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Data belonging to: Teurlincx, S., Verhofstad, M. J., Bakker, E. S., & Declerck, S. A. (2018). Managing successional stage heterogeneity to maximize landscape-wide biodiversity of aquatic vegetation in ditch networks. Frontiers in plant science, 9, 1013.
<p>Data belonging to the paper Teurlincx, S., Verhofstad, M. J., Bakker, E. S., & Declerck, S. A. (2018). Managing successional stage heterogeneity to maximize landscape-wide biodiversity of aquatic vegetation in ditch networks. Frontiers in plant science, 9, 1013.</p> <p>Data includes analysis scripts (R Language) and all used data files. Data is composed of location information of the different sites, environmental conditions on site and vegetation composition.</p>
Data files belonging to the paper "Dealing with clustered samples for assessing map accuracy by cross-validation"
<p>Mapping of environmental variables often relies on map accuracy assessment through cross-validation with the data used for calibrating the underlying mapping model. When the data points are spatially clustered, conventional cross-validation leads to optimistically biased estimates of map accuracy. Several papers have promoted spatial cross-validation as a means to tackle this over-optimism. Many of these papers blame spatial autocorrelation as the cause of the bias and propagate the widespread misconception that spatial proximity of calibration points to validation points invalidates classical statistical validation of maps. In the paper related to these data, we present and evaluate alternative cross-validation approaches for assessing map accuracy from clustered sample data. </p> <p> </p> <p>The study area is western Europe, constrained in the north at 52° latitude and at -10° and 24° longitude The projection is IGNF:ETRS89LAEA (Lambert azimuthal equal area projection).</p> <p> </p> <p><strong>Files:</strong></p> <p>agb.tif = above ground biomass (AGB) map from version 3 of the 2017 CCI-Biomass product (<a href="https://catalogue.ceda.ac.uk/uuid/5f331c418e9f4935b8eb1b836f8a91b8">https://catalogue.ceda.ac.uk/uuid/5f331c418e9f4935b8eb1b836f8a91b8</a>)<br> AGBstack.tif = covariates used for predicting AGB<br> aggArea.tif = coarse grid used for simulation in the model-based methods<br> ocs.tif = soil organic carbon stock (OCS) map (0-30 cm) from Soilgrids (<a href="https://www.isric.org/explore/soilgrids">https://www.isric.org/explore/soilgrids</a>)<br> OCSstack.tif = covariates used for predicting OCS<br> strata.xxx = 100 compact geo-strata (ESRI shape) created with the spcosa package; used for generating clustered samples<br> TOTmask.tif = mask of the area covered by the covariates</p> <p> </p> <p><strong>Details and data sources of the covariates in AGBstack.tif and OCSstack.tif:</strong></p> <table> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Description</strong></p> </td> <td> <p><strong>Source</strong></p> </td> <td> <p><strong>Note</strong></p> </td> </tr> <tr> <td> <p>ai</p> </td> <td> <p>Aridity Index</p> </td> <td> <p><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></p> </td> <td>Version 2.1</td> </tr> <tr> <td> <p>bio1</p> </td> <td> <p>Mean annual air temperature [°C]</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>bio5</p> </td> <td> <p>Mean daily maximum air temperature of the warmest month [°C]</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>bio7</p> </td> <td> <p>Annual range of air temperature [°C]</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>bio12</p> </td> <td> <p>Annual precipitation [kg/m<sup>2</sup>]</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>bio15</p> </td> <td> <p>Precipitation seasonality [kg/m<sup>2</sup>]</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>gdd10</p> </td> <td> <p>Growing degree days heat sum above 10°C</p> </td> <td><a href="https://chelsa-climate.org/downloads/">https://chelsa-climate.org/downloads/</a></td> <td>Version 2.1</td> </tr> <tr> <td> <p>clay</p> </td> <td> <p>Clay content [g/kg] of the 0-5cm layer</p> </td> <td> <p><a href="https://soilgrids.org/">https://soilgrids.org/</a></p> <p> </p> </td> <td> <p>Only used for AGB</p> </td> </tr> <tr> <td> <p>sand</p> </td> <td> <p>Sand content [g/kg] of the 0-5cm layer</p> </td> <td><a href="https://soilgrids.org/">https://soilgrids.org/</a></td> <td>as above</td> </tr> <tr> <td> <p>pH</p> </td> <td> <p>Acidity (Ph(water)) of the 0-5cm layer</p> </td> <td><a href="https://soilgrids.org/">https://soilgrids.org/</a></td> <td>as above</td> </tr> <tr> <td> <p>glc2017</p> </td> <td> <p>Landcover 2017</p> </td> <td> <p><a href="https://land.copernicus.eu/global/products/lc">https://land.copernicus.eu/global/products/lc</a>, reclassified to: closed forest, open forest, natural non-forest veg., bare & sparse veg. cropland, built-up, water</p> </td> <td> <p>Categorical variable</p> </td> </tr> <tr> <td> <p>dem</p> </td> <td> <p>Elevation</p> </td> <td> <p><a href="https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-eu-dem">https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-eu-dem</a></p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p>cosasp</p> </td> <td> <p>Cosine of slope aspect</p> </td> <td> <p>Computed with the terra package from elevation</p> </td> <td>Computed @25m resolution; next aggregated to 0.5km</td> </tr> <tr> <td> <p>sinasp</p> </td> <td> <p>Sine of slope aspect</p> </td> <td>Computed with the terra package from elevation</td> <td>as above</td> </tr> <tr> <td> <p>slope</p> </td> <td> <p>Slope</p> </td> <td>Computed with the terra package from elevation</td> <td>as above</td> </tr> <tr> <td> <p>TPI</p> </td> <td> <p>Topographic position index</p> </td> <td>Computed with the terra package from elevation</td> <td>as above</td> </tr> <tr> <td> <p>TRI</p> </td> <td> <p>Terrain ruggedness index</p> </td> <td>Computed with the terra package from elevation</td> <td>as above</td> </tr> <tr> <td> <p>TWI</p> </td> <td> <p>Topographic wetness index</p> </td> <td> <p>Computed with SAGA from 500m resolution (aggregated) dem</p> </td> <td> </td> </tr> <tr> <td> <p>gedi</p> </td> <td> <p>Forest height</p> </td> <td> <p><a href="https://glad.umd.edu/dataset/gedi">https://glad.umd.edu/dataset/gedi</a></p> </td> <td> <p>Zone: NAFR</p> </td> </tr> <tr> <td> <p>xcoord</p> </td> <td> <p>X coordinate</p> </td> <td> <p>Using a mask created from the other covariates</p> </td> <td> </td> </tr> <tr> <td> <p>ycoord</p> </td> <td> <p>Y coordinate</p> </td> <td>Using a mask created from the other covariates</td> <td> </td> </tr> <tr> <td> <p>Dcoast</p> </td> <td> <p>Distance from coast</p> </td> <td> <p>Using a land mask created from the other covariates</p> </td> <td> </td> </tr> </tbody> </table> <p> </p>
Numerical Data Set Belonging to: 'Numerical Study of Phase-Change Phenomena: A Conservative Linearized Enthalpy Approach'
<p>This is the numerical data set belonging to the Nureth conference paper entitled: 'Numerical Study of Phase-Change Phenomena: A Conservative Linearized Enthalpy Approach'. </p> <p>The files 'Stefan_singlePhase_Tfield.dat' and 'Stefan_singlePhase_interface.dat' represent the raw data belonging to figure 1 in the paper and contain the solution to the one-phase Stefan problem for the temperature field and interface position (section 3.1 in the paper). The files 'Stefan_singlePhase_error.dat' and 'Stefan_twoPhase_error.dat' represent the raw data belonging to figure 2 in the paper and contain the L2 norm of the relative difference between the numerical and analytical solution for the single and two phase Stefan problem respectively. </p> <p>The files 'Gau_1140s_lf_50x50_3D', 'Gau_1140s_lf_100x100_3D', 'Gau_1140s_lf_200x200_3D' feature the raw OpenFOAM(v7) data containing the numerical solution to the liquid fraction of the Gallium melting in a rectangular enclosure problem (Gau, 1986) at 1140s of simulation time. These data were used for the mesh convergence study (figure 3, section 3.2). </p> <p>The files 'Gau_120s_U_200x200_3D', 'Gau_360s_U_200x200_3D', 'Gau_750s_U_200x200_3D', 'Gau_1140s_U_200x200_3D' feature the raw OpenFOAM(v7) data containing the 3-dimensional numerical solution to the velocity of the Gallium melting in a rectangular enclosure problem (Gau, 1986) at respectively 120s, 360s, 750s and 1140s of simulation time. These data underly the velocity colours shown in figure 4 and figure 6 (section 3.2).</p> <p>Likewise, the files 'Gau_120s_U_200x200_2D' and 'Gau_360s_U_200x200_2D' feature the raw OpenFOAM(v7) data containing the 2-dimensional numerical solution to the velocity of the Gallium melting in a rectangular enclosure problem. These data underly the velocity colours shown in figure 5 (section 3.2).</p> <p> </p> <p> </p>
Potential Metabolic Activity, Catalase Activity, Performance traits and Morphological variables of 94 individuals belonging to Podarcis muralis species used in the analysis
<p>Potential Metabolic Activity (ETS26_P, ETS31_P, ETS36_P), Catalase Activity (CAT_P), Performance traits (BITE, SPRINT,CLIMB, MANO) and Morphological variables (snout-vent length (SVL), trunk length (TRL), pileus length (PL), head length (HL), head width (HW), head height (HH), fore limb length (FLL) and hind limb length (HLL) of 94 individuals belonging to <em>Podarcis muralis</em> species. The data was used in the analysis of the paper entitled: Is It Function or Fashion? An Integrative Analysis of Morphology, Performance, and Metabolism in a Colour Polymorphic Lizard, by authors Verónica Gomes, Anamarija Žagar, Guillem Pérez i de Lanuza, Tatjana Simčič and Miguel A. Carretero, published in the journal Diversity 2022, 14, 116. <a href="https://doi.org/10.3390/d14020116">https://doi.org/10.3390/d14020116</a></p>
Datasets belonging to the paper "Dual phase patterning during a congruent grain boundary phase transition in elemental copper"
<p>This repository contains the raw data of the experimental STEM imaging and the data corresponding to the simulations and theoretical calculations of the paper "Dual phase patterning during a congruent grain boundary phase transition in elemental copper" available under <a href="https://doi.org/10.1038/s41467-022-30922-3">https://doi.org/10.1038/s41467-022-30922-3</a> .</p> <p>See the file README.md for a detailed description.</p>
Fig. 8 in Two New Species of Composetia (Annelida: Nereididae) from Small Estuaries in the Ryukyu Islands, Southern Japan, with a List of All Species Currently Belonging to Composetia
Fig. 8. Chaetae of Composetia tokashikiensis sp. nov. A–C, chaetae in parapodium 5 of the holotype (NSMT-Pol H-774): A, homogomph spiniger of notochaetae; B, heterogomph spiniger with short blade from upper neurochaetal bundle; C, heterogomph spiniger with long blade from lower neurochaetal bundle (upper position). D, E, chaetae in parapodium 43 of paratype (NSMT-Pol P-775): D, heterogomph spiniger with short blade from lower neurochaetal bundle (lower position); E, heterogomph falciger from lower neurochaetal bundle. Scale bar: 0.05 mm.
Fig. 5 in Two New Species of Composetia (Annelida: Nereididae) from Small Estuaries in the Ryukyu Islands, Southern Japan, with a List of All Species Currently Belonging to Composetia
Fig. 5. Landscape of the type locality of Composetia kumensis sp. nov. at the uplifted coral reef at Gushicha Gusuku on Kume-jima island (photographed on 22 November 2013). A, overview of the uplifted coral reef around the sampling site; B, the sampling site in a small creek originating from a freshwater spring (arrow) in the upper intertidal zone of the uplifted coral reef, surrounded by saltmarsh vegetation. Scale bar in B: 1 m.
Fig. 3 in Two New Species of Composetia (Annelida: Nereididae) from Small Estuaries in the Ryukyu Islands, Southern Japan, with a List of All Species Currently Belonging to Composetia
Fig. 3. Chaetae in chaetiger 20 of paratype (NSMT-Pol P-771) of Composetia kumensis sp. nov. A, homogomph spiniger from notochaetae; B, heterogomph spiniger from lower neurochaetae; C, heterogomph falciger from upper neurochaetae. Scale bar: 0.05 mm.
Fig. 7 in Two New Species of Composetia (Annelida: Nereididae) from Small Estuaries in the Ryukyu Islands, Southern Japan, with a List of All Species Currently Belonging to Composetia
Fig. 7. Composetia tokashikiensis sp. nov. A–C, paratype (NSMT-Pol P-775): A, dorsal views of prostomium and peristomium; B, dorsal view of the everted proboscis; C, ventral view of the everted proboscis. D–G, holotype (NSMT-Pol H-774): D, posterior view of right parapodium 1; E, posterior view of right parapodium 5; F, anterior view of right parapodium 5; G, posterior view of right parapodium 32. H, posterior view of right parapodium 51 of the paratype (NSMT-Pol P-775). Arrow indicates a notoacicular process. Abbreviations: g, glandular patch; i, neuropodial inferior lobe; ne, neuroacicula; no, notoacicula; p, neuropodial postchaetal lobe. Scale bars: 1 mm (A–C); 0.1 mm (D–H).
Fig. 4 in Two New Species of Composetia (Annelida: Nereididae) from Small Estuaries in the Ryukyu Islands, Southern Japan, with a List of All Species Currently Belonging to Composetia
Fig. 4. Schematic diagrams of chaetal arrangement in distal view of right parapodium around chaetiger 5. A, Composetia kumensis sp. nov. B, C. tokashikiensis sp. nov. Closed circles: homogomph spinigers. Closed squares: heterogomph spinigers. Closed stars: heterogomph falcigers. Asterisks indicate that few heterogomph falcigers are sometimes present. Abbreviations: dc, dorsal cirrus; i, neuropodial inferior lobe; ne, neuroacicula; nea, neuropodial acicular ligule; nev, neuropodial ventral ligule; no, notoacicula; nod, notopodial dorsal ligule; np, notoacicular process; nov, notopodial ventral ligule; po, neuropodial postchaetal lobe; vc, ventral cirrus.
Fig. 6 in Two New Species of Composetia (Annelida: Nereididae) from Small Estuaries in the Ryukyu Islands, Southern Japan, with a List of All Species Currently Belonging to Composetia
Fig. 6. Composetia tokashikiensis sp. nov. A, dorsal view of the whole body of the preserved specimen of holotype (NSMT-Pol H-774). Arrow indicates the enlarged oral ring of the everted proboscis. B–D, anterior end of a paratype (NSMT-Pol P-783): B, dorsal view of prostomium, peristomium, and anterior chaetigers; C, dorsal view of the everted proboscis; D, ventral view of the everted proboscis. E, landscape of the type locality at the upper reaches of a small estuary in the Tokashiki-gawa river in Tokashiki-jima island (photographed on 27 May 2012). Scale bars: 1 mm (A); 0.5 mm (B–D).
Fig. 1 in Two New Species of Composetia (Annelida: Nereididae) from Small Estuaries in the Ryukyu Islands, Southern Japan, with a List of All Species Currently Belonging to Composetia
Fig. 1. Composetia kumensis sp. nov. A, dorsal view of the whole body of the preserved specimen of holotype (NSMT-Pol H-766). Arrow indicates the enlarged oral ring of the everted proboscis. B, C, paratype (NSMT-Pol P-772): B, dorsal view of the anterior body of a live specimen; C, dorsal view of anterior end of the preserved specimen. D, Jaw of paratype (NSMT-Pol P-773). Scale bars: 1 mm (A, B); 0.5 mm (C); 0.1 mm (D).
Figures 9–10 in Studies on palearctic Onthophagus associated with burrows of small mammals. IV. A new Iranian species belonging to the furciceps group (Coleoptera, Scarabaeidae, Onthophagini)
Figures 9–10. Onthophagus (Paleonthophagus) psychopompus sp. n. Male, paratype (Iran, Sirdan, Qazvin prov.) and female, paratype (Iran, Saqqez, Kordestan prov.). 9 Dorsum of male 10 Dorsum of female. Photos by A. Ballerio, scanned by G. Fiumi.
Figures 1–7 in Studies on palearctic Onthophagus associated with burrows of small mammals. IV. A new Iranian species belonging to the furciceps group (Coleoptera, Scarabaeidae, Onthophagini)
Figures 1–7. Onthophagus (Paleonthophagus) psychopompus sp. n. Male, holotype, and female, allotype (Iran, Hashtgerd, Tehran prov.). 1 Male: head and pronotum, dorsal view 2 Male: head, frontal view 3 Female: head and pronotum, dorsal view 4 Female: head, frontal view 5 Parameres, lateral view 6 Parameres, dorsal view 7 Lamella copulatrix, ventral side. Drawings by I. Gudenzi.
Figs 2-4 in Xanthopygoides niger Cameron, 1951 (Xanthopygina) belongs to the genus Philonthus Stephens, 1829 (Philonthina): systematic and nomenclatural changes for the African Staphylinini (Coleoptera, Staphylinidae, Staphylininae, Staphylinini)
Figs 2-4. Philonthus neoniger Solodovnikov, nom. nov. Details of structure: 2, prothorax (in vertral view, only left side illustrated, anterior leg removed); 3, aedeagus in dorsal (parameral) view (internal sac evert-ed); 4, aedeagus in lateral view (internal sac everted). Scale bar 1 mm.
Dataset and trained models belonging to the article 'Distant reading patterns of iconicity in 940.000 online circulations of 26 iconic photographs'
<p>Quantifying Iconicity - Zenodo</p> <p><br> ## The Dataset<br> This dataset contains the material collected for the article "Distant reading 940,000 online circulations of 26 iconic photographs" (to be) published in New Media & Society (DOI: 10.1177/14614448211049459). We identified 26 iconic photographs based on earlier work (Van der Hoeven, 2019). The Google Cloud Vision (GCV) API was subsequently used to identify webpages that host a reproduction of the iconic image. The GCV API uses computer vision methods and the Google index to retrieve these reproductions. The code for calling the API and parsing the data can be found on GitHub: https://github.com/rubenros1795/ReACT_GCV.</p> <p>The core dataset consists of .tsv-files with the URLs that refer to the webpages. Other metadata provided by the GCV API is also found in the file and manually generated metadata. This includes:<br> - the URL that refers specifically to the image. This can be an URL that refers to a full match or a partial match<br> - the title of the page<br> - the iteration number. Because the GCV API puts a limit on its output, we had to reupload the identified images to the API to extend our search. We continued these iterations until no more new unique URLs were found<br> - the language found by the ``langid`` Python module [link](https://github.com/saffsd/langid.py), along with the normalized score.<br> - the labels associated with the image by Google<br> - the scrape date</p> <p>Alongside the .tsv-files, there are several other elements in the following folder structure:</p> <p>```<br> ├── data<br> │ ├── embeddings<br> │ └── doc2vec<br> │ └── input-text<br> │ └── metadata<br> │ └── umap<br> │ └── evaluation<br> │ └── results<br> │ └── diachronic-plots<br> │ └── top-words<br> │ └── tsv<br> ```</p> <p>1. The ```/embeddings``` folder contains the doc2vec models, the training input for the models, the metadata (id, URL, date) and the UMAP embeddings used in the GMM clustering. Please note that the date parser was not able to find dates for all webpages and for this reason not all training texts have associated metadata.<br> 2. The ```/evaluation``` folder contains the AIC and BIC scores for GMM clustering with different numbers of clusters.<br> 3. The ```/results``` folder contains the top words associated with the clusters and the diachronic cluster prominence plots.</p> <p>## Data Cleaning and Curation<br> Our pipeline contained several interventions to prevent noise in the data. First, in between the iterations we manually checked the scraped photos for relevance. We did so because reuploading an iconic image that is paired with another, irrelevant, one results in reproductions of the irrelevant one in the next iteration. Because we did not catch all noise, we used Scale Invariant Feature Transform (SIFT), a basic computer vision algorithm, to remove images that did not meet a threshold of ten keypoints. By doing so we removed completely unrelated photographs, but left room for variations of the original (such as painted versions of Che Guevara, or cropped versions of the Napalm Girl image). Another issue was the parsing of webpage texts. After experimenting with different webpage parsers that aim to extract 'relevant' text it proved too difficult to use one solution for all our webpages. Therefore we simply parsed all the text contained in commonly used html-tags, such as ```<p>```, ```<h1>``` etc.</p>
Figure 10. Spurilla neapolitana. A in The family Aeolidiidae Gray, 1827 (Gastropoda Opisthobranchia) from Brazil, with a description of a new species belonging to the genus Berghia Trinchese, 1877
Figure 10. Spurilla neapolitana. A, radular teeth (18-mm-long specimen, from Praia de Manguihos); scale bar = 100 Mm. B, smaller radular teeth of the same specimen; scale bar = 50 Mm. C, central cusp of some teeth. D, jaws; scale bar = 0.5 mm.
Figure 9 in The family Aeolidiidae Gray, 1827 (Gastropoda Opisthobranchia) from Brazil, with a description of a new species belonging to the genus Berghia Trinchese, 1877
Figure 9. Spurilla neapolitana: SEM images of the radula and jaws. A, radula of a specimen from Buzios; scale bar = 100 Mm. B, detail of the tooth of a specimen from Buzios; scale bar = 20 Mm. C, jaw of a specimen from El Grove; scale bar = 100 Mm. D, radula of a specimen from Huelva; scale bar = 10 Mm. E, detail of the tooth of specimen from Huelva; scale bar = 10 Mm. F, radula of a specimen from El Grove; scale bar = 100 Mm.
Figure 8. Living animals. A, B, Berghia verruciconnis from Huelva. C, Berghia columbina from Huelva. D, E, Spurilla neapolitana from Huelva. F in The family Aeolidiidae Gray, 1827 (Gastropoda Opisthobranchia) from Brazil, with a description of a new species belonging to the genus Berghia Trinchese, 1877
Figure 8. Living animals. A, B, Berghia verruciconnis from Huelva. C, Berghia columbina from Huelva. D, E, Spurilla neapolitana from Huelva. F, Spurilla neapolitana from El Grove.
Figure 7 in The family Aeolidiidae Gray, 1827 (Gastropoda Opisthobranchia) from Brazil, with a description of a new species belonging to the genus Berghia Trinchese, 1877
Figure 7. SEM images of the radulae and jaws. A, radula of Berghia marcusi sp. nov. (12-mm-long specimen, from Praia de Aramaçao); scale bar = 20 Mm. B, masticatory border of the jaw of Berghia marcusi sp. nov. (12-mm-long specimen, from Praia de Aramaçao); scale bar = 5 Mm. C, D, radula of Berghia verrucicornis from Huelva; scale bar = 10 Mm. E, radula of Berghia columbina from Huelva; scale bar = 10 Mm. F, jaw of Berghia columbina from Huelva; scale bar = 100 Mm.
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.