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Field Applied Research (FAR) Australia High Yielding Wheat Data 2015-2022 v1.0
<p>This dataset compiles experimental results from FAR Australia's research into wheat, from medium and high yielding environments in Australia, between 2015 and 2022.</p> <p>The data has been gathered to improve the validation of APSIM-wheat at high yield levels (>8 t/ha; a component of GRDC Project FAR2304-002RTX). The ultimate aim is to incorporate much of it into the APSIM NextGen wheat model validation dataset (<a href="https://github.com/APSIMInitiative/ApsimX/tree/master/Tests/Validation/Wheat">https://github.com/APSIMInitiative/ApsimX/tree/master/Tests/Validation/Wheat</a>).</p> <p>Yield was the initial criteria for including experiments, along with availability of explanatory data such as dry matter and NDVI, and availability of soil and weather data. The criteria have been relaxed to include lower yielding experiments with phenology data that will assist in the parameterisation of cultivars not already in APSIM. </p> <p>We will be continuing to collate information about the experiments as we build associated simulations. This repository represents progress to date.</p> <p><strong>Sharing/access information (D-3.1*)</strong></p> <p>There is no restriction on the use of this data.</p> <p>Most of the work has not been published previously in peer-reviewed literature.</p> <p>If you plan to publish work based to any substantial degree off this dataset, FAR Australia would appreciate being offered the opportunity to be involved early. Our aim is to make a constructive contribution to the work and be included as a co-author.</p> <p>Where possible, Portable Document Format (pdf) copies of the most recent report on each experiment are included to faciliate interpretation. Many of these can also be found on the FAR Australia website (<a href="https://faraustralia.com.au/resource">https://faraustralia.com.au/resource</a> ), or by searching online for GRDC update papers that mention "hyper yielding".</p> <p>A subset of this data (2022 Hyper-Yielding Wheat experiments) has been published on Zenodo previously, at <a href="https://zenodo.org/record/7851239">https://zenodo.org/record/7851239</a> .</p> <p><em><strong>Recommended citation:</strong></em></p> <p>Jones, Ben, Nick Poole, Darcy Warren, Maxwell Bloomfield, and Aaron Vague. ‘Field Applied Research (FAR) Australia High Yielding Wheat Data 2015-2022 v1.0’. Zenodo, 3 July 2023. <a href="https://doi.org/10.5281/zenodo.8106751">https://doi.org/10.5281/zenodo.8106751</a>.</p> <p><strong>Data and file overview (D-3.2)</strong></p> <p><em><strong>Data files</strong></em></p> <p>The data are organised by approximate site, year, and trial ID.</p> <p>For example, the Albany folder contains folders for each year between 2020 and 2022. The 2020 folder contains folders for two trials: FAR WAA W20-01a, and FAR WAA W20-01b. These were adjacent experiments with the same treatments, on clayed (a) and unclayed (b) soils.</p> <p>Each folder contains:</p> <ul> <li>The most recent version of the excel data sheet(s) that record original data and contains related calculations (eg. plant density from plant counts). These are in similar formats for most trials, and typically contain at least one version of the trial design (map), assessment worksheets (typically Plot, Serp = serpentine, and/or Full), and often statistical analysis and results tables.</li> <li>The pdf copy of the most recent report on the experiment.</li> <li>A JSON object with a unique integer ID that contains site, year, and FAR trial ID.</li> </ul> <p> The Trials.json object in the root directory contains an array of all the JSON objects, including the paths of the pdf and data files relative to the root directory, in order that files in the dataset can easily be manipulated programmatically. For example:</p> <pre><code class="language-json">{"Valid":true,"Id":51,"Trial":"FAR WAA W20-01a","Site":"Albany","Year":2020,"Notes":"","Files":[{"Path":"Albany\\2020\\FAR WAA W20-01a\\200423 FAR WAA W20-01a (clay) ex TW.xlsx","Type":"data"},{"Path":"Albany\\2020\\FAR WAA W20-01a\\210316 HRZ 2020 Cereal Results - FINAL.pdf","Type":"doc"}],"TimeStamp":"2023-07-03T01:40:25.72264Z"}</code></pre> <p><em><strong>Location and weather information</strong></em></p> <p>The Site_Year_LatLng.xlsx spreadsheet in the root directory lists the latitude and longitude of each trial, where possible determined from the EXIF information attached to photographs of trial plots.</p> <p>The Weather folder contains downloads of data from weather stations at research sites. The Stations_list.xlsx spreadsheet in the Weather folder indicates which stations are associated with which sites.</p> <p><em><strong>Soil and management (ancillary) information</strong></em></p> <p>The Ancillary folder contains an initial attempt to draw together soil, management and other information that may be useful for modeling/interpretation but not available in the excel data sheet. This has some basic organisation, but no specific data format. In some cases these are tables copied from the research report.</p> <p>Where available, these are excel spreadsheets named by site and year, containing one worksheet per trial for information specific to that trial, and remaining worksheets that are relevant to the site as a whole.</p> <p>For example the Albany_2020.xlsx spreadsheet in the Ancillary/Albany/2020 folder contains a FAR WAA W20 worksheet with information about the site as a whole, a number of other worksheets imported from site soil samples, and FAR WAA W20-01a and FAR WAA W20-01b worksheets with trial-specific information.</p> <p><em><strong>Dataset version</strong></em></p> <p>This is the first version of the dataset, uploaded on July 3, 2023.</p> <p><strong>Methodological information (D-3.3)</strong></p> <p>A number of processes were used to "discover" the data included in this dataset. Several exhaustive processes were used on files within the FAR Australia dropbox folder. These included:</p> <ul> <li>Survey of all Agricultural Research Manager (ARM) files</li> <li>Collation of all Microsoft Word (*.doc/x) and Excel (*.xls/x) files</li> <li>Collation of all image (*.jpg/jpeg) files</li> <li>Collation of all wheat phenological data</li> </ul> <p>While the initial screen of data to be included focused on wheat yield, dry matter and NDVI (hand-held Greenseeker) data, additional experiments were included based on availability of phenology data.</p> <p>The trial coding used within FAR Australia systems has remained relatively consistent over most of the period, and has assisted with finding related data. For the example FAR WAA W20-01a, WAA is generally location-related (WA Albany), W is wheat (D is Durum Wheat), 20 is the year 2020, 01 is protocol 1. A further suffixed number is used to indicate time of sowing (W20-01-2 would be a second time of sowing after W20-01-1). Protocol numbering typically remains consistent within a project, but may differ from the sequential numbering given to trials in a report to make them more readable.</p> <p>No special software should be required, and standards/calibration information is not currently available beyond what is reported for each trial.</p> <p><strong>Environmental/experimental conditions (D-3.4)</strong></p> <p>There has been no quality assurance procedure performed on the data. This will be a subject for the FAR2304-002RTX project.</p> <p>People involved with sample collection, processing, analysis and/or submission are acknowledged in the report associated with each trial.</p> <p>The trial reports describe experimental or environmental conditions affecting the interpretation of the relevant trial data. Some spreadsheets also contains notes on the data.</p> <p>Where trial(s) are obviously missing, this is usually related to environmental conditions that rendered the data unusable. An example of this is severe waterlogging at the Gnarwarre site in 2021.</p> <p><strong>Data-specific information (D-3.5)</strong></p> <p>The data spreadsheets are self-describing. The rows above the data typically contain the date, crop growth stage, a brief description of the assessment made, initials of the people making the assessment, and any notes.</p> <p>*D- references refer to the GRDC RD&E Data Capture and Storage Guidelines for Research Partners, Version 1.0</p> <p><a href="https://grdc.com.au/__data/assets/pdf_file/0033/584259/GRDC-RD-and-E-Data-Capture-and-Storage-Guidelines-for-Research-Partners.pdf">https://grdc.com.au/__data/assets/pdf_file/0033/584259/GRDC-RD-and-E-Data-Capture-and-Storage-Guidelines-for-Research-Partners.pdf</a></p>
Expression data from field droughted rice plants
GEO Series GSE24048. Oryza sativa. 12 samples. Type: Expression profiling by array.
PENGUIn-2 (PG2) Ground-based Vector Magnetic Field (L2) 1.0 s Data
PENGUIn-2, Antarctica, Ground-based Vector Magnetic Field Level 2 Data, 1.0 s Time Resolution, Station Code: (PG2), Station Location: (GEO Latitude -84.4, Longitude 58.0), Antarctic Network
CALIPSO Wide Field Camera (WFC) L1B Science 125 m Native Science Data V3-01
Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) was launched on April 28, 2006 to study the impact of clouds and aerosols on the Earth’s radiation budget and climate. It flies in formation with five other satellites in the international “A-Train” (PDF) constellation for coincident Earth observations. The CALIPSO satellite comprises three instruments, the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP), the Imaging Infrared Radiometer (IIR), and the Wide Field Camera (WFC). CALIPSO is a joint satellite mission between NASA and the French Agency, CNES. These data consist 5 km aerosol layer data.
Yellowknife (YKNF) Ground-based Vector Magnetic Field (L2) 0.5 s Data
Yellowknife, NT, Ground-based Vector Magnetic Field Level 2 Data, 0.5 s Time Resolution, Station Code: (YKNF), Station Location: (GEO Latitude 62.5, Longitude 245.7), THEMIS GBO/UCLA Network
MMS 1 Digital Signal Processor (DSP) Search Coil Magnetometer (SCM), Magnetic Field Power Spectral Density, Level 2 (L2), Fast Mode, 2 s Data
The MMS magnetic field power spectral density (BPSD) is computed onboard by the Digital Signal Processor (DSP). The fast Fourier transform (FFT) calculation is performed on a digitized version of analog signals from the Search Coil Magnetometer (SCM) in the SCM123 coordinate system, see SCM data product guide for details, https://lasp.colorado.edu/mms/sdc/public/datasets/fields/. This data product is computed in space from individual components that are not synchronized to the 1 second pulse. Therefore, the timing between channels can be inaccurate by a fraction of a second. The samples times are interval start times taken from the x component. The spectra are calculated via a 1024-point FFT algorithm on piecewise continuous sets of waveform data. Nine signals can be processed simultaneously. Six of the twelve DC-coupled E, DC-coupled V, or SCM signals (16384 samples/s) are selected for spectral processing at 100% duty cycle. In addition, the three AC-coupled signals (262,144 kS/s) each can be processed at 6.25% duty cycle. Each of the nine signals has 16, 1024-point FFT operations every second; the field-programmable gate array (FPGA) performs 144 FFTs per second. The FFT is performed by an arithmetic logic unit (ALU), which is controlled by a state machine. Both are hard-coded into the FPGA. The operation starts by applying a 1024-point Hanning window onto a waveform. Next, an FFT is implemented. The FFT is broken into a series of "butterfly" operations performed by the ALU. The result has real and imaginary data. Power spectra are calculated by taking the sum of squares of real and imaginary values (the ALU includes a multiplier), which produces a power spectrum with 512 frequency bins. The frequency bins are then combined to give pseudo-logarithmic frequency spacing (del f)/f. The spectra are reduced to 88 frequency bins with (del f)/f between 6% and 12% when possible. Narrow-band emissions can be fit to an accuracy of (del f)/f ~3%, allowing for an accurate determination of plasma density. The spectra can be averaged in time. The fastest reporting rate of any signal is 16 spectra per second. Reporting rates can be as slow a one spectra every 16 s (averaging 256 spectra). The DSP and SCM instrument papers can be found at https://link.springer.com/article/10.1007/s11214-014-0115-x and https://link.springer.com/article/10.1007/s11214-014-0096-9, respectively. The DSP and SCM data product guides can be found at https://lasp.colorado.edu/mms/sdc/public/datasets/fields/.
Dikson (DIK) Ground-based Vector Magnetic Field (L2) 1.0 min Data
Dikson, Russia, Ground-based Vector Magnetic Field Level 2 Data, 1.0 min Time Resolution, Station Code: (DIK), Station Location: (GEO Latitude 73.5, Longitude 80.7), AARI Network
Longyearbyen (LYR) Ground-based Vector Magnetic Field (L2) 1.0 min Data
Longyearbyen, Norway, Ground-based Vector Magnetic Field Level 2 Data, 1.0 min Time Resolution, Station Code: (LYR), Station Location: (GEO Latitude 78.2, Longitude 15.8), TGO Network
Dead Horse (DED) Ground-based Vector Magnetic Field (L2) 1.0 s Data
Dead Horse, AK, Ground-based Vector Magnetic Field Level 2 Data, 1.0 s Time Resolution, Station Code: (DED), Station Location: (GEO Latitude 70.4, Longitude 211.2), USGS Network
MMS 4 Digital Signal Processor (DSP) Double Probe (ADP, SDP), Electric Field Power Spectral Density, Level 2 (L2), Slow Mode, 16 s Data
The MMS electric field power spectral density (EPSD) is computed onboard by the Digital Signal Processor (DSP). The fast Fourier transform (FFT) calculation is performed on a digitized version of analog signals from the Axial Double Probe (ADP) and Spin-Plane Double Probe (SDP). This data product is computed in space from individual components that are not synchronized to the 1 second pulse. Therefore, the timing between channels can be inaccurate by a fraction of a second. The samples times are interval start times taken from the x component. The spectra are calculated via a 1024-point FFT algorithm on piecewise continuous sets of waveform data. Nine signals can be processed simultaneously. Six of the twelve DC-coupled E, DC-coupled V, or SCM signals (16384 samples/s) are selected for spectral processing at 100% duty cycle. In addition, the three AC-coupled signals (262,144 kS/s) each can be processed at 6.25% duty cycle. Each of the nine signals has 16, 1024-point FFT operations every second; the field-programmable gate array (FPGA) performs 144 FFTs per second. The FFT is performed by an arithmetic logic unit (ALU), which is controlled by a state machine. Both are hard-coded into the FPGA. The operation starts by applying a 1024-point Hanning window onto a waveform. Next, an FFT is implemented. The FFT is broken into a series of "butterfly" operations performed by the ALU. The result has real and imaginary data. Power spectra are calculated by taking the sum of squares of real and imaginary values (the ALU includes a multiplier), which produces a power spectrum with 512 frequency bins. The frequency bins are then combined to give pseudo-logarithmic frequency spacing (del f)/f. The spectra are reduced to 56 frequency bins with (del f)/f between 6% and 12% when possible. Narrow-band emissions can be fit to an accuracy of (del f)/f ~3%, allowing for an accurate determination of plasma density. The spectra can be averaged in time. The fastest reporting rate of any signal is 16 spectra per second. Reporting rates can be as slow a one spectra every 16 s (averaging 256 spectra). The averaging process has 48-bit accuracy to maximize the dynamic range. The amplitudes undergo a pseudo-logarithmic compression to an 8-bit number representing over 120 dB of dynamic range at ~5% precision.
Baranov (BRN) Ground-based Vector Magnetic Field (L2) 1.0 min Data
Baranov, Russia, Ground-based Vector Magnetic Field Level 2 Data, 1.0 min Time Resolution, Station Code: (BRN), Station Location: (GEO Latitude 47.8, Longitude 132.4), AARI Network
Pello (PEL) Ground-based Vector Magnetic Field (L2) 10.0 s Data
Pello, Finland, Ground-based Vector Magnetic Field Level 2 Data, 10.0 s Time Resolution, Station Code: (PEL), Station Location: (GEO Latitude 66.9, Longitude 24.1), IMAGE Network
Voyager 2 48-s Triaxial Fluxgate Magnetometer (MAG) Magnetic Field Data in CDF Format
This data set includes the Voyager spacecraft number (1 or 2), the date-time in decimal year (90.00000 is day 1 of 1990), the magnetic field strength, F1, computed from high-resolution magnitudes, the elevation and azimuth angles (degrees) in heliographic (RTN) coordinates, and the magnetic field strength, F2, computed from hour averages of the components. The vector components of B can be computed from F2 and the two angles. The elevation angle is the latitude angle above or below the solar equatorial plane, and the azimuth angle is in the direction of orbital motion around the Sun from the projection of the Sun-to-spacecraft axis into the solar equatorial plane. The Voyager MAG experiment and coordinates are further described in the following publication: Behannon, K.W., M.H. Acuna, L.F. Burlaga, R.P. Lepping, N.F. Ness, and F.M. Neubauer, Magnetic-Field Experiment for Voyager-1 and Voyager-2, Space Science Reviews, 21 (3), 235-257, 1977. At the time of experiment proposal, it was expected that the required accuracy of the measurements would be 0.1 nT, determined by the combined noise of the sensors and the spacecraft field. The spacecraft magnetic field at the outboard magnetic field sensor, referred to as the primary unit, was expected to be 0.2 nT and highly variable, consistent with current estimates. Hence, the dual magnetometer design (Ness et al., 1971, 1973; Behannon et al. 1977) was adopted. At distances > 40 AU, the heliospheric magnetic fields are generally much weaker than 0.4 nT; the average magnetic field strength near 40 AU and 85 AU is about 0.15 nT and 0.05 nT, respectively. The use of roll calibrations lasting about 6 hours permits determination of the effective zero levels for the two independent magnetic axes that are perpendicular to the roll axis (which is nearly parallel to the radial vector to the Sun) at intervals of about 3 months. There is no roll calibration for the third magnetic axis. Comparison of the two derived magnetic vectors from the two magnetometers permits validation of the primary magnetometer data with an accuracy of 0.02 to 0.05 nT. A discussion of the uncertainties that must be considered when using these data is given in the Appendix of Burlaga et al. (1994) and in Appendix A of Burlaga et al. (2002). References: Behannon, K.W., M.H. Acuna, L.F. Burlaga, R.P. Lepping, N.F. Ness, and F.M. Neubauer, Magnetic-Field Experiment for Voyager-1 and Voyager-2, Space Science Reviews, 21 (3), 235-257, 1977. Burlaga, L.F., Merged interaction regions and large-scale magnetic field fluctuations during 1991 - Voyager-2 observations, J. Geophys. Res., 99 (A10), 19341-19350, 1994. Burlaga, L.F., N.F. Ness, Y.-M. Wang, and N.R. Sheeley Jr., Heliospheric magnetic field strength and polarity from 1 to 81 AU during the ascending phase of solar cycle 23, J. Geophys. Res., 107 (A11), 1410, 2002. Ness, N., K.W. Behannon, R. Lepping, and K.H. Schatten, J. Geophys. Res., 76, 3564, 1971. Ness et al., 1973. Coordinate Systems: Interplanetary magnetic field studies make use of two important coordinate systems, the Heliographic Inertial (HGI) coordinate system and the Heliographic (HG) coordinate system. The HGI coordinate system is use to define the spacecraft's position. The HGI system is defined with its origin at the Sun. There are three orthogonal axes, X(HGI), Y(HGI), and Z(HGI). The Z(HGI) axis points northward along the Sun's spin axis. The X(HGI)-Y(HGI) plane lays in the solar equatorial plane. The intersection of the solar equatorial plane with the ecliptic plane defines a line, the longitude of the ascending node, which is taken to be the X(HGI) axis. The X(HGI) axis drifts slowly with time, approximately one degree per 72 years. Magnetic field orientation is defined in relation to the spacecraft. Drawing a line from the Sun's center, the HGI origin, to the spacecraft defines the X axis of the HG coordinate system. The HG coordinate system is defined with its origin centered at the spacecraft. Three orthogonal axes are defined, X(HG), Y(HG), and Z(HG). The X(HG) axis points radially away from the Sun and the Y(HG) axis is parallel to the solar equatorial plane and therefore parallel to the X(HGI)-Y(HGI) plane too. The Z(HG) axis is chosen to complete the orthonormal triad. An excellent reference guide with diagrams explaining the HGI and HG systems may be found in L. F. Burlaga, MHD Processes in the Outer Heliosphere, Space and Science Reviews, 39, 255-316. Support data calib_flag_on, calib_flag_MF, and calib_flag_offset are added to file version 2. Variable calib_flag_on consists of points where bit 4 or 5 in variable magStatus equal 1. Variable calib_flag_MF represents observations where magnetometer was in cailbration mode. Variable calib_flag_offset represent delay between data points when the magnetometer was in calibration mode and data points where magStatus variable indicated calibration periods. Due to specific shape of magnetometer data profile variable calibration_flag_
Kodiak (KODK) Ground-based Vector Magnetic Field (L2) 0.5 s Data
Kodiak, AK, Ground-based Vector Magnetic Field Level 2 Data, 0.5 s Time Resolution, Station Code: (KODK), Station Location: (GEO Latitude 57.8, Longitude 207.6), THEMIS GBO Network
Wind Magnetic Field Investigation (MFI) Data at full resolution
This data product contains Wind MFI full resolution magnetic field magnitude and GSE and GSM Cartesian components. Time resolution is typically 0.046s or 0.092s from launch through 1997, and is 0.092s thereafter. Final Version 5 data are accessible to within about 3 months of current date, newly defined Version 4 data (with final Bz offsets and not-yet-final spacecraft position vectors) between 3 months and about 2 weeks of current date, and newly defined Version 3 data (with the most recently determined Bz offset value, not yet final for the Version 3 interval) from 2 weeks to about 2 days of current.
OMNI Combined Heliopheric Observations (COHO), Merged Magnetic Field, Plasma and Ephermeris, Definitive Hourly Data
Hourly Averaged Definitive Multispacecraft Interplanetary ParametersData. The Heliographic Inertial, HGI, Coordinates are Sun-Centered and inertially fixed with respect to an X-Axis directed along the Intersection Line of the Ecliptic and Solar Equatorial Planes. The Solar Equatorial Plane is inclined at 7.25° from the Ecliptic. This Direction was towards Ecliptic Longitude of 74.367° on 1 January 1900 at 1200 UT but because of Precession of the Celestial Equator, this Longitude increases by 1.4° per Century. The Z-Axis is directed Perpendicular and Northward from the Solar Equator, and the Y-Axis completes the Right-Handed Set. This System differs from usual Heliographic Coordinates, e.g. Carrington Longitudes, which are fixed in the Frame of the Rotating Sun. The RTN System is fixed at a Spacecraft or a Planet. The R-Axis is directed radially away from the Sun, the T-Axis is the Cross Product of the Solar Rotation Axis and the R-Axis, and the N-Axis is the Cross Product of the R-Axis and T-Axis. At 0° Heliographic Latitude when the Spacecraft is in the Solar Equatorial Plane, the N-Axis and Solar Rotation Axis are Parallel. Latitude and Longitude Angles of Solar Wind Plasma Flow are generally measured from the Radius Vector away from the Sun. In all cases, Latitude Angles are Positive for North-Going Flow. The Flow Longitude Angles have been treated differently for the Near-Earth Data, i.e. the OMNI, and for the Deep Space Data. The Flow is Positive for the Near-Earth Data when coming from the Right Side of the Sun as viewed from the Earth, i.e. flowing toward +Y from -X GSE or Opposite to the Direction of Planetary Motion. On the other hand, the Flow Longitudes for the Deep Space Spacecraft use the opposite Sign Convection, i.e. Positive for Flow in the +T Direction in the RTN System.
THEMIS-B: On Board spin fits (FIT) of Electric (EFI) and Magnetic (FGM) field. On-Board Spin-fit electric and magnetic field data
THEMIS-B: On Board spin fits of Electric (EFI) and Magnetic (FGM) fields. This file contains data EFI and FGM that has been despun on-board to 3 second resolution. It stores meta information like the number of points that contributed to each spin and the standard deviation of those points. For the EFI data it also stores variables with the Z component of the EFI data zeroed and the Z component of the EFI estimated using the E.B=0 equality. The need to use an estimated Z axis for the EFI is due to error in measurements from the EFI axial booms. These data are provided in DSL (despun spacecraft L-Z vector), GSM, and GSE coordinates.
Oulujarvi (OUJ) Ground-based Vector Magnetic Field (L2) 10.0 s Data
Oulujarvi, Finland, Ground-based Vector Magnetic Field Level 2 Data, 10.0 s Time Resolution, Station Code: (OUJ), Station Location: (GEO Latitude 64.5, Longitude 27.2), IMAGE Network
Tucson (TUC) Ground-based Vector Magnetic Field (L2) 1.0 s Data
Tucson, AZ, Ground-based Vector Magnetic Field Level 2 Data, 1.0 s Time Resolution, Station Code: (TUC), Station Location: (GEO Latitude 32.2, Longitude 249.3), USGS Network
Kuujjuaq (KUUJ) Ground-based Vector Magnetic Field (L2) 0.5 s Data
Kuujjuaq, QC, Ground-based Vector Magnetic Field Level 2 Data, 0.5 s Time Resolution, Station Code: (KUUJ), Station Location: (GEO Latitude 58.3, Longitude 291.8), THEMIS GBO/UCLA Network
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.