauges from the original source dataset that were initially not included because of the area thresholds (i.e. basins smaller than 100sqkm or larger than 2000sqkm). Also extended the forcing period for all gauges (including the original ones) to 1950-2023. Added two different download options that include timeseries data only as either csv files (Caravan-csv.tar.xz) or netcdf files (Caravan-nc.tar.xz). Including the large basins also required an update in the earth engine code</li> <li><strong>16 Jan 2025: Version 1.5</strong> - Added FAO Penman-Monteith PET (potential_evaporation_sum_FAO_PENMAN_MONTEITH) and renamed the ERA5-LAND potential_evaporation band to potential_evaporation_sum_ERA5_LAND. Also added all PET-related climated indices derived with the Penman-Monteith PET band (suffix "_FAO_PM") and renamed the old PET-related indices accordingly (suffix "_ERA5_LAND"). </li> <li><strong>27 May 2025: Version 1.6</strong><br> <ul> <li>Updated the CAMELS-AUS data to source from CAMELS-AUS v2. This means more basins (561 compared to 222) and more recent streamflow data (2022 compared to 2014). Note that the gauge id for four basins changed between the original CAMELS-AUS version and v2. Those gauges are ['camelsaus_224213A', 'camelsaus_224214A', 'camelsaus_227225A', 'camelsaus_403213A'] that all lost their trailing "A". To stay synced with CAMELS-AUS (v2), we also adapted the new naming.</li> <li>Added VERSION file to the root directory that contains the current version number.</li> <li>Updated the code to the most recent GitHub snapshot (commit 6eab036).</li> <li>Due to the 50GB repository limit, we had to split the netCDF version and the CSV version into two separate repositories. The CSV version can be found under https://zenodo.org/records/15530021</li> </ul> </li> </ul>
Summarised contextual data about metabarcoding Tara Oceans samples (2009-2013)
<p>Tab-separated values table describing the metabarcoding samples from the expedition Tara Oceans (2009-2013).</p> <p>Information such as depth, time, geographic position, size fraction, collected from <a href="https://pangaea.de/">Pangaea</a>, are listed in context_general tables. In context_stat tables, you will find a selection of physico-chemical parameters. Tara_Oceans_Pangaea_context.rds gathers all the data collected from Pangaea in a single R object.</p> <p>These tables have been built using the code here: <a href="https://gitlab.com/tara-and-friends-euk-metab/tara-oceans-metab-context/-/tree/v1.1.1" target="_blank" rel="noopener">https://gitlab.com/tara-and-friends-euk-metab/tara-oceans-metab-context/-/tree/v1.1.2</a> (v1.1.2).</p> <p>In this version 16S metabarcoding samples missing in previous versions were added in context_general.* and context_sats.*</p>
Minigraph pangenome graphs for HPRC samples
<p>Minigraph pangenome graphs and derived results for HPRC samples. Glenn Hickey generated the graphs. Heng Li produced the per-assembly variant calls and annotated the alleles. See 00README.txt for file description.</p>
The Kconfig Variability Framework as a Feature Model: Sampled Configurations for Manual Evaluation
<p>This dataset contains plain text files with sampled solutions used during the manual evaluation of the transformation rules presented in https://doi.org/10.5445/IR/1000162110. To reproduce the manual evaluation process yourself, please copy over the respective Kconfig files in a local copy of the Linux kernel Git repository and run `make menuconfig`. You need to insert an invisible `MODULES` configuration symbol to ensure that tristate configuration symbols are handled correctly by Kconfig. Additionally, you need to remove the default Linux Kconfig file and rename the Kconfig file for which you want to reproduce the evaluation process accordingly (simply remove the number prefix).</p><p>Configurations marked with KCONFIG_NONSOLUTION cannot be reconstructed in `menuconfig`, wherein configurations marked with KCONFIG_SOLUTION should be reproducable in the `menuconfig` interface.</p><p>We additionally provide the generated feature models for the 9 selected Kconfig files, alongside with the Kconfig files themselves. Kconfig{1,2,3,4,5} can be automatically evaluated with Kfeature, as they contain no tristate confsyms.</p><p>The upstream version of Kfeature can be found on Codeberg: https://codeberg.org/6b6279/Kfeature</p>
Sample tweets from May 2022
<p>Dataset containing twitter data, namely hashed twitter id, hashed user id, tweet language, user statistics </p><p> </p>
Fig.ç4.C aligus longiramus sp. nov., holotype, female (KMNH IvR 500,511). A, leg 2, dorsal view; B, leg 3, dorsal view; C, leg 4, dorsal view; D, leg 5, ventral view. Scale bars: 0.1 mm. in Occurrence of Caligid Copepods (Crustacea) in Plankton Samples Collected from Japan and Ŋailand, with the Description of a New Species
Fig.ç4.C aligus longiramus sp. nov., holotype, female (KMNH IvR 500,511). A, leg 2, dorsal view; B, leg 3, dorsal view; C, leg 4, dorsal view; D, leg 5, ventral view. Scale bars: 0.1 mm.
Fig.ç3.C aligus longiramus sp. nov., holotype, female (KMNH IvR 500,511). A, maxilla, dorsal view; B, maxilliped, dorsal view; C, sternal furca, dorsal view; D, leg 1, ventral view; E, exopod of leg 1 enlarged, ventral view. Scale bars: 0.1 mm. in Occurrence of Caligid Copepods (Crustacea) in Plankton Samples Collected from Japan and Ŋailand, with the Description of a New Species
Fig.ç3.C aligus longiramus sp. nov., holotype, female (KMNH IvR 500,511). A, maxilla, dorsal view; B, maxilliped, dorsal view; C, sternal furca, dorsal view; D, leg 1, ventral view; E, exopod of leg 1 enlarged, ventral view. Scale bars: 0.1 mm.
Fig.ç2.A, Caligus latigenitalis Shiino, 1954, male (KMNH IvR 500, 510), habitus, dorsal view; B–F. Caligus longiramus sp. nov., holotype, female (KMNH IvR 500, 511): B, habitus, dorsal view; C, caudal rami, dorsal view; D, antennule, ventral view; E, antenna, postantennal process, and maxillule, ventral view; F, mandible. Scale bars: 1 mm (A, B); 0.1 mm (C–F). in Occurrence of Caligid Copepods (Crustacea) in Plankton Samples Collected from Japan and Ŋailand, with the Description of a New Species
Fig.ç2.A, Caligus latigenitalis Shiino, 1954, male (KMNH IvR 500, 510), habitus, dorsal view; B–F. Caligus longiramus sp. nov., holotype, female (KMNH IvR 500, 511): B, habitus, dorsal view; C, caudal rami, dorsal view; D, antennule, ventral view; E, antenna, postantennal process, and maxillule, ventral view; F, mandible. Scale bars: 1 mm (A, B); 0.1 mm (C–F).
Fig.ç1.C ollection sites of pelagic caligids including 3 stations in Japanese waters (St. 2–4, 2010) and 1 station in the Gulf of ffiailand (St. 1, 2006). in Occurrence of Caligid Copepods (Crustacea) in Plankton Samples Collected from Japan and Ŋailand, with the Description of a New Species
Fig.ç1.C ollection sites of pelagic caligids including 3 stations in Japanese waters (St. 2–4, 2010) and 1 station in the Gulf of ffiailand (St. 1, 2006).
Fig. 1. a. Sampling occurred within a 200 in Taxonomic And Functional Diversity Of Ants (Hymenoptera:Formicidae) In An Upper Hill Dipterocarp Forest In Peninsular Malaysia
Fig. 1. a. Sampling occurred within a 200-ha area of the Perak Integrated Timber Complex (PITC) where 24 sampling plots were established. b. Filled circle shows the location of PITC in the Temengor Forest Reserve in northern Peninsular Malaysia. c. Each sampling plot consisted of a 20 × 80 m rectangle that contained four square subplots (20 × 20 m) delineated within them. Each subplot was further divided into four 10 × 10 m quadrats. Open circles indicate placement of arboreal pitfall and ground pitfall traps and filled circles indicate placement of baits and leaf litter sifting in each quadrat.
Fig. 1. Map showing the localities where Acropora corals were sampled during the first Snellius expedition, numbers correspond with Table 1 in The Acropora Humilis Group (Scleractinia) Of The Snellius Expedition (1929-30)
Fig. 1. Map showing the localities where Acropora corals were sampled during the first Snellius expedition, numbers correspond with Table 1.
Sacurine toy dataset - 6 samples / 3 QC / 3 Blanks
<p><strong>Objective: </strong></p> <p>Influence of age, body mass index, and gender on the urine metabolome</p> <p><strong>Cohort: </strong></p> <p>183 employees from CEA</p> <p><strong>LC-HRMS: </strong></p> <p>LTQ-Orbitrap (negative ionization mode)</p>
Data from: Integrated species distribution models to account for sampling biases and improve range wide occurrence predictions
<p><strong><span>Aim</span></strong></p> <p><span>Species distribution models (SDMs) that integrate presence-only and presence-absence data offer a promising avenue to improve information on species' geographic distributions. The use of such 'integrated SDMs' on a species range-wide extent has been constrained by the often-limited presence-absence data and by the heterogeneous sampling of the presence-only data. Here, we evaluate integrated SDMs for studying species ranges with a novel expert range map-based evaluation. We build a new understanding about how integrated SDMs address issues of estimation accuracy and data deficiency and thereby offer advantages over traditional SDMs.</span></p> <p><strong><span>Location</span></strong></p> <p><span>South and Central America.</span></p> <p><strong><span>Time period</span></strong></p> <p><span>1979-2017.</span></p> <p><strong><span>Major taxa studied</span></strong></p> <p><span>Hummingbirds.</span></p> <p><strong><span>Methods</span></strong></p> <p><span>We build integrated SDMs by linking two observation models – one for each data type – to the same underlying spatial process.</span> <span>We validate SDMs with two schemes: i) cross-validation with presence-absence data and ii) comparison with respect to the species' whole range as defined with IUCN range maps. We also compare models relative to the estimated response curves and compute the association between the benefit of the data integration and the number of presence records in each data set.</span></p> <p><strong><span>Results</span></strong></p> <p><span>The integrated SDM accounting for the spatially varying sampling intensity of the presence-only data was one of the top-performing models in both model validation schemes. Presence-only data alleviated overly large niche estimates, and data integration was beneficial compared to modelling solely presence-only data for species that had few presence points when predicting the species' whole range. On the community level, integrated models improved the species richness prediction.</span></p> <p><strong><span>Main conclusions</span></strong></p> <p><span>Integrated SDMs combining presence-only and presence-absence data are successfully able to borrow strengths from both data types and offer improved predictions of species' ranges. Integrated SDMs can potentially alleviate the impacts of taxonomically and geographically uneven sampling and to leverage the detailed sampling information in presence-absence data.</span></p>
Data from: Wildlife fecal microbiota exhibit community stability across a semi-controlled longitudinal non-invasive sampling experiment
<p>Wildlife microbiome studies are being used to assess microbial links with animal health and habitat. The gold standard of sampling microbiomes directly from captured animals is ideal for limiting potential abiotic influences on microbiome composition, yet fails to leverage the many benefits of non-invasive sampling. Application of microbiome-based monitoring for rare, endangered, or elusive species creates a need to non-invasively collect scat samples shed into the environment. Since controlling sample age is not always possible, the potential influence of time-associated abiotic factors was assessed. To accomplish this, we analyzed partial 16S rRNA genes of fecal metagenomic DNA sampled non-invasively from Rocky Mountain elk (<em>Cervus canadensis</em>) near Yellowstone National Park. We sampled pellet piles from four different elk, then aged them in a natural forest plot for 1, 3, 7, and 14 days, with triplicate samples at each time point (i.e., a blocked, repeat measures (longitudinal) study design). We compared microbiomes of each elk through time with point estimates of diversity, bootstrapped hierarchical clustering of samples, and a version of ANOVA–simultaneous components analysis (ASCA) with PCA (LiMM-PCA) to assess the variance contributions of time, individual and sample replication. Our results showed community stability through days 0, 1, 3 and 7, with a modest but detectable change in abundance in only 2 genera (<em>Bacteroides</em> and <em>Sporobacter</em>) at day 14. The total variance explained by time in our LiMM-PCA model across the entire 2-week period was not statistically significant (p>0.195) and the overall effect size was small (<10% variance) compared to the variance explained by the individual animal (p<0.0005; 21% var.). We conclude that non-invasive sampling of elk scat collected within one week during winter/early spring provides a reliable approach to characterize microbiome composition in a 16S rDNA survey and that sampled individuals can be directly compared across unknown time points with minimal bias. Further, point estimates of microbiome diversity were not mechanistically affected by sample age. Our assessment of samples using bootstrap hierarchical clustering produced clustering by animal (branches) but not by sample age (nodes). These results support greater use of non-invasive microbiome sampling to assess ecological patterns in animal systems.</p>
Diversity-aware Fairness Testing of Machine Learning Classifiers through Hashing-based Sampling
<p>The experimental results of the evaluation of VBT-X.</p> <h2>Abstract</h2> <div> <h3>Context:</h3> <p>There are growing concerns about algorithmic fairness, as some machine learning (ML)-based algorithms have been found to exhibit biases against protected attributes such as gender, race, age and so on. Individual fairness requires an ML classifier to produce similar outputs for similar individuals. Verification Based Testing (<span>Vbt</span>) is a state-of-the-art black-box testing algorithm for individual fairness that leverages constraint solving to generate test cases.</p> </div> <div> <h3>Objective:</h3> <p>Generating diverse test cases is expected to facilitate efficient detection of diverse discriminatory data instances (i. e., cases that violate individual fairness). Hashing-based sampling techniques draw a sample approximately uniformly at random from the set of solutions of given Boolean constraints. We propose <span>Vbt</span>-X, which improves <span>Vbt</span> with hashing-based sampling, aiming to improve its testing performance.</p> </div> <div> <h3>Method:</h3> <p>We realize hashing-based sampling for <span>Vbt</span>. The challenge is that the off-the-shelf hashing-based sampling techniques cannot be integrated in a straightforward manner because the constraints in <span>Vbt</span> are generally not Boolean. Moreover, we propose several enhancement techniques to make <span>Vbt</span>-X more efficient.</p> </div> <div> <h3>Results:</h3> <p>To evaluate our method, we conduct experiments, where <span>Vbt</span>-X is compared to <span>Vbt</span>, <span>Sg</span> and ExpGA (other well-known fairness testing algorithms) over a set of configurations consisting of several datasets, protected attributes, and ML classifiers. The results show that, with each configuration, <span>Vbt</span>-X detects more discriminatory data instances with higher diversity than <span>Vbt</span> and <span>Sg</span>. <span>Vbt</span>-X detects discriminatory data instances with higher diversity than ExpGA, though the number of discriminatory data instances detected by <span>Vbt</span>-X is lesser than ExpGA.</p> </div> <div> <h3>Conclusion:</h3> <p>Our proposed method performs better than other state-of-the-art black-box fairness testing algorithms, particularly in terms of diversity. Our method can serve to efficiently identify flaws in ML classifiers with respect to individual fairness for subsequent improvements of an ML classifier. On the other hand, although our method is specific to individual fairness, it could work for testing other aspects of a software system such as security and counterfactual explanations with some technical adaptations, which remains for future work.</p> </div> <p> </p> <div> <h2>Acknowledgments</h2> <p>This paper is partly based on results obtained from a project, JPNP20006, commissioned by the New Energy and Industrial Technology Development Organization (NEDO). This paper is supported by JST SPRING, Grant Number JPMJSP2131.</p> </div>
Dataset for the uncertainty assessment of confocal measurements of industrial samples
<p>These original measurement data relate to the publication: J. Paredes, G. Kortaberria: Towards task-specific uncertainty assessment for imaging confocal microscopes, <a href="https://www.euspen.eu/knowledge-base/ICE23191.pdf">ICE23191.pdf (euspen.eu)</a>. Please refer to this open access publication for a detailed description of the measurement setup and procedure.</p><p>All data are in ASCII-format. Each file contains 2 columns, where they are the measured <i>x/y</i> and <i>z</i>-coordinates of the 2D profile extracted from the surface. All the coordinates are recorded in micrometers. The 0/90 at the end of the file names represent the orientation of the extracted profile being<i> x</i> and <i>y</i> direction respectively.</p><p>The topoghraphy files contain 3 columns, they are the measured<i> x</i>, <i>y</i>, and <i>z</i>-coordinates of the surface.</p><p><strong>Acknowledgement</strong></p><p>This project 20IND07 TracOptic has received funding from the EMPIR programme co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation programme. Funder name: European Metrology Programme for Innovation and Research (EMPIR).</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.