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106 results for “data merge”
WACCM-X simulated data for Wu et al.'s paper on "The Formation Mechanism of Merged EIA during a Storm on November 4, 2021"
This dataset contains all the data related to WACCM-X simulated result figure in paper "The Formation Mechanism of Merged EIA during a Storm on November 4, 2021". The data includes simulated NMF2 at 0° longitude (40°S - 40°N latitude), between 9UT and 24UT on November 3rd and 4th; electron density, WI, V, d(O+)/dt, d(O+)(chem), d(O+)(ExB), d(O+)(wind), and d(O+)(ambi) at 0° longitude (40°S - 40°N latitude) between 100-500 km from 9UT to 24UT on November 3rd and 4th.
Code, benchmarks and experiment data for the ICAPS 2024 paper "Merging or Computing Saturated Cost Partitionings? A Merge Strategy for the Merge-and-Shrink Framework"
<p>This bundle contains code, scripts and benchmarks for reproducing all experiments reported in the paper. It also contains the data generated for the paper. Finally, it contains an appendix with some more detailed results for the paper.</p> <p>appendix.pdf: document with more detailed results ommitted in the paper.</p> <p>sievers-et-al-icaps2024-fast-downward.zip contains the implementation based on Fast Downward. It also contains the experiment scripts compatible with Lab 7.1 for reproducing all experiments of the paper, under experiments/scp-ms. The scripts 2024-03-* contain configurations for running the experiments and the script paper-crc.py gathers the data and produces plots and tables. (Note that some adjustments to the scripts would need to be done because, e.g., the entire tree is not a repository anymore.)</p> <p>sievers-et-al-icaps2024-ipc-benchmarks.zip contains the IPC benchmarks. It consists of the STRIPS IPC benchmarks used in all optimal sequential tracks of IPCs up to 2023 (suite optimal_strips from https://github.com/aibasel/downward-benchmarks).</p> <p>sievers-et-al-icaps2024-lab.tar.gz contains a copy of Lab 7.1 (https://github.com/aibasel/lab).</p> <p>sievers-et-al-icaps2024-raw-data.zip and sievers-et-al-icaps2024-processed-data.zip contain the experimental data. Directories without the "-eval" ending (sievers-et-al-icaps2024-raw-data.zip) contain raw data, distributed over a subdirectory for each experiment. Each of these contain a subdirectory tree structure "runs-*" where each planner run has its own directory. For each run, there are symbolic links to the input PDDL files domain.pddl and problem.pddl (can be resolved by putting the benchmarks directory to the right place), the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), the run script "run" used to start the experiment, and a "properties" file that contains data parsed from the log file(s). Directories with the "-eval" suffix (sievers-et-al-icaps2024-processed-data.zip) contain a "properties" file, which contains a JSON directory with combined data of all runs of the corresponding experiment. In essence, the properties file is the union over all properties files generated for each individual planner run.</p> <p>Note on license: we chose GPL v3.0 or later mainly because we consider our implementation based on Fast Downward the main contribution of this package, and Fast Downward comes with GPL v3.0. We only include a copy of Lab and the benchmarks for convenience.</p>
A merged satellite data from SABER, MLS, and HALOE
<p>Dimensions: </p> <p>Time: from 1993.01 to 2020.12 </p> <p>Lev: 55 layers from the surface to 1e-5 hPa</p> <p>Lat: 22 grids from 52.5°N to 52.5°S</p> <p>Lon: 1 grid, zonal mean </p> <p> </p> <p>below 14 hPa: merge MLS and HALOE</p> <p>above 14 hPa: first merging MLS and SABER, then merge with HALOE </p>
Retrieve, Merge, Predict: Augmenting Tables with Data Lakes
<p>Files composing the YADL data lake, for the paper "Retrieve, Merge, Predict: Augmenting Tables with Data Lakes (Experiment, Analysis & Benchmark Paper)"</p> <p>We present an in-depth analysis of data discovery for analytics in data lakes, focusing on table augmentation for given machine learning tasks. We analyze alternative methods used in the three key steps: retrieving joinable tables, merging information, and predicting with the resultant table. As data lakes, the paper uses YADL (Yet Another Data Lake) -- a novel dataset developed as a tool for benchmarking this data discovery task -- and Open Data US, a well-referenced real data lake. Through systematic exploration on both lakes, our study outlines the importance of accurately retrieving join candidates, and the efficiency of simple aggregation methods. We report new insights on the benefits of existing solutions and on the their limitations, aiming at guiding future research in this space.</p> <p>Archives provided here follow the notation used for the experiments, which is different from what is reported in the paper. The four YADL versions available here are:</p> <ul> <li>"binary_update" (YADL Binary)</li> <li>"wordnet_full" (YADL Base)</li> <li>"wordnet_vldb_10" (YADL 10k)</li> <li>"wordnet_vldb_50" (YADL 50k)</li> </ul>
Additional data of the PhD thesis "Merge-and-Shrink Abstractions for Classical Planning: Theory, Strategies, and Implementation"
<p>The original data set for the thesis "Merge-and-Shrink Abstractions for Classical Planning: Theory, Strategies, and Implementation" by Sievers, 2017, available under https://doi.org/10.5281/zenodo.1164137, accidentally did not include the data of one experiment, namely the raw data of the parsed data contained in "sota-symba-spmas-eval". It can be found in this data set.</p>
Experimental data and benchmarks of the paper "Merge-and-Shrink Task Reformulation for Classical Planning"
<p>This dataset contains both benchmarks and data used in the paper.</p> <p>PDDL benchmark files can be found in the file optimal-strips-benchmarks.tar.gz<br> and satisficing-strips-benchmarks.tar.gz, which contain the STRIPS domains from<br> the sequential optimal/satisficing tracks of all IPCs as collected in the<br> repository https://bitbucket.org/aibasel/downward-benchmarks.</p> <p>All other files in this dataset contain raw and processed data of all<br> experiments, which were generated using Downward-Lab (see<br> https://doi.org/10.5281/zenodo.399255). The scripts used to run the experiments<br> can be found in the software bundle for this paper (see<br> https://doi.org/10.5281/zenodo.3232878).</p> <p>Directories without the "-eval" ending contain raw data, distributed over a<br> subdirectory for each experiment. Each of these contain a subdirectory tree<br> structure "runs-*" where each planner run has its own directory. For each run,<br> there are symbolic links to the input PDDL files domain.pddl and problem.pddl<br> (can be resolved by putting the benchmarks directory to the right place), the<br> run log file "run.log" (stdout), possibly also a run error file "run.err"<br> (stderr), the run script "run" used to start the experiment, and a "properties"<br> file that contains data parsed from the log file(s).</p> <p>Directories with the "-eval" ending contain a "properties" file, which contains<br> a JSON directory with combined data of all runs of the corresponding<br> experiment. In essence, the properties file is the union over all properties<br> files generated for each individual planner run.</p>
Test data for Galaxy IUC Seurat Inspect & Manipulate Tool (Merge)
Open the record for dataset details and reuse information.
Cluster merged fluxgate/search coil data for a solar wind interval occuring on 15/02/2015 21:25:00-22:40:00
<p>Cluster merged fluxgate/search coil data for a solar wind interval occuring on 15/02/2015 21:25:00-22:40:00</p>
Figure 1. A in A web based tool to merge geometric morphometric data from multiple characters
Figure 1. A workflow for the method.
Data from: Burrowing into the past: Extending niche space models of procellariiform breeding grounds by merging fossil and historic data
Open the record for dataset details and reuse information.
Data from: Deterministic cell pairing with simultaneous microfluidic merging and sorting of droplets
Open the record for dataset details and reuse information.
Supporting data for: Honeybees adapt to a range of comb cell size by merging, tilting and layering their construction
Open the record for dataset details and reuse information.
Merged Global Pluvial Floods DataBase (Input Data)
<p>This repository contains the input data used in the Jupyter notebook downloadable from Github <a href="https://github.com/FatimaPillosu/Merged_Global_Pluvial_Floods_DataBase">here</a>.</p> <p>Such input data consists in two main datasets:</p> <ul> <li>A raw (global and regional) flood reports from four different databases.</li> <li>A global daily rainfall analysis from NOAA (CPC_GLOBAL_PRCP_v1.0).</li> </ul> <p>The Jupyter notebook runs a Python code that post-processes the raw flood reports, using information extracted from other datasets, to select some reports of interest (mainly regarding pluvial and flash floods). At a later stage, such reports are merged into a single database for global pluvial/flash flood reports. The Jupyter notebook also runs a Metview-Python code to visualize partial and final results as map plots.</p> <p>The four original databases are:</p> <ol> <li>FloodList, FL (Global domain): <a href="http://floodlist.com/">http://floodlist.com/</a></li> <li>Emergency Events Database, EMDAT (Global domain): <a href="https://www.emdat.be/">https://www.emdat.be/</a></li> <li>European Severe Weather Database, ESWD (Europe): <a href="https://www.essl.org/cms/european-severe-weather-database/">https://www.essl.org/cms/european-severe-weather-database/</a></li> <li>Storm Events Database, SED (USA): <a href="https://www.ncdc.noaa.gov/stormevents/">https://www.ncdc.noaa.gov/stormevents/</a></li> </ol> <p><em>NOTE: </em>For more details about these databases (documentation, licenses, etc.), look at the README.md file.</p> <p><em>NOTE: </em>The data in this repository is intended for an exclusive NON-COMMERCIAL academic or personal use, and it is released under the Creative Commons Attribution-ShareAlike 4.0 International Public License. For more information, look at the LICENSE.md file.</p>
Data from: On merging Acer sections Rubra and Hyptiocarpa: molecular and morphological evidence
In this study, we expanded Acer sect. Rubra Pax to include A. sect. Hyptiocarpa Fang. Traditionally, section Rubra comprises two iconic species, Acer rubrum Linnaeus (red maple) and A. saccharinum Linnaeus (silver maple), of eastern North American forests as well as the rare Japanese montane species, A. pycnanthum K. Koch. Section Hyptiocarpa consists of A. laurinum Hasskarl and A. pinnatinervium Merrill, which occur in subtropical and tropical regions of southwestern China to southeast Asia. Here, we confirm prior phylogenetic results showing the close relationship between sects. Rubra and Hyptiocarpa, and we use scanning electron microscopy to demonstrate that leaves of species within these sections have similar arrangements of cuticular waxes, which account for the silvery color of their abaxial surfaces. We describe that the sections also share labile sex expression; inflorescences that range from compound racemose thyrses, to racemes or umbels and that may have undergone evolutionary reduction; and several features of their fruits, such as seed locules without keels, basal portion of wings straight, acute attachment angle between mericarps, and production of some mericarps that are seedless and partially developed at maturity. Our expansion of sect. Rubra to include sect. Hyptiocarpa better elucidates the biogeographic and evolutionary history of these species. Additionally, we show that A. laurinum and A. pinnatinervium have intergrading morphology and are probably synonymous, but we note that further studies are required to conclude their taxonomic status.
Data release: "The metallicity dependence and evolutionary times of merging binary black holes: Combined constraints from individual gravitational-wave detections and the stochastic background"
<p>This data release contains the data to reproduce the results of "<strong>The metallicity dependence and evolutionary times of merging binary black holes: Combined constraints from individual gravitational-wave detections and the stochastic background</strong>" (<a href="https://arxiv.org/abs/2310.17625">arXiv:2310.17625</a>, published version <a href="https://iopscience.iop.org/article/10.3847/1538-4357/ad3d5c">here</a>).</p> <p>The code that was used to generate this data can be found on <a href="https://github.com/kevinturbang/bbh_gwb_time_delay_inference">this GitHub repository</a>. Jupyter notebooks to reproduce the figures of the paper are also included, and can be found <a href="https://github.com/kevinturbang/bbh_gwb_time_delay_inference/tree/main/figures">here</a>.</p>
Subspecies and Distribution. S. s. scrofa Linnaeus, 1758 — W Europe, from Denmark, Germany, Poland, and Czech Republic to N Italy and N Iberian Peninsula; possibly also Albania. The taxonomic status of animals in Austria, Switzerland, Slovenia, and Slovakia is unclear but presumably these populations are included in scrofa, as are the populations of Sweden, Finland, and the Baltic states. However, restocking of once depleted populations, for example in Italy, has likely involved the introduction and mixing of this subspecies with other subspecies, such as attila. S. s. affinis Gray, 1847 — S India and Sri Lanka. S. s. algirus Loche, 1867 — Tunisia, Algeria, and Morocco, on the coastal side of the mountains or in the low montane areas. S. s. attila Thomas, 1912 — Hungary, Ukraine, C & S Belarus, Romania, Moldova, and S Russia towards the N flank of the Caucasus, but not including the Transcaucasian countries of Georgia, Armenia, and Azerbaijan. The range possibly extends as far S as the Mesopotamian Delta in Iraq, in which case it would likely include W & SW Iran, and possibly E Turkey and Syria, where it borders with lybicus. Such a range could not be easily reconciled with a statement by Groves that "the difference between pigs from N and S of the Caucasus is quite striking; Transcaucasian boars are certainly not attila." This subspecies may also extend into C Asia and include Kazakhstan, Uzbekistan, and Turkmenistan, but no data exist to support this. S. s. baeticus Thomas, 1912 — originally described from Coto Donana, S Spain, and later merged with meridionalis; also S Portugal. Unless evidence is found that these Italian and Iberian populations are the relics of a much larger formerly contiguous range, this subspecies should be kept as distinct. S. s. coreanus Heude, 1897 — Korean Peninsula. S. s. eristatus Wagner, 1839 — Himalayas S to C India and E to Indochina (N of the Kra Isthmus). S. s. davidi Groves, 1981 — the arid zone from E Iran to Gujarat, including Pakistan and NW India, and perhaps N to Tajikistan. S. s. leucomystax Temminck, 1842 — main Is ofJapan (Honshu, Shikoku, Kyushu, Nakadori, Hiburijima, Tojima, Kushima, and other smaller Is). S. s. lybicus Gray, 1868 — Bulgaria, Greece, Turkey, Syria, Jordan, Israel, Palestine, in the past also in Lybia, and Egypt. The former Yugoslavia was included in its range, which would suggest that now Slovenia, Serbia, Croatia, Bosnia and Herzegovina, Montenegro, and Kosovo are within the range of this subspecies, although the exact boundaries are unclear. Pigs from Albania have been assigned to S. s. scrofa. S. s. majori De Beaux & Festa, 1927 — C & S Italian Peninsula. S. s. menidionalis Forsyth Major, 1882 — Corsica and Sardinia, with the proviso that the two populations are very likely to be introduced or feral. S. s. moupinensis Milne-Edwards, 1871 — China, S to Vietnam and W to Sichuan. S. s. nigripes Blanford, 1875 — the flanks of the Tianshan mountains in Kyrgyzstan and NW China (Xinjiang). An animal photographed in NE Iran (Golestan) looked like this subspecies. S. s. nukiuanus Kuroda, 1924 — Iriomote, Ishigaki, Okinawa, Tokunoshima, Amamioshima, and Kakerome Is in the Ryukyu chain in extreme S Japan, though some of these populations have hybridized with introduced domesticates. S. s. sibiricus Staffe, 1922 — Mongolia and Transbaikal (S & E of Lake Baikal). S. s. tawvanus Swinhoe, 1863 — Taiwan. S. s. ussuricus Heude, 1888 — far E Russia and the Manchurian region (China). Korean populations were previously included in this subspecies, but based on new evidence, the Korean taxon seems more similar to moupinensis. S. s. vittatus Boie, 1828 — Malay Peninsula, S of the Isthmus of Kra, the offshore islands of Terutai and Langkawi, Sumatra, Riau Archipelago, Java, Bali, and a range of smaller islands around these, including Babi, Bakong, Batam, Bawean, Bengkalis, Bintan, Bulan, Bunguran, Cuyo, Deli, Durian, Enggano, Galang, Jambongan, Karimon (Riau Is), Kundur, Lagong, Laut, Lingga, Lingung, Mapor, Moro Kecil, North Pagai, Nias, Panaitan, Payong, Penang, Pinie, Rupat, Siantan, Siberut, Simeulue, Singkep, Sugi, Sugi Bawa, Telibon, Tinggi, Tuangku, and the Tambelan Is. This species was originally present from the British Is in the extreme W, through Eurasia from S Scandinavia to S Siberia, extending as far E as Korea and Japan, and SE into some of the Sunda Is and Taiwan. In the S the species ranged along the Nile Valley to Khartoum, and N of the Sahara in Africa, more orless following the continental coasts of S, E, and SE Asia. Within this range it was absent only from extremely dry deserts, e.g. the driest regions of Mongolia and in China W of Sichuan; and alpine zones, such as the high altitudes of Pamir and Tien Shan. In recent centuries, the range of S. scrofa has changed dramatically because of hunting and changes in available habitat. The species disappeared from the British Is in the 17" century, from Denmark in the 19" century, and was greatly reduced in range and numbers in the 20" century from areas as distant as Tunisia, Sudan, Germany, and Russia. Following these severe declines, there were some slight population recoveries in Russia, Italy, Spain, and Germany in the mid-20™ century, and natural and assisted range expansions in Denmark and Sweden. The species has also been inadvertently reintroduced in various locations in the Great Britain via escapees of mixed origin from commercial farming enterprises. Ex-S. scrofa stocks also occur as introduced feral populations in various other parts of the world, including Australia, New Zealand, the eastern Malay Archipelago, and in North, Central, and South America. In all of these areas they are now generally recognized as a major pest. in Suidae
Subspecies and Distribution. S. s. scrofa Linnaeus, 1758 — W Europe, from Denmark, Germany, Poland, and Czech Republic to N Italy and N Iberian Peninsula; possibly also Albania. The taxonomic status of animals in Austria, Switzerland, Slovenia, and Slovakia is unclear but presumably these populations are included in scrofa, as are the populations of Sweden, Finland, and the Baltic states. However, restocking of once depleted populations, for example in Italy, has likely involved the introduction and mixing of this subspecies with other subspecies, such as attila. S. s. affinis Gray, 1847 — S India and Sri Lanka. S. s. algirus Loche, 1867 — Tunisia, Algeria, and Morocco, on the coastal side of the mountains or in the low montane areas. S. s. attila Thomas, 1912 — Hungary, Ukraine, C & S Belarus, Romania, Moldova, and S Russia towards the N flank of the Caucasus, but not including the Transcaucasian countries of Georgia, Armenia, and Azerbaijan. The range possibly extends as far S as the Mesopotamian Delta in Iraq, in which case it would likely include W & SW Iran, and possibly E Turkey and Syria, where it borders with lybicus. Such a range could not be easily reconciled with a statement by Groves that "the difference between pigs from N and S of the Caucasus is quite striking; Transcaucasian boars are certainly not attila." This subspecies may also extend into C Asia and include Kazakhstan, Uzbekistan, and Turkmenistan, but no data exist to support this. S. s. baeticus Thomas, 1912 — originally described from Coto Donana, S Spain, and later merged with meridionalis; also S Portugal. Unless evidence is found that these Italian and Iberian populations are the relics of a much larger formerly contiguous range, this subspecies should be kept as distinct. S. s. coreanus Heude, 1897 — Korean Peninsula. S. s. eristatus Wagner, 1839 — Himalayas S to C India and E to Indochina (N of the Kra Isthmus). S. s. davidi Groves, 1981 — the arid zone from E Iran to Gujarat, including Pakistan and NW India, and perhaps N to Tajikistan. S. s. leucomystax Temminck, 1842 — main Is ofJapan (Honshu, Shikoku, Kyushu, Nakadori, Hiburijima, Tojima, Kushima, and other smaller Is). S. s. lybicus Gray, 1868 — Bulgaria, Greece, Turkey, Syria, Jordan, Israel, Palestine, in the past also in Lybia, and Egypt. The former Yugoslavia was included in its range, which would suggest that now Slovenia, Serbia, Croatia, Bosnia and Herzegovina, Montenegro, and Kosovo are within the range of this subspecies, although the exact boundaries are unclear. Pigs from Albania have been assigned to S. s. scrofa. S. s. majori De Beaux & Festa, 1927 — C & S Italian Peninsula. S. s. menidionalis Forsyth Major, 1882 — Corsica and Sardinia, with the proviso that the two populations are very likely to be introduced or feral. S. s. moupinensis Milne-Edwards, 1871 — China, S to Vietnam and W to Sichuan. S. s. nigripes Blanford, 1875 — the flanks of the Tianshan mountains in Kyrgyzstan and NW China (Xinjiang). An animal photographed in NE Iran (Golestan) looked like this subspecies. S. s. nukiuanus Kuroda, 1924 — Iriomote, Ishigaki, Okinawa, Tokunoshima, Amamioshima, and Kakerome Is in the Ryukyu chain in extreme S Japan, though some of these populations have hybridized with introduced domesticates. S. s. sibiricus Staffe, 1922 — Mongolia and Transbaikal (S & E of Lake Baikal). S. s. tawvanus Swinhoe, 1863 — Taiwan. S. s. ussuricus Heude, 1888 — far E Russia and the Manchurian region (China). Korean populations were previously included in this subspecies, but based on new evidence, the Korean taxon seems more similar to moupinensis. S. s. vittatus Boie, 1828 — Malay Peninsula, S of the Isthmus of Kra, the offshore islands of Terutai and Langkawi, Sumatra, Riau Archipelago, Java, Bali, and a range of smaller islands around these, including Babi, Bakong, Batam, Bawean, Bengkalis, Bintan, Bulan, Bunguran, Cuyo, Deli, Durian, Enggano, Galang, Jambongan, Karimon (Riau Is), Kundur, Lagong, Laut, Lingga, Lingung, Mapor, Moro Kecil, North Pagai, Nias, Panaitan, Payong, Penang, Pinie, Rupat, Siantan, Siberut, Simeulue, Singkep, Sugi, Sugi Bawa, Telibon, Tinggi, Tuangku, and the Tambelan Is. This species was originally present from the British Is in the extreme W, through Eurasia from S Scandinavia to S Siberia, extending as far E as Korea and Japan, and SE into some of the Sunda Is and Taiwan. In the S the species ranged along the Nile Valley to Khartoum, and N of the Sahara in Africa, more orless following the continental coasts of S, E, and SE Asia. Within this range it was absent only from extremely dry deserts, e.g. the driest regions of Mongolia and in China W of Sichuan; and alpine zones, such as the high altitudes of Pamir and Tien Shan. In recent centuries, the range of S. scrofa has changed dramatically because of hunting and changes in available habitat. The species disappeared from the British Is in the 17" century, from Denmark in the 19" century, and was greatly reduced in range and numbers in the 20" century from areas as distant as Tunisia, Sudan, Germany, and Russia. Following these severe declines, there were some slight population recoveries in Russia, Italy, Spain, and Germany in the mid-20™ century, and natural and assisted range expansions in Denmark and Sweden. The species has also been inadvertently reintroduced in various locations in the Great Britain via escapees of mixed origin from commercial farming enterprises. Ex-S. scrofa stocks also occur as introduced feral populations in various other parts of the world, including Australia, New Zealand, the eastern Malay Archipelago, and in North, Central, and South America. In all of these areas they are now generally recognized as a major pest.
Data set for FSE 2022 Submission Program Merge Conflict Resolution via Neural Transformers
<p>Data set for FSE 2022 Submission Program Merge Conflict Resolution via Neural Transformers</p>
IMD New High Spatial Resolution (0.25X0.25 degree) Long Period (1901-2022) Daily Gridded Rainfall Data Set Over India: Matlab script for merging into single netcdf/mat file with datetime stamping
Open the record for dataset details and reuse information.
Data for "Expanding the scope of a catalogue search to bioisosteric fragment merges using a graph database approach"
<p>Data for "Expanding the scope of a catalogue search to bioisosteric fragment merges using a graph database approach"</p> <p>Contains files with the SMILES retrieved from the database (fragnet_query_outputs.zip) and sdf files containing the full lists of scored compounds for Fragalysis target test cases (scored_output_sdfs.zip). The compound sets used in the bioisosteric and perfect comparisons are saved in a separate directory.</p>
Data for "The use of a graph database is a complementary approach to a classical similarity search for identifying commercially available fragment merges"
<p>The input and output data to the filtering pipeline described in "The use of a graph database is a complementary approach to a classical similarity search for identifying commercially available fragment merges"; code available from https://github.com/oxpig/fragment_network_merges. </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.