Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
3,136
datasets available to search
ShareScore release 0.9.0
Dataset results
3,136 results for “Terrestrialization”
Fig. 2 in Terrestrial Molluscs Of The Tsyr-Pripyat Area In Volyn (Northern Ukraine): The First Findings Of The Threatened Snail Vertigo Moulinsiana In Mainland Ukraine
Fig. 2. Shells of Vertigo moulinsiana from the vicinities of Velyka Glusha (plot 10), scale bar 1 mm.
Supporting information for the paper: Terrestrial Gamma-ray Flashes with Accompanying Elves Detected by ASIM
<p>Supporting data to the paper "Terrestrial Gamma-ray Flashes with Accompanying Elves Detected by ASIM"</p>
Global terrestrial water storage reconstruction using cyclostationary empirical orthogonal functions (1979 - present)
<p>The dataset consists of a 1979-present reconstruction of Terrestrial Water Storage (TWS) anomalies from Gravity Recovery and Climate Experiment (GRACE) using common modes of variability between TWS and terrestrial precipitation and land surface temperatures. </p>
Supplementary data to: Climate change threatens terrestrial water storage over the Tibetan Plateau
<p>This data archive includes the boundary of the Tibetan Plateau (TP) and river basins, and projected changes in terrestrial water storage by the mid-21<sup>st</sup> century (up to 2060). The boundary of the TP and river basins is in the shapefile (.shp) format, and all other processed data are in the geotiff (.tif) format. Please see Readme for more data information. For calculation details please see the publication.</p>
WWLLN Datasets for "A Terrestrial Gamma-ray Flash from the 2022 Hunga Tonga–Hunga Ha'apai Volcanic Eruption"
<p>These data files contain data used in the paper "A Terrestrial Gamma-ray Flash from the 2022 Hunga Tonga–Hunga Ha’apai Volcanic Eruption", M. S. Briggs, S. Lesage, C. Schultz, B. Mailyan, R. H. Holzworth, Geophysical Research Letters, 2022.</p> <p>The authors wish to thank the World Wide Lightning Location Network (WWLLN), a collaboration among over 50 universities and institutions, for providing the lightning location data used in these datasets and in the paper. Additional WWLLN data are available at nominal cost from http://wwlln.net.</p> <p>The file named Fig_1.txt contains the data used to generate Figure 1 in the paper.</p> <p>The first two columns list the time ranges for each histogram bin, in UTC on 2022 January 15, while the final column lists the lightning detection rate, in counts per minute, for all WWLLN sferics located within a 400 km radius of the Hunga Tonga–Hunga Ha’apai volcano.</p> <p>The times when Fermi passed within 1000 km of the volcano, shown as grey bars in Figure 1, are:<br> 03:47:58.5 to 03:52:56.2 UTC<br> 05:29:25.1 to 05:33:59.7 UTC<br> 07:11:04.0 to 07:15:18.3 UTC<br> 08:52:04.8 to 08:57:05.1 UTC<br> 10:33:48.1 to 10:37:32.7 UTC</p> <p>The time of the Fermi TGF detection, shown as a red line in Figure 1, is:<br> 08:52:40.011500 UTC</p> <p><br> The file named Fig_2.txt contains the WWLLN sferic data used to generate Figure 2 in the aforementioned paper.</p> <p>This file has the same format as the text files for the WWLLN maps provided in the Fermi GBM TGF catalog, https://fermi.gsfc.nasa.gov/ssc/data/access/gbm/tgf/.</p> <p>Line 1 is the network_name<br> Line 2 is TGF_name<br> Line 3 is the coordinates of Fermi at the time of the TGF (2022-01-15 08:52:40.011500 UTC).<br> Line 4 is the coordinates of the center of the map<br> The second number on line 5 is the number of sferics in a +/- 1 minute interval about the TGF.<br> The remaining 104 lines list the properties of each sferic in columns containing the following information:<br> sequence_number, longitude, latitude, time_separation_between_sferic_and_TGF_corrected_for_light-travel-time</p> <p>The two GLM lightning flashes, shown as magenta dots in Figure 2, have longitude and latitude values:<br> -175.27394, -20.9348<br> -175.29301, -20.8466</p> <p>All of the aforementioned longitudes are East longitudes.<br> </p>
Flood Detection Using GRACE Terrestrial Water Storage and Extreme Precipitation
<p>The flood day products were derived from GRACE Terrestrial Water Storage and Extreme Precipitation. We used GRACE terrestrial water storage and precipitation data combined with high-frequency filtering, anomaly detection and flood potential index methods to successfully extract historical flood days globally between Apr. 1st, 2002, and Aug. 31st, 2016, and further compared and validated the results with Dartmouth Flood Observatory (DFO) data, Global Runoff Data Centre (GRDC) discharge data, news reports and social media data. The results showed that GRACE-based flood days could cover 81% of the flood events in the DFO database, 87% of flood events extracted by MODIS and supplement many additional flood events not recorded by the DFO. Moreover, the probability of detection greater than or equal to 0.5 reached 62% among 261 river basins compared to flood events derived from the GRDC discharge data. These detection capabilities and detection results are both good. We finally provided flood day products with 1° spatial resolution covering the range of 60°S—60°N from Apr. 1st, 2002, to Aug. 31st, 2016. This research provides a data foundation for the mechanistic analysis and attribution of global flood events.</p>
Impacts of abiotic and biotic factors on terrestrial leeches in
<p>Haemadipsid leeches are ubiquitous inhabitants of tropical and sub-tropical forests in the Indo-Pacific region. They are increasingly used as indicator taxa for biomonitoring, yet very little is known about their basic ecology. For example, to date no study has assessed the occurrence and distribution of haemadipsid leeches across naturally occurring gradients within intact habitats. We analysed a long-term data set (2012-2020) on the closely related tiger (Haemadipsa picta) and brown (Haemadipsa spp.) leech species to investigate if and how abiotic and biotic factors influence their occurrence across a gradient of forest types at an undisturbed tropical rainforest site in Indonesian Borneo. We compared a series of negative binomial mixed models and found that, of the abiotic factors, soil moisture had the largest positive effect on encounter rates of both leech species. Among biotic factors, forest type had differential effects on counts of the two species: while tiger leech counts were greater in low elevation forest types, brown leech counts were greater in high elevation forest types. Additionally, we found that the presence of one species had a positive effect on the presence of the other species. Finally, our results show that the tiger leech has a narrower distribution, being restricted to lower elevation forest types with higher water retention, suggesting that the tiger leech could be more sensitive to lower soil moisture levels.</p>
Serravallian terrestrial climate data for Europe
<p>Sheet 1 - Mean Annual Temperature (MAT) and Mean Annual Precipitation (MAP) data with uncertainty and latitude-longitude</p> <p>Sheet 2 - References for this data</p>
An ensemble of 48 perturbed-physics model (Noah-MP) estimates of the 1/8° terrestrial water storage over the conterminous United States, 1980–2015 (time-merged version)
<p>This dataset contains the 1980–2015 monthly terrestrial water storage simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include the total terrestrial water storage and its constituents (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The file name has four parts: the variable collection, the used parameterization, the time period, the suffix, and the compression format.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
Supporting information for the paper: The temporal relationship between Terrestrial Gamma-ray flashes and associated optical pulses from lightning
<p>Supporting information for the paper: The temporal relationship between Terrestrial Gamma-ray flashes and associated optical pulses from lightning, consisting of 2 data files and 221 presentations of TGF-Optical emission events observed by ASIM between end of March 2019 and November 2020.</p> <p>See 0_READ_ME for information about the individual files and variables.</p>
Population models used in: Method to assess potential magnitude of terrestrial European avian population reductions from ingestion of lead ammunition
<p>Current estimates of terrestrial bird losses across Europe from ingestion of lead ammunition are based on uncertain or generic assumptions. A method is needed to develop defensible European-specific estimates compatible with available data that does not require long-term field studies. We propose a 2-step method using carcass data and population models. The method estimates percentage of deaths diagnosed as directly caused by lead poisoning as a lower bound and, as an upper bound, the percentage of possible deaths from sublethal lead poisoning that weakens birds, making them susceptible to death by other causes. We use these estimates to modify known population-level annual mortality. Our method also allows for potential reductions in reproduction from lead shot ingestion because reductions in survival and reproduction are entered into population models of species with life histories representative of the most groups of susceptible species. The models estimate the sustainability and potential population decreases from lead poisoning in Europe. Using the best available data, we demonstrate the method on two taxonomic groups of birds: gallinaceous birds and diurnal raptors. The direction of the population trends affects the estimate, and we incorporated such trends into the method. Our midpoint estimates of the reduction in population size of the European gallinaceous bird (< 2%) group and raptor group (2.9 – 7.7%) depend on the species life history, maximum growth rate, population trend, and if reproduction is assumed to be reduced. Our estimates can be refined as more information becomes available in countries with data gaps. We advocate use of this method to improve upon or supplement approaches currently being used. As we demonstrate, the method also can be applied to individual species of concern if enough data across countries are available.</p>
An ensemble of 48 physically perturbed model estimates of the 1/8° terrestrial water budget over the conterminous United States, 1980–2015
<p>This dataset contains the 1980–2015 monthly terrestrial water budget simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration), runoff (the surface and subsurface components), as well as terrestrial water storage (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The file name has four parts: the abbreviation for "terrestrial water budget", the used parameterization, the time scale, and the suffix.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
Figure 1 in Terrestrial Parasitengona mites (Trombidiformes) of Denmark - new data on parasite-host associations and new country records
Figure 1 Erythraeid larvae (Parasitengona: Erythraeidae) parasitizing various hosts: A – Charletonia cardinalis* on Stiroma affinis(Hemiptera: Delphacidae); B – [?] Leptus mariae* on Brachysomus echinatus(Coleoptera: Curculionidae). Not to scale. *Larvae depigmented due to preservation in EtOH.
Figure 2 in Terrestrial Parasitengona mites (Trombidiformes) of Denmark - new data on parasite-host associations and new country records
Figure 2 Trombidiid larvae (Parasitengona: Trombidiidae) parasitizing various hosts: A – Paratrombium egregrium* on Pachyneuron groenlandicum(Hymenoptera: Pteromalidae); B –Trombidium holosericeum on Pinalitus viscicola(Hemiptera: Miridae); C –T. holosericeum* on
Fig. 4 in Gyliotrachela cultura, a new species of terrestrial microsnail (Gastropoda: Eupulmonata: Vertiginidae) from Thailand
Fig. 4. Genital system of Gyliotrachela cultura, new species. A, schematic drawing of genital system; B, genital system (paratype, NHLRU012). Abbreviations: ag = albumin gland; at = atrium; e = epiphallus; erc = epiphallic retractor caecum; fo = free oviduct; hd = hermaphroditic duct; p = penis; pr = penial retractor muscle; pro = prostate; gs = gametolytic sac; ut = uterus; v = vagina; vd = vas deferens.
Fig. 3 in Gyliotrachela cultura, a new species of terrestrial microsnail (Gastropoda: Eupulmonata: Vertiginidae) from Thailand
Fig. 3. Radula morphology of Gyliotrachela cultura, new species, paratype (NHLRU012). Abbreviations: C = central tooth; L = lateral teeth; M = marginal teeth.
Fig. 2. Gyliotrachela cultura, new species. A–F in Gyliotrachela cultura, a new species of terrestrial microsnail (Gastropoda: Eupulmonata: Vertiginidae) from Thailand
Fig. 2. Gyliotrachela cultura, new species. A–F, holotype (NHLRU011), apertural view (A), protoconch (B, C), basal view (D), lateral view (E), and apertural teeth detail (F). G–L, paratype (ZRCBUU 0750), apertural view (G), protoconch (H, I), basal view (J), lateral view (K), and apertural teeth detail (L). Abbreviations for apertural teeth: P = parietal lamella; A = angular lamella; T = twin; SupPL = suprapalatal plica; UPL = upper palatal plica; IPL = interpalatal plicae; LPL = lower palatal plica; InPL = infrapalatal plica; SubC = subcollumellar lamella; C = columellar lamella; SupC = supracolumellar plica; Infp = infraparietal lamella.
Fig. 1 in Gyliotrachela cultura, a new species of terrestrial microsnail (Gastropoda: Eupulmonata: Vertiginidae) from Thailand
Fig. 1. Map of Southeast Asia showing the type localities of the known Gyliotrachela spp. The numbers correspond to the species numbers listed in Table 1.
Text-fig. 1. a: Po Plain and foothills of the Northern Apennine in Northern Italy (inset) with the location of Oriolo (black star) and other Early and Middle Pleistocene plant localities, Enza and Stirone. Red lines indicate the frontal thrust arcs (modified from Martinetto et al. 2015). b: The "La Salita" section, Oriolo and chronology of the two "Sabbie gialle" cycles based on large mammals and palaeomagnetic correlation (modified from Toniato et al. 2017; IMMS 2020* [Italian Mediterranean Marine Stages] updated from Cohen and Gibbars 2020; GTS 2021* [Global Time Scale] updated from Head et al. 2021). c: Quarry "La Salita", Oriolo, in 1987. Main unconformities (U) separating the two "Sabbie gialle" cycles and terrestrial deposits on top are shown. Leaf symbols indicate the positions of some of the layers rich in fossil leaves (photo by G. B. Vai, modified). d: Surroundings of Faenza with the location of Oriolo and adjacent coeval sites yielding plant macrofossils. in The Late Early Pleistocene Flora Of Oriolo, Faenza (Italy): Assembly Of The Modern Forest Biome
Text-fig. 1. a: Po Plain and foothills of the Northern Apennine in Northern Italy (inset) with the location of Oriolo (black star) and other Early and Middle Pleistocene plant localities, Enza and Stirone. Red lines indicate the frontal thrust arcs (modified from Martinetto et al. 2015). b: The "La Salita" section, Oriolo and chronology of the two "Sabbie gialle" cycles based on large mammals and palaeomagnetic correlation (modified from Toniato et al. 2017; IMMS 2020* [Italian Mediterranean Marine Stages] updated from Cohen and Gibbars 2020; GTS 2021* [Global Time Scale] updated from Head et al. 2021). c: Quarry "La Salita", Oriolo, in 1987. Main unconformities (U) separating the two "Sabbie gialle" cycles and terrestrial deposits on top are shown. Leaf symbols indicate the positions of some of the layers rich in fossil leaves (photo by G. B. Vai, modified). d: Surroundings of Faenza with the location of Oriolo and adjacent coeval sites yielding plant macrofossils.
Aerial and Terrestrial Thermal Images of German Multi-Family Buildings
<p>This dataset consists of 968 thermal images including 693 captured by hand-held camera on the ground and 275 via UAV. Of the aerial images, 139 were recorded manually and 136 in automatic flight mode. The images depict four multi-family buildings of 18 m height in the German city of Karlsruhe belonging to the local municipal housing association Volkswohnung Karlsruhe GmbH. Table 1 gives an overview of the buildings in question, all of which were fully rented out on the days of image acquisition.</p> <p><strong>Table 1:</strong> Building information</p> <table> <tbody> <tr> <td> <p><sup> Building</sup><br> <sub>Details</sub></p> </td> <td> <p>Sophienstr. 201-203</p> </td> <td> <p>Volzstr. 2</p> </td> <td> <p>Wichernstr. 4</p> </td> <td> <p>Wichernstr. 10-18</p> </td> </tr> <tr> <td> <p>construction year</p> </td> <td> <p>1957</p> </td> <td> <p>1954</p> </td> <td> <p>1953</p> </td> <td> <p>1953</p> </td> </tr> <tr> <td> <p>apartments</p> </td> <td> <p>30</p> </td> <td> <p>12</p> </td> <td> <p>25</p> </td> <td> <p>30</p> </td> </tr> </tbody> </table> <p>The aerial images were acquired using DJI’s “Matrice 600” UAV (DJI, 2022) equipped with the “Zenmuse XT2”, a combination of FLIR’s “Duo Pro R” thermal and RGB camera technology and DJI’s gimbal (FLIR, 2021a). All thermal images were recorded in FLIR’s proprietary image format RJPEG. The terrestrial thermographic images were captured with FLIR’s “T200” hand-held camera (FLIR, 2021b) in the standard JPEG format. The emissivity was set to 0.95 throughout the acquisition of both aerial and terrestrial images. Thermographic image processing and analysis was realized using the "FLIR Thermal Studio" software (FLIR Systems Inc., 2022). The temperature scale was set to -8 °C to +13 °C and color distribution function "signal linear" selected.</p> <p>The thermal images of this dataset were recorded on February 28<sup>th</sup> and March 1<sup>st</sup>, 2022, between 8 p.m. and 1 a.m. On February 28<sup>th</sup> the outside air temperature registered at between 1 °C and 3 °C. Wind speeds reached a maximum of 17 km/h. The sky was cloudless both during the flights and in the preceding 24 hours. A maximum temperature of 11 °C was recorded by local weather stations in that time period. Very similar weather conditions were present on March 1<sup>st</sup>. The outside air temperature was recorded at between -1 °C and 5 °C during acquisition, with wind speeds of max. 11 km/h. Again, the sky was entirely clear both during the flights and in the preceding 24 hours, with a maximum temperature of 9 °C present in that time period. The sun set at around 6:10 p.m. on both days (timeanddate, 2022).</p> <p>The images were recorded using ten different flight settings of varying speed, flight height, and camera angle. Details are summarized in Table 2.</p> <p><strong>Table 2: </strong>Flight settings</p> <table align="center"> <tbody> <tr> <td> <p>Flight</p> </td> <td> <p>Building</p> </td> <td> <p>Automatically/ manually performed flight route</p> </td> <td> <p>Camera angle</p> <p>[°]</p> </td> <td> <p>Height above ground</p> <p>[m]</p> </td> <td> <p>Height above building</p> <p>[m]</p> </td> <td> <p>Distance to façade</p> <p>[m]</p> </td> <td> <p>Flight speed</p> <p>[m/s]</p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>Full area</p> </td> <td> <p>Automatically</p> </td> <td> <p>45 (oblique)</p> </td> <td> <p>40</p> </td> <td> <p>22</p> </td> <td> <p>-</p> </td> <td> <p>1</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>Full area</p> </td> <td> <p>Automatically</p> </td> <td> <p>45 (oblique)</p> </td> <td> <p>40</p> </td> <td> <p>22</p> </td> <td> <p>-</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>Full area</p> </td> <td> <p>Automatically</p> </td> <td> <p>45 (oblique)</p> </td> <td> <p>40</p> </td> <td> <p>22</p> </td> <td> <p>-</p> </td> <td> <p>5</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>Full area</p> </td> <td> <p>Automatically</p> </td> <td> <p>45 (oblique)</p> </td> <td> <p>60</p> </td> <td> <p>42</p> </td> <td> <p>-</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>Full area</p> </td> <td> <p>Automatically</p> </td> <td> <p>90 (nadir)</p> </td> <td> <p>40</p> </td> <td> <p>22</p> </td> <td> <p>-</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>Full area</p> </td> <td> <p>Automatically</p> </td> <td> <p>90 (nadir)</p> </td> <td> <p>60</p> </td> <td> <p>42</p> </td> <td> <p>-</p> </td> <td> <p>3</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p>Wichernstr. 4</p> </td> <td> <p>Manually</p> </td> <td> <p>0 (manual)</p> </td> <td> <p>4 to 12</p> </td> <td> <p>-</p> </td> <td> <p>4</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>8</p> </td> <td> <p>Wichernstr. 4</p> </td> <td> <p>Manually</p> </td> <td> <p>0 (manual)</p> </td> <td> <p>4 to 12</p> </td> <td> <p>-</p> </td> <td> <p>8</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>9</p> </td> <td> <p>Wichernstr. 4</p> </td> <td> <p>Manually</p> </td> <td> <p>0 (manual)</p> </td> <td> <p>4 to 12</p> </td> <td> <p>-</p> </td> <td> <p>15</p> </td> <td> <p>-</p> </td> </tr> <tr> <td> <p>10</p> </td> <td> <p>Wichernstr. 10-18</p> </td> <td> <p>Manually</p> </td> <td> <p>0 (manual)</p> </td> <td> <p>4 to 12</p> </td> <td> <p>-</p> </td> <td> <p>15</p> </td> <td> <p>-</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>Acknowledgments:</strong> The authors appreciate the support of Marinus Vogl (Air Bavarian GmbH) in acquiring the thermal images via UAV. Moreover, they thank Harald Schneider (Karlsruhe Institute of Technology) for his advice and assistance. Lastly, they gratefully acknowledge the consent and support of Karlsruher Volkswohnung GmbH within this research project.</p> <p> </p> <p><strong>References:</strong></p> <p>DJI (2022). Matrice 600 - DJI. URL: https://www.dji.com/de/matrice600 (accessed 10<sup>th</sup> January 2022)</p> <p>FLIR (2021a). FLIR XT2 product information (Wilsonville, USA). URL: https://www.flir.de/products/xt2/ (accessed 10<sup>th</sup> January 2022)</p> <p>FLIR (2021b). FLIR T-series (Wilsonville, USA). URL: https://www.flir.com/instruments/t-series/ (accessed 10<sup>th</sup> January 2022)</p> <p>FLIR Systems Inc. (2022). User’s manual Flir Thermal Studio. URL: https://www.sahkonumerot.fi/6708162/doc/operatinginstructions/ (accessed 12<sup>th</sup> August 2022)</p> <p>Timeanddate (2022). Wetter im Februar 2022 in Karlsruhe, Baden-Württemberg, Deutschland. URL: https://www.timeanddate.de/wetter/deutschland/karlsruhe/rueckblick?month=2&year=2022 (accessed 12<sup>th</sup> March 2022)</p> <p> </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.