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9,882 results for “scales”
Soil bacterial diversity inventories along small-scale stress gradients in the Arctic, Antarctic, and Chihuahuan Deserts (2022-2023)
Bacteria form the foundation of soil ecosystems in desert ecosystems, driving soil function, diversity, and ecology. Soil physicochemistry is largely dictated by larger topographical variations and can directly drive bacterial community composition and the relationships within. Bacteria may form complex networks of interactions with other bacteria and other soil taxa that have implications for emergent properties such as diversity and stability, but the way these interactions are impacted by environmental stressors remains poorly understood. Here, we sampled soil bacterial communities of three desert ecosystems at different latitudes: the McMurdo Dry Valleys, Antarctica; the northern Chihuahuan Desert, Jornada Experimental Range (JER), New Mexico, USA; and the Arctic tundra at the Canadian High Arctic Research Research Station (CHARS), Victoria Island, Nunavut, Canada. In each system, a holistic stress-gradient was sampled based on local topographical variation, vegetation cover, and water availability. Sampling along the stress-gradient was conducted at four distinct stress levels, namely lower elevation with vegetation cover, lower elevation without vegetation cover, higher elevation with vegetation cover, and higher elevation without vegetation cover. To allow robust biodiversity inference and co-occurrence network construction, 30 replicates were collected at each stress level, and this was done at two independent stress gradients for the Chihuahuan Desert and Arctic sites. The Antarctic samples consisted of two independent stress gradients, one ranging from low, middle to high elevation without vegetation cover, and one consisting of two levels with and without vegetation cover. For each site, soil pH and gravimetric water content was also measured. Each replicate was then sequenced on an Illumina MiSeq for 2x250 paired-end sequencing of the 16S rRNA marker. Sequences were archived in NCBI under BioProject PRJNA1098956, with accession numbers included herein.
Geochemical, physicochemical, and genomic data from a continental-scale survey of microbial diversity in Antarctic soils (2003-2023)
This data package offers comprehensive insights into Antarctic soil microbial diversity and composition. From 2003 to 2023, a total of 186 samples were collected from diverse locations spanning the Antarctic Peninsula to East Antarctica, representing a wide range of environmental gradients and climatic conditions. Soils were stored at -20°C to preserve their integrity for downstream analyses. This data package integrates cultivation-independent sequencing of prokaryotic and fungal communities alongside a robust cultivation-dependent culture collection to enable direct comparisons across microbial diversity assessment methods. Accompanying geochemical, physicochemical, and environmental parameters provide critical context for biogeographical analyses, offering a valuable resource for studying microbial adaptations and community dynamics in extreme Antarctic environments.
MCR LTER: Coral Reef: Landscape-scale patterns of nutrient enrichment in a coral reef ecosystem: implications for coral to algae phase shifts, Adam et al., Ecol. Appl.
These data and analyses code were generated in support of the manuscript: Adam TC, Burkepile DE, Holbrook SJ, Carpenter RC, Claudet J, Loiseau C, Thiault L, Brooks, AJ, Washburn L, and RJ Schmitt, Ecological Applications We investigated the potential role of anthropogenic nutrient loading in driving recent coral-to-macroalgae phase shifts on reefs in the lagoons surrounding Moorea, French Polynesia. We used nitrogen (N) tissue content and stable isotopes (δ15N) in an abundant macroalga (Turbinaria ornata) together with empirical models of nutrient discharge to describe spatial and temporal patterns of nutrient enrichment in the lagoons. Turbinaria ornata were collected at 190 sites around Moorea in January, May, and August 2016. These sampling periods corresponded with distinct seasonal shifts in rainfall and wave forcing. Our results revealed that patterns of N enrichment were linked to rainfall, wave-driven circulation, and distance from anthropogenic nutrient sources, especially human sewage. In addition to describing high resolution patterns of N enrichment from 2016, we also analyzed core MCR time series on N tissue content in Turbinaria ornata from three habitats (fringing reef, back reef, and reef crest) at the six core MCR LTER sites between 2007 and 2013. These data showed that fringing reefs have been consistently enriched in N relative to back reefs, which are enriched relative to the reef crest. Further, these patterns mirror long-term patterns of nitrate and nitrite concentrations in the water column. We also analyzed core MCR time series on benthic communities and fishes and found that back reef sites that were consistently enriched in N between 2007 and 2013 experienced large increases in macroalgae while macroalgae remained much less abundant at back reef sites with lower N. These phase shifts to macroalgae occurred despite island-wide increases in the density and biomass of herbivorous fishes over the time period. Together, these results indicate th
Final models for "Global-Scale Full-Waveform Ambient Noise Inversion" by Sager et al. (2020)
<p>The exodus model contains the inverted structure model and the source distribution can be found in the HDF5 file. Both can be visualized in ParaView. For the source model, we recommend opening it with the correspoding XDMF file (select "XDMF Reader" in the dialogue box).</p>
Hydraulic scale model experiments on the two-dimensional run-up of impulse wave trains on steep to vertical slopes
<p>This dataset includes the experimental data and videos, which were generated during the study on the run-up of impulse wave trains at the Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich.</p>
Public Dataset for "Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior"
<p>Dataset for the "Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior" paper, published in ICWSM 2018. The full text of the paper can be found <a href="https://arxiv.org/pdf/1802.00393.pdf">here</a>. </p> <p>The dataset provided here includes an updated version of the original dataset, with ~100k tweets annotated using the CrowdFlower platform: </p> <ul> <li> <p>hatespeech_id_label_PUBLIC_100K.csv: contains ~100K rows, where every row consists of a unique Tweet ID. </p> </li> <li> <p>hatespeech_text_label_vote_RESTRICTED_100K.csv: contains ~100K rows, where every row consists of the tweet text, its label according to majority annotation and the number of majority annotators. Available only <a href="https://zenodo.org/record/3706866#.Xmkh6i97FQI">here</a>.</p> </li> <li> <p>retweets.csv: contains ~2K rows, where every row consists of the row number in the hatespeech_text_label_vote_RESTRICTED_100K.csv file which is the first occurrence of a Tweet text followed by comma-separated row numbers of all other occurrences of the same Tweet text in the same file. There are ~8K other occurrences due to retweets. Available only <a href="https://zenodo.org/record/3706866#.Xmkh6i97FQI">here</a>.</p> </li> </ul> <p> </p> <p>UPDATE: It has come to our understanding that a number of the tweets are not available anymore for download on Twitter. Therefore, we provide <a href="https://zenodo.org/record/3706866#.YYLG6S8RqjQ">here </a>the hatespeech_text_label_vote_RESTRICTED_100K file with the full ~100K tweet texts, their associated majority label, and the number of votes for the majority label. The tweets are shuffled so that there is no connection between tweet IDs and texts (in order to be in line with the T&C of Twitter). </p> <p>Please cite the paper in any published work that uses any of these resources. </p> <p>@inproceedings{founta2018large, <br> title={Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior}, <br> author={Founta, Antigoni-Maria and Djouvas, Constantinos and Chatzakou, Despoina and Leontiadis, Ilias and Blackburn, Jeremy and Stringhini, Gianluca and Vakali, Athena and Sirivianos, Michael and Kourtellis, Nicolas}, <br> booktitle={11th International Conference on Web and Social Media, ICWSM 2018}, <br> year={2018}, <br> organization={AAAI Press} <br> } </p> <p>For any further questions contact a.m.founta at gmail dot com AND markos.charalambous at eecei dot cut dot ac dot cy </p>
Sensitivity simulation investigating the influence of subgrid scale orography
<p>The main data used to generate the figure of the work entitled ''Alleviation of an Arctic Sea Ice Bias in a Coupled Model through Modifications in the Subgrid-scale Orographic Parameterization'' by Gastineau et al., submitted for publication.</p> <p>If you have question, please contact Guillaume Gastineau : guillaume.gastineau (at) locean-ipsl.upmc.fr</p>
Remote detection and recording of atomic-scale spin dynamics
<p>This folder contains all data and all processing files for the paper titled "Remote detection and recording of atomic-scale spin dynamics". View full paper here: https://www.nature.com/articles/s42005-020-0361-z</p>
DNA Crookedness Regulates DNA Mechanical Properties at Short Length Scales
<p>Data presented in the annual conference DPG conference 2019 (<a href="https://regensburg19.dpg-tagungen.de/">https://regensburg19.dpg-tagungen.de/</a>). </p> <p>Here we discuss how DNA sequence allow us to modulate its structure and mechanical properties, thus proving for the first time a one to one map between sequence and mechanical code. This is intended to provide an overview of the results published in the following peer-reviewed freely available papers:<br> Phys. Rev. Lett. 122, 048102 (2019) [DOI: 10.1103/PhysRevLett.122.048102 or https://doi.org/10.1101/283648]<br> Nanoscale Nanoscale 11, 21471 (2019) [DOI: 10.1039/C9NR07516J]<br> PNAS 114, 7049 (2017) [DOI: 10.1073/pnas.1705642114]<br> Nucleic Acids Research, gkaa225 [DOI: 10.1093/nar/gkaa225]<br> (Please cite them, if you found this information useful.)</p>
CHAMP and Swarm solar activity- and height-scaled polar cap plasma density measurements
<p>Solar activity- and height-adjusted plasma density measurements in the polar cap (i.e., above 80° latitude in Modified Apex<sub>110</sub> coordinates) from the Swarm and CHAMP satellites. covering the entire CHAMP mission period (2002–2009) and the Swarm mission period from launch through February 2020.</p> <p>Plasma density measurements are scaled to a nominal solar activity level of <<em>F</em>10.7><sub>27</sub> = 80 sfu, and an altitude of 500 km, as described in Hatch et al. (submitted to JGR: Space Physics; <a href="https://www.essoar.org/doi/abs/10.1002/essoar.10502854.1">ESSOAr pre-print</a>) </p> <p>This dataset was prepared as a part of the "Swarm+ Coupling High-Low Atmosphere Interactions: Ion Outflow" project (<a href="https://swarmoutflow.w.uib.no/">project website</a>) (<a href="https://eo4society.esa.int/projects/swarm-coupling-high-low-atmosphere-interactions-ion-outflow/">ESA website</a>), and is funded by European Space Agency Contract #4000126731.</p> <p>Data are stored in HDF5 format as a Python Pandas dataframe. They can be loaded into Python via the following.</p> <pre><code class="language-python">import pandas as pd df = pd.read_hdf('CHAMP_Swarm_polarcap_adjDensity.hdf',key='df')</code></pre> <p>The data columns are</p> <ul> <li>'NeAdj' : Solar activity- and height-adjusted plasma density (cm<sup>-3</sup>)</li> <li>'a110lat' : Modified Apex<sub>110</sub> latitude (deg)</li> <li>'a110lon' : Modified Apex<sub>110</sub> longitude (deg)</li> <li>'mlt' : Modified Apex<sub>110</sub> magnetic local time</li> <li>'h_km' : satellite altitude (km)</li> <li>'gclat' : geocentric latitude (deg)</li> <li>'gclon' : geocentric longitude (deg)</li> <li>'sat' : satellite identifier (string, one of 'A', 'B',' 'C', or 'CHAMP')</li> </ul>
Data from a Large-Scale Experiment to Evaluate the Effects of Trapping to Control Muskrats (Ondatra zibethicus) in The Netherlands
<p>This data set supports the publication 'A Large-Scale Experiment to Evaluate the Effects of Trapping to Control Muskrats (Ondatra zibethicus) in The Netherlands' by Daan Bos, Emiel van Loon, Erik Klop and Ron Ydenberg. (the paper was accepted for publication in Wildlife Society Bulletin in 2020)</p> <p>The Muskrat is an invasive species in Europe and in the Netherlands muskrat burrowing can compromise the integrity of dykes and hence poses a public safety threat. For that reason a control programme has been in effect since the arrival of the species in 1941. To investigate the relation between catch and effort and enhance prediction models, a large randomized controlled experiment was designed and conducted from 2013 till 2016. The publication by Bos et al. (2020) analyses the experimental results and here we present and document the experimental data. See the readme.md file for further information.</p>
Where does subduction initiate and cease? A global scale perspective (Supplementary material)
<p>Supplementary material for Where does subduction initiate and cease? A global scale perspective</p>
Video Analysis Scale of Engagement (VASE): Initial and Final Protocols
<p>These files contain the initial and final protocols that were used to develop the Video Analysis Scale for Engagement (VASE). The abstract of our article in Wellcome Open Research, 5:230: https://wellcomeopenresearch.org/articles/5-230/v2:</p> <p><strong>Background</strong>: The current study sought to develop a valid, reliable and unobtrusive tablet computer-based observational measure to assess engagement of people with advanced dementia. The Video Analysis Scale of Engagement (VASE) was designed to enable the rating of moment-by-moment changes in engagement during an activity, which would be useful for both future research and current residential care. <strong>Methods:</strong> An initial version of the VASE was tested. Face validity and content validity were assessed to validate an operational definition of engagement and develop an acceptable protocol for the scale. Thirty-seven non-professional and professional volunteers were recruited to view and rate level of engagement in music activities using the VASE. <strong>Results</strong>: An inter-class coefficient (ICC) test gave a high level of rating agreement across professionals and non-professionals. However, the ICC results of within-professionals were mixed. Linear mixed modelling suggested that the types of interventions (active or passive music listening), the particular intervention session being rated, time period of video and the age of raters could affect the ratings. <strong>Conclusions</strong>: Results suggested that raters used the VASE in a dynamic fashion and that the measure was able to distinguish between interventions. Further investigation and adjustments are warranted for this to be considered a valid and reliable scale in the measurement of engagement of people with advanced dementia in a residential care setting.</p>
Genome-scale metabolic model of Quercus suber
<p>Genome-scale metabolic model of Quercus suber in SBML Level 3 Version 2 format. This model was reconstructed using <em>merlin</em> (https://merlin-sysbio.org), an open-source software.</p>
Result data related to "Tröndle et al (2020) -- Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe"
<p>The dataset contains aggregated result data of our study. See `README.md` for more information.</p> <p>If you use this data in an academic publication, please cite the following article:</p> <blockquote> <p>Tröndle, T., Lilliestam, J., Marelli, S., Pfenninger, S., 2020. Trade-offs between geographic scale, cost, and infrastructure requirements for fully renewable electricity in Europe. Joule.</p> </blockquote> <p>CHANGELOG:</p> <p>Version 1.2 (2020-09-28)</p> <p>* Add location name to scenario results.<br> * Add scenario results in CSV format, next to already existing NetCDF format.<br> * Remove capacity factors from scenario results.</p> <p>Version 1.1 (2020-07-17)</p> <p>* Remove macOS resource forks cluttering the zip file.</p> <p> </p>
Questionnaire data to research small-scale farmers' information sharing for adapting to climate change in Mozambique (2019-2020)
<p>Data collected from individual questionnaires with local communities of 4 districts of Mozambique in November 2019 and July 2020. It contains as well data from nine individual questionnaires to institutions (government and NGOs) working with local communities for their development.</p> <p>Data are replies from interviews containing open and closed questions about a) climate change adaptation options necessary for Mozambican small scale farmers, about b) the most used and preferred information sources of farmers, about c) the main barriers for a better exchange of information, and about d) proposals for improving it. The questionnaire can be consulted in Appendix A (in English and Portuguese). The open questions had the purpose to understand the causes and explanations about the themes presented. The closed questions followed a 0-5 likert scale approach, where 5 meant a very important factor and 0 non important one. This format was pursued for developing statistical analysis and comparison between the different types of participants. We used the same questions and format for interviewing farmers and stakeholders, although the questionnaire for farmers included also personal aspects like gender, age, and education.</p>
Monthly SPEI dataset at 3-, 6-, and 12-month time scales in the Amazon River Basin
<p>SPEI datasets derived from the Global SPEI database webpage and developed by Vicente-Serrano et al. (2010). This product is disseminated by the Spanish National Research Council (CSIC) for the period from January 1901 to December 2018 (118 years, version 2.6). The SPEI datasets at 3-, 6-, and 12-month time scales are gridded over a 0.50-degree grid on the Amazon River Basin. A total of 1416 GeoTIFF files in Geographic Tagged Image File Format (GeoTIFF) format, one per month, are provided.</p>
Data and code from: Insect biomass decline scaled to species diversity: General patterns derived from a hoverfly community
<p>To study changes in flying insect communities, and hoverflies in particular, malaise trap samples from a German site were compared between two years (Hallmann et al. 2020). The data files deposited here contain data obtained from six malaise traps in the Wahnbachtal (North Rhine-Westphalia, Germany, 50.851944N, 7.320833E) that were deployed in 1989 and again in 2014, at the exact same locations. Traps were situated in wet meadows as well as tall perennial meadows, in close proximity to shrub corridors, to forest–grassland borders, and to the Wahnbach River and surrounded by agricultural land, essentially a rather heterogeneous habitat. The Wahnbach River and the greater part of the valley are protected for watershed purposes and are subject to nature conservation management by the Wahnbach Talperrenverband. Hence, several restrictions apply to safeguard against water contamination.</p> <p>Total insect biomass collected with these traps was already included in Hallmann et al. (2017), but here we focus on additional information: the abundance and richness of hoverflies (Syrphidae) in each of the collected samples (pots). Methodologies of collection are described in Sorg (1990), Schwan et al. (1993), Sorg et al. (2013), Hallmann et al. (2017), and Ssymank et al. (2018). In brief, malaise traps were deployed throughout the growing season and operated continuously (day and night). Malaise trap construction (e.g., size, material, colouring, and ground sealing) and placing (e.g., positioning, orientation, and slope of the locations) were standardised in all aspects. Insect samples were preserved in 80% ethanol solution. Catches of the six traps investigated in the present study were emptied regularly: On average exposure intervals were 7.0 d (SD = 0.5) in 1989 and 16.7 d (SD = 5.6) in 2014. Across the six traps in 2014 the total exposure time (in number of days) was 42% higher compared to 1989. All collected samples (n = 196) were used in the present analysis with in total 19,604 individual hoverflies counted, distributed over 162 species and 59 genera.</p> <p>To assess how environmental conditions have changed over the 25 year, several additional datasets were assembled. Climatic<br> data were obtained from 169 climatic stations and were used to interpolate daily weather variables to each trap location, using spatiotemporal kriging. These steps are described in detail in Hallmann et al. (2017).</p> <p>Our analysis (see R code) consists of three components. First, we considered total abundance, species richness, and species diversity, at two temporal scales: pooled per year, i.e., across the sampling season, and seasonally (i.e., per day), and we compared these metrics between 1989 and 2014. Second, we examined how total flying biomass (i.e., the weight of all trapped insects, of which hoverflies are only a small proportion) related to total abundance as well as species richness of hoverflies. Third, we derived persistence probabilities and population growth rate trends per species, to examine interspecific variation in these parameters.</p> <p>Descriptions of the deposited files:</p> <p><strong>Groups.csv</strong><br> MF_NR = identifier of each of the six malaise trap locations<br> yrf = year of sampling<br> pot = sample identifier<br> dt = number of sampling days<br> from.dnr = day-of-the-year on which a pot was attached to a malaise trap<br> to.dnr = day-of-the-year on which a pot was collected from a malaise trap<br> mean.daynr = mean day-of-the-year of the sampling period<br> Nspec = number of different hoverfly species found in a pot<br> Nind = number of hoverfly individuals found in a pot</p> <p><strong>Counts.csv</strong><br> A matrix of counts of individual hoverflies per pot per species. The 196 rows represent the pots in the same order as in the file 'Groups.csv'. The columns represent the 162 different hoverfly species found. The scientific species names are indicated in the column headers.</p> <p><strong>PairedData.csv</strong><br> pot = sample identifier<br> JAHR = year of sampling<br> MF_NR = identifier of each of the six malaise trap locations<br> dt = number of sampling days<br> from.dnr = day-of-the-year on which a pot was attached to a malaise trap<br> to.dnr = day-of-the-year on which a pot was collected from a malaise trap<br> NI = number of hoverfly individuals found in a potbiomass.daily<br> NSP = number of different hoverfly species found in a pot<br> biomass.daily = daily fresh weight [gram] of flying insects: total fresh weight in a pot divided by the number of sampling days.</p> <p><strong>ModelFrame.csv</strong><br> MF_NR = identifier of each of the six malaise trap locations<br> yrf = year of sampling<br> pot = sample identifier<br> dt = number of sampling days<br> from.dnr = day-of-the-year on which a pot was attached to a malaise trap<br> to.dnr = day-of-the-year on which a pot was collected from a malaise trap<br> mean.daynr = mean day-of-the-year of the sampling period<br> plot = identifier of each of the six malaise trap locations<br> date = date for which the weather variables are interpolated<br> daynr = day-of-the-year for which the weather variables are interpolated<br> altitude = altitude [m] of the malaise trap locations<br> year = year of sampling<br> temperature = interpolated temperature [degrees Celsius]<br> precipitation = interpolated precipitation [mm per day]<br> wind.speed = interpolated wind speed [m/s]</p> <p><strong>Data_Rcode.pdf</strong><br> This pdf provides the R-code behind the analysis of the Hoverfly data. Three datasets are provided along with this R-code document, namely "Counts.csv", "Groups.csv", "PairedData.csv" and "ModelFrame.csv". Additionally, the BUGS-code ""syrphidModel.jag" is required for running the daily-activity model in JAGS.</p> <p><strong>syrphidModel.jag</strong><br> This BUGS-code is required for running the daily-activity model in JAGS.</p>
LAI_TS_Val: LAI time-series validation datasets in the 1-km pixel grid at global scale from 2001 to 2011
<p>Leaf area index (LAI), which is defined as one half of the total green leaf area per unit ground surface area, is a critical structural variable for quantifying the exchange processes of energy and matter between the land surface and atmosphere, it is thus identified as a key parameter in most terrestrial ecosystem models. To acquire long-term LAI records at the global scale, several remote sensing LAI products have been generated from various satellite sensors. However, assessing the uncertainties associated with these LAI products through comparisons with independent ground-truth measurements is pivotal for an effective application of products. Many sites from global networks have collected and provided invaluable ground LAI measurements covering a wide range of biome types and spatial variabilities. These site-based LAI measurements have been obtained about 30 years (1990-now). However, the spatial scale mismatch between site and pixel observations restricts the utilization of LAI measurements for product time-series validation. This datasets were generated from site-based LAI measurements of FLUXET and Chinese Ecosystem Research Network (CERN), using the proposed GUGM (Grading and Upscaling of Ground Measurements) method to resolve the scale-mismatch issue between site and sensor observations and maximize the utility of time-series of site-based LAI measurements, which can achieve the goal of product time-series validation. This GUGM approach first ingests both high-resolution images and site-based LAI measurements to capture the spatiotemporal variability in the product pixel grid. Then, a strategy was employed to grade the spatial representativeness of LAI measurements in the product pixel grid. For those LAI measurements which cannot be directly used in the validation of products, a strategy was adopted to calculate the spatial upscaling coefficient based on site-based LAI measurements and aggregated high-resolution reference maps to derive reliable LAI time-series validation datasets. The GUGM method has been applied to the site-based LAI measurements to generate global time-series LAI validation datasets from 2001 to 2011 in the 1 km pixel grid. The datasets include 28 sites which are mainly located in North America and Asia, providing 924 validation data in total. Among these sites, 16 sites with 508 (55.0%) validation data were obtained for forest, while 11 sites with 341 (36.9%) validation data and one site with 75 (8.1%) were obtained for crops and grasses, respectively. This datasets were saved in two formats: *.xls and *.kmz and each format was zipped for 63 KB and 31 KB, respectively.</p>
CLDF dataset accompanying Chechuro et al.'s "Small-scale multilingualism through the prism of lexical borrowing" from 2021
<p>Cite the source of the dataset as:</p> <blockquote> <p>Chechuro et al. (2021) Small-scale multilingualism through the prism of lexical borrowing. International Journal of Bilingualism.</p> </blockquote>
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.