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3,101 results for “Historical”
Figure 3 from: Angyal D, Simões N, Mascaró M (2020) Uptaded checklist, historical overview and illustrated guide to the stygobiont Malacostraca (Arthropoda: Crustacea) species of Yucatan (Mexico). Subterranean Biology 36: 83-108. https://doi.org/10.3897/subtbiol.36.53558
Figure 3 Stygiomysida, schematic drawing.
Figure 6 from: Angyal D, Simões N, Mascaró M (2020) Uptaded checklist, historical overview and illustrated guide to the stygobiont Malacostraca (Arthropoda: Crustacea) species of Yucatan (Mexico). Subterranean Biology 36: 83-108. https://doi.org/10.3897/subtbiol.36.53558
Figure 6 Mysida, schematic drawing.
Figure 19 from: Angyal D, Simões N, Mascaró M (2020) Uptaded checklist, historical overview and illustrated guide to the stygobiont Malacostraca (Arthropoda: Crustacea) species of Yucatan (Mexico). Subterranean Biology 36: 83-108. https://doi.org/10.3897/subtbiol.36.53558
Figure 19 Decapoda, schematic drawing.
Figure 12 from: Angyal D, Simões N, Mascaró M (2020) Uptaded checklist, historical overview and illustrated guide to the stygobiont Malacostraca (Arthropoda: Crustacea) species of Yucatan (Mexico). Subterranean Biology 36: 83-108. https://doi.org/10.3897/subtbiol.36.53558
Figure 12 ACreaseriella anops rolled position BC. anops cephalon CC. anops pleotelson and uropods.
Figure 20 from: Angyal D, Simões N, Mascaró M (2020) Uptaded checklist, historical overview and illustrated guide to the stygobiont Malacostraca (Arthropoda: Crustacea) species of Yucatan (Mexico). Subterranean Biology 36: 83-108. https://doi.org/10.3897/subtbiol.36.53558
Figure 20 ATyphlatya pearsei rostrum and eyestalk BT. pearsei pereopod V schematic drawing.
Figure 1 from: Angyal D, Simões N, Mascaró M (2020) Uptaded checklist, historical overview and illustrated guide to the stygobiont Malacostraca (Arthropoda: Crustacea) species of Yucatan (Mexico). Subterranean Biology 36: 83-108. https://doi.org/10.3897/subtbiol.36.53558
Figure 1 Thermosbaenacea, schematic drawing.
Figure 23 from: Angyal D, Simões N, Mascaró M (2020) Uptaded checklist, historical overview and illustrated guide to the stygobiont Malacostraca (Arthropoda: Crustacea) species of Yucatan (Mexico). Subterranean Biology 36: 83-108. https://doi.org/10.3897/subtbiol.36.53558
Figure 23 ACreaseria morleyi rostrum and eyestalks BC. morleyi telson and uropods.
Figure 15 from: Angyal D, Simões N, Mascaró M (2020) Uptaded checklist, historical overview and illustrated guide to the stygobiont Malacostraca (Arthropoda: Crustacea) species of Yucatan (Mexico). Subterranean Biology 36: 83-108. https://doi.org/10.3897/subtbiol.36.53558
Figure 15 Amphipoda, schematic drawing.
Figure 2 from: Angyal D, Simões N, Mascaró M (2020) Uptaded checklist, historical overview and illustrated guide to the stygobiont Malacostraca (Arthropoda: Crustacea) species of Yucatan (Mexico). Subterranean Biology 36: 83-108. https://doi.org/10.3897/subtbiol.36.53558
Figure 2 ATulumella unidens habitus simplified drawing BT. unidens pereopod VI.
Figure 4 from: Angyal D, Simões N, Mascaró M (2020) Uptaded checklist, historical overview and illustrated guide to the stygobiont Malacostraca (Arthropoda: Crustacea) species of Yucatan (Mexico). Subterranean Biology 36: 83-108. https://doi.org/10.3897/subtbiol.36.53558
Figure 4 AStygiomysis holthuisi telson BS. holthuisi uropod protopodal process.
Figure 8 from: Angyal D, Simões N, Mascaró M (2020) Uptaded checklist, historical overview and illustrated guide to the stygobiont Malacostraca (Arthropoda: Crustacea) species of Yucatan (Mexico). Subterranean Biology 36: 83-108. https://doi.org/10.3897/subtbiol.36.53558
Figure 8 Isopoda, schematic drawing.
Data from: Pleistocene sea level fluctuation and host plant habitat requirement influenced the historical phylogeography of the invasive species Amphiareus obscuriceps (Hemiptera: Anthocoridae) in its native range
Background: On account of repeated exposure and submergence of the East China Sea (ECS) land bridge, sea level fluctuation played an important role in shaping the population structure of many temperate species across the ECS during the glacial period. The flower bug Amphiareus obscuriceps (Poppius, 1909) (Hemiptera: Anthocoridae) is an invasive species native to the Sino-Japanese Region (SJR) of East Asia. We tested the hypothesis of the ECS land bridge acting as a dispersal corridor or filter for A. obscuriceps during the glacial period. Specifically, we tested whether and the extent to which dispersal ability and host plant habitat requirement influenced the genetic structure of A. obscuriceps during the exposure of the ECS land bridge. Results: Phylogenetic and network analyses indicated that A. obscuriceps is composed of two major lineages, i.e., China and Japan. Divergence time on both sides of the ECS was estimated to be approximately 1.07 (0.79-1.32) Ma, which was about the same period that the sea level increased. No significant Isolation by Distance (IBD) relationship was found between Фst and Euclidean distances in the Mantel tests, which is consistent with the hypothesis that this species has a good dispersal ability. Our Last Glacial Maximum (LGM) niche modeling of plants that constitute preferred habitats for A. obscuriceps exhibited a similar habitat gap on the exposed ECS continental shelf between China and Japan, but showed a continuous distribution across the Taiwan Strait. Conclusion: Our results suggest that ecological properties (habitat requirement and dispersal ability), together with sea level fluctuation during the Pleistocene across the ECS, have shaped the genetic structure and demographic history of A. obscuriceps in its native area. The host plant habitat requirement could also be a key to the colonization of the A. obscuriceps species during the exposure of the ECS land bridge. Our findings will shed light on the potential role of habitat requirement in the process of biological invasion in future studies.
Data from: Do the historical biogeography and evolutionary history of the digenean Margotrema spp. across central Mexico mirror those of their freshwater fish hosts (Goodeinae)?
Host-parasite systems provide an ideal platform to study evolution at different levels, including codivergence in a historical biogeography context. In this study we aim to describe biogeographic and codivergent patterns and associated processes of the Goodeinae freshwater fish and their digenean parasite (Margotrema spp.) over the last 6.5 Ma (million years), identifying the main factors (host and/or hydrogeomorphology) that influenced the evolution of Margotrema. We obtained a species tree for Margotrema spp. using DNA sequence data from mitochondrial and nuclear molecular markers (COI and ITS1, respectively) and performed molecular dating to discern divergence events within the genus. The dispersal-extinction-cladogenesis (DEC) model was used to describe the historical biogeography of digeneans and applied to cophylogenetic analyses of Margotrema and their goodeine hosts. Our results showed that the evolutionary history of Margotrema has been shaped in close association with its geographic context, especially with the geological history of central Mexico during the Pleistocene. Host-specificity has been established at three levels of historical association: a) Species-Species, represented by Xenotaenia resolanae-M. resolanae exclusively found in the Cuzalapa River Basin; b) Species-Lineage, represented by Characodon audax-M. bravoae Lineage II, exclusive to the Upper and Middle Mezquital River Basin, and c) Tribe-Lineage, including two instances of historical associations among parasites and hosts at the taxonomical level of tribe, one represented by Ilyodontini-M. bravoae Lineage I (distributed across the Ayuquila and Balsas River Basins), and another comprised of Girardinichthyini/Chapalichthyini-M. bravoae Lineage III, found only in the Lerma River Basin. We show that the evolutionary history of the parasites is, on several occasions, in agreement with the phylogenetic and biogeographic history of their hosts. A series of biogeographic and host-parasite events explain the codivergence patterns observed, in which cospeciation and colonisation via host-switching and vicariant plus dispersal events are appreciated, at different times during the diversification history of both associates, particularly during the Pleistocene.
Data from: Phylogenomics and historical biogeography of the monocot order Liliales: out of Australia and through Antarctica
We present the first phylogenomic analysis of relationships among all ten families of Liliales, based on 75 plastid genes from 35 species in 29 genera, and 97 additional plastomes stratified across angiosperm lineages. We used a supermatrix approach to extend our analysis to 58 of 64 genera of Liliales, and calibrated the resulting phylogeny against 17 fossil dates to produce a new timeline for monocot evolution. Liliales diverged from other monocots 124 Mya and began splitting into separate families 113 Mya. Our data support an Australian origin for Liliales, with close relationships between three pairs of lineages (Corsiaceae/Campynemataceae, Philesiaceae/Ripogonaceae, tribes Alstroemerieae/Luzuriageae) in South America and Australia or New Zealand reflecting teleconnections of these areas via Antarctica. Long-distance dispersal (LDD) across the Pacific and Tasman Sea led to re-invasion of New Zealand by two lineages (Luzuriaga, Ripogonum); LDD allowed Campynemanthe to colonize New Caledonia after its submergence until 37 Mya. LDD permitted Colchicaceae to invade East Asia and Africa from Australia, and re-invade Africa from Australia. Periodic desert greening permitted Gloriosa and Iphigenia to colonize Southeast Asia overland from Africa, and Androcymbium–Colchicum to invade the Mediterranean from South Africa. Melanthiaceae and Liliaceae crossed the Bering land-bridge several times from the Miocene to the Pleistocene.
Replication package for the study "Broken Windows: Correlating historical and new code quality"
<p>Is the quality of existing code correlated with the quality of subsequent changes? According to the (controversial) broken windows theory, which inspired this study, disorder sets descriptive norms and signals behavior that further increases it. From a large code corpus, we examine whether code history does indeed affect the evolution of code quality. We examine C code quality metrics and Java code smells in specific files, and see whether subsequent commits by developers continue on that path. We check whether developers tailor the quality of their commits based on the quality of the file they commit to. Our results show that history matters, that developers behave differently depending on some aspects of the code quality they encounter, and that programming style inconsistency is not necessarily related to structural qualities. These findings have implications for both software practice and research.</p><p>This replication package contains the data and source code used in the study.</p>
TGLO Establish Historical Long-term Wetland Boundary Evolution Through Satellite Imagery
<p>This dataset includes wetland erosion rates for West Galveston, Matagorda, and San Antonio Bay. Long-term trends in wetland boundary changes are estimated using Landsat satellite imagery. Sections of the wetland experiencing the highest rates of erosion will be further investigated through CubeSat satellite observations. The format of the data is summarized as follows</p> <p>(1) Landsat based wetland evolution results from 1984 to 2020</p> <ul> <li>The Annual and seasonal water occurrence (in gif format)</li> <li>The wetland change map (in TIF format)</li> </ul> <p>(2) CubeSat based wetland evolution results from 2009 to 2020</p> <ul> <li>Water occurrence maps from 2009 to 2021 for the RapidEye based bi-annual results (FSX-occurrence-yyy1-yyy2.tif) and the PlanetScope based annual results (FSX-occurrence-yyy1.tif), where yyyy represents the given year. The legend image is 'occurrence-cbar.jpg'</li> <li>Erosion maps based on the difference between water occurrence mapping in 2009 and 2021: ('FSX-occurrence-diff-2021-2009.tif'). The legend is 'occurrence-diff-cbar.jpg'</li> <li>The 0.2-meter bed counter line images based on the water occurrence maps and the tide elevation threshold from 2017 to 2021: (FSX-bed-yyy1.tif). Again, yyy1 represents the given year. The legend is 'color-bed.jpg</li> <li>The difference between the beds in the 0.2-meter bed counter line images in 2017 and 2021 at FS-1, FS-2, FS-3, and FS-4: (FSX-bed-diff-2021-2017.tif). The legend is 'color-bed-dif.jpg'</li> </ul> <p>(3) Analysis of wetland boundary evolution and erosion rate</p> <ul> <li>Landsat based Wetland erosion rate from 1984 to 2020 and CubeSat erosion rate from 2009 to 2021 (data format in ArcGIS shapefile)*</li> <li>Landsat based coastlines in 1984, 2000, 2010, and 2020**</li> <li>* *The dataset also shown on the Coast Atlas website (<a href="https://www.texascoastalatlas.com/bayatlas/index.html">this link</a>)</li> </ul> <p> </p> <p> </p> <p> </p>
Historical Turkish cannon.
* Historical cannon located in Bursa Tophane Region.  Source: Objaverse 1.0 / Sketchfab
Historical Emission Inventory Database for Shipping
<p><span>The global historical shipping emission inventory dataset covers CO<sub>2</sub> and five key </span><span>atmospheric pollutants (</span><span>NO<em><sub>x</sub></em>, SO<sub>2</sub>, CO, NMVOCs and PM) over the time period from 1970 – 2021 and has been allocated to 0.1</span><span>°×</span><span>0.1</span><span>°</span><span> grids on a bilateral basis.</span><span> </span></p>
Raw landmarks related to the paper, "Evolution under intensive industrial breeding: skull size and shape comparison between historic and modern pig lineages'
<p>Raw coordinates (p x k = 82 x 3) of domestic and wild pig skulls that form the dataset for the paper, "­Evolution under intensive industrial breeding: skull size and shape comparison between historic and modern pig lineages </p>
SECURES-Energy: Hourly electricity demand and supply profiles for historical climate and climate change projections in Europe until 2100
<p><strong>SECURES-Energy</strong></p> <p>Weather-dependent renewable electricity systems are vulnerable to climate change impacts. Electricity generation and demand profiles considering weather and climate impacts are needed in energy system modelling. We present a consistent and high-quality energy database in data formats useful for energy system modelling and keeping the high spatiotemporal complexity of climate data. The open-access dataset SECURES-Energy contains all relevant electricity demand and supply components for the EU and several additional European countries in hourly resolution covering the period 1981-2100. It is based on reanalysis data ERA5(-Land) for the historical period and two EURO-CORDEX emission scenarios (RCP 4.5 and RCP 8.5). On the generation side, impacts on onshore and offshore wind power generation, solar PV generation, and hydropower generation (run-of-river and reservoirs) – which is often missing in comparable datasets – are provided. On the demand side, all demand components relevant to future electricity systems including e-heating, e-cooling, e-mobility, and electricity demand in industry, are provided.</p> <p>The detailed methods are described in the final project report (see link below) in Chapter 2.2 and Chapter 4.3 and a related journal publication is currently in preparation.</p> <p><strong>Further information:</strong></p> <ul> <li>Project website SECURES: https://www.secures.at/</li> <li>All project-related publications: https://www.secures.at/publications</li> <li>Final SECURES project report: https://www.secures.at/fileadmin/cmc/Final_Report_SECURES.pdf and https://www.klimafonds.gv.at/wp-content/uploads/sites/16/C061007-ACRP12-SECURES-KR19AC0K17532-EB.pdf</li> </ul> <p>The SECURES-Energy dataset provides variables visible in the table.</p> <ol> <li>Hourly profiles ERA5-Land 1981-2010</li> <li>Hourly profiles RCP 4.5/RCP 8.5 2011-2100</li> </ol> <p> </p> <p><strong>Production profiles:</strong></p> <table> <tbody> <tr> <th>Variable</th> <th>Short name</th> <th>Unit</th> <th>Temporal resolution</th> </tr> <tr> <th>Photovoltaics</th> <td>pv</td> <td>-</td> <td>hourly</td> </tr> <tr> <th>Wind onshore</th> <td>wind</td> <td>-</td> <td>hourly</td> </tr> <tr> <th>Wind offshore</th> <td>wind_offshore</td> <td>-</td> <td>hourly</td> </tr> <tr> <th>Hydro run-of-river</th> <td>hydro_ror</td> <td>-</td> <td>hourly</td> </tr> </tbody> </table> <p> </p> <p><strong>Demand profiles:</strong></p> <table> <tbody> <tr> <th>Variable</th> <th>Short name</th> <th>Unit</th> <th>Explanation</th> </tr> <tr> <th>Temperature</th> <td>temperature</td> <td> <p>°C</p> </td> <td> <p>Population-weighted mean temperature (2 m)</p> </td> </tr> <tr> <th> <p>Rounded temperature</p> </th> <td>rounded_temperature</td> <td>°C</td> <td>Temperature values rounded to zero decimal places</td> </tr> <tr> <th>Daytype</th> <td>day type</td> <td>-</td> <td> <p>weekdays = typeday 0; Saturday or day before a holiday = typeday 1; Sunday or holiday = typeday 2</p> </td> </tr> <tr> <th>Month<strong><br></strong></th> <td> <p>month</p> </td> <td> <p>-</p> </td> <td> <p> The column “month” refers to the month of the year. 1 = January, 2 = February etc.</p> </td> </tr> <tr> <th> Season</th> <td>season</td> <td>-</td> <td> <p>0 = Summer (15/05 - 14/09)</p> <p>1 = Winter (1/11 - 20/3)</p> <p>2 = Transition (21/3 - 14/5 & 15/9 - 31/10)</p> </td> </tr> <tr> <th>Load e-mobilty</th> <td> <p>load_emobility</p> </td> <td> <p>-</p> </td> <td> <p>E-mobility electricity demand profile, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent)</p> </td> </tr> <tr> <th>Non-metallic minerals</th> <td> <p>non_metallic_minerals</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile of the industrial sector non-metallic minerals, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent)</p> </td> </tr> <tr> <th>Paper</th> <td> <p>paper</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile of the industrial sector paper, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent)</p> </td> </tr> <tr> <th>Iron and steel</th> <td> <p>iron_and_steel</p> </td> <td> <p>-</p> </td> <td>Electricity demand profile of the industrial sector iron and steel, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent)</td> </tr> <tr> <th>Chemicals and petrochemicals</th> <td> <p>chemicals_and_petrochemicals</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile of the industrial sector chemicals and petrochemicals, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent)</p> </td> </tr> <tr> <th>Food and tobacco</th> <td> <p>food_and_tobacco</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile of the industrial sector food and tobacco, normalized to an annual demand of 200,000 (sum of all industry sectors 1,000,000) (non-weather-dependent)</p> </td> </tr> <tr> <th>SHW residential</th> <td> <p>shw_residential</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile for sanitary hot water in the residential sector, normalized to an annual demand of 1,000,000 (non-weather-dependent)</p> </td> </tr> <tr> <th>SHW tertiary<strong><br></strong></th> <td> <p>shw_tertiary</p> </td> <td> <p> </p> </td> <td> <p>Electricity demand profile for sanitary hot water in the tertiary sector, normalized to an annual demand of 1,000,000 (non-weather-dependent)</p> </td> </tr> <tr> <th>Cooling residential<strong><br></strong></th> <td> <p>cooling_residential</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile for cooling in the residential sector, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent)</p> </td> </tr> <tr> <th>Heating residential<strong><br></strong></th> <td> <p>heating_residential</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile for heating in the residential sector, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent)</p> </td> </tr> <tr> <th>Cooling tertiary</th> <td> <p>cooling_tertiary</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile for cooling in the tertiary sector, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent)</p> </td> </tr> <tr> <th>Heating tertiary<strong><br></strong></th> <td> <p>heating_tertiary</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile for heating in the tertiary sector, normalized to an annual demand of 1,000,000 in the reference year 2010 (weather-dependent)</p> </td> </tr> <tr> <th>Rest<strong><br></strong></th> <td> <p>rest</p> </td> <td> <p>-</p> </td> <td> <p>Rest electricity demand profile, normalized to an annual demand of 1,000,000 (non-weather-dependent)</p> </td> </tr> <tr> <th>Exogenous H2<strong><br></strong></th> <td> <p>exogenous_H2</p> </td> <td> <p>-</p> </td> <td> <p>Electricity demand profile for electrolysis (flat profile), normalized to an annual demand of 1,000,000 (non-weather-dependent)</p> </td> </tr> <tr> <th>Total<strong><br></strong></th> <td> <p>total</p> </td> <td> <p>-</p> </td> <td> <p>Total electricity demand profile containing all components above (e-mobility, industry, residential heating, residential sanitary hot water, residential cooling, tertiary heating, tertiary sanitary hot water, tertiary cooling, rest, and exogenous H2 electricity demand), normalized to an annual demand of 10,000,000 in the reference year 2010</p> </td> </tr> </tbody> </table> <p>Electricity supply profiles for wind (onshore and offshore), hydro (run-of-river), and solar generation are provided for almost all European countries, namely: Andorra (AD), Albania (AL), Austria (AT), Bosnia and Herzegovina (BA), Belgium (BE), Bulgaria (BG), Switzerland (CH), Czech Republic (CZ), Germany (DE), Denmark (DK), Estonia (EE), Spain (ES), Finland (FI), France (FR), United Kingdom of Great Britain and Northern Ireland (GB), Greece (GR), Croatia (HR), Hungary (HU), Republic of Ireland (IE), Italy (IT), Liechtenstein (LI), Lithuania (LT), Luxembourg (LU), Latvia (LV), Montenegro (ME), North Macedonia (MK), Malta (MT), Netherlands (NL), Norway (NO), Poland (PL), Portugal (PT), Romania (RO), Serbia (RS), Sweden (SE), Slovenia (SI), Slovakia (SK), San Marino (SM), Ukraine (UA), Vatican (VA), and Kosovo (XK). The countries covered by the electricity demand profiles are the EU27 countries (except for Cyprus), CH, GB, and NO.</p> <p>Industrial, heating, and cooling demand profiles are based on regressions developed in the H2020 Hotmaps project [1] [2]. </p> <p>SECURES-Energy is available in a tabular csv format for the historical period (1981-2010) created from ERA5 and ERA5-Land and two future emission scenarios (<strong>RCP 4.5 </strong>and <strong>RCP 8.5</strong>, both 2011-2100) created from one CMIP5 EURO-CORDEX model (GCM: ICHEC-EC-EARTH, RCM: KNMI-RACMO22E) on the<strong> </strong>spatial aggregation level<strong> NUTS0 </strong>(country-wide).</p> <p>The data is divided into the historical (Historical.zip) and the two emission scenarios (Future_RCP45.zip and Future_RCP85.zip), a README file, which describes, how the files are organized, and a folder (Meta.zip), which has information and shapefiles of the different NUTS levels.</p> <p>Hydro reservoir profiles are also published and can be found in the related dataset SECURES-Met: https://zenodo.org/records/7907883.</p> <p>The project SECURES and corresponding publications are funded by the Climate and Energy Fund (Klima- und Energiefonds) under project number KR19AC0K17532.</p> <p>[1] Fallahnejad M. Hotmaps-data-repository-structure 2019. https://wiki.hotmaps.eu/en/Hotmaps-open-data-repositories.</p> <p>[2] Pezzutto S, Zambotti S, Croce S, Zambelli P, Garegnani G, Scaramuzzino C, et al. HOTMAPS - D2.3 WP2 Report – Open Data Set for the EU28. 2019.</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.