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2,288 results for “period”
Periodic Degassing Rhythms in Three Mineral Springs in the Neuwied Basin, Germany 2016
We present a geochemical dataset acquired during continual sampling over 7 months (bi-weekly) and 4 weeks (every 8 hours) in the Neuwied Basin, a part of the East Eifel Volcanic Field (EEVF, Germany). We used a combination of geochemical, geophysical, and statistical methods to describe and identify potential causal processes underlying the correlations of degassing patterns of CO2, He, Rn, and tectonic processes in three investigated mineral springs (Nette, Kärlich and Kobern). We provide for the first time, temporal analyses of periodic degassing patterns (1 day and 2-6 days) in springs. The temporal fluctuations in cyclic behavior of 4–5 days that we recorded had not been observed previously but may be attributed to a fundamental change in either gas source processes, subsequent gas transport to the surface, or the influence of volcano-tectonic earthquakes. Periods observed at 10 and 15 days may be related to discharge pulses of magma in the same periodic rhythm. We report the potential hint that deep low-frequency (DLF) earthquakes might actively modulate degassing. Temporal analyses of the CO2-He and CO2-Rn couples indicate that all springs are interlinked by previously unknown fault systems. The volcanic activity in the EEVF is dormant but not extinct. To understand and monitor its magmatic and degassing systems in relation to new developments in DLF-earthquakes and magmatic recharging processes and to identify seasonal variation in gas flux, we recommend continual monitoring of geogenic gases in all available springs taken at short temporal intervals.
Daily runoff and nutrient loads for the North Sea and the Baltic Sea based on modelling and observations for the period 1961 to 2019 and adapted to NEMO-SCOBI
<p>This dataset consists of daily values of runoff and reconstructed nutrient loads for the period 1961 to 2019 for the North Sea-Baltic Sea system. Both runoff and nutrient loads were obtained from a model simulation performed with the European application of the Hydrological Predictions for the Environment model v.3.1.8 (E-HYPE). This dataset includes a more realistic number of river outlets than those from observational-based datasets, as not all rivers are monitored, and captures well the interannual variability of all parameters. However, the E-HYPE v.3.1.8 was calibrated to represent 2010 and thus cannot simulate all historical changes related to land management (i.e., the increase of fertilizers in the 1960s). Consequently, the observed rise of nutrients from land due to increased fertilizers and the consequent reduction due to nutrient regulation policy in the 1980s is not captured in the outputs from E-HYPE directly. In the North Sea and the Baltic Sea, this is of primary importance for management policy in eutrophication and deoxygenation. Therefore, we have adapted the E-HYPE nutrient loads based on yearly estimates of historical loads that use riverine concentrations, so that the high tempo-spacial resolution is kept, but with a decadal variability that is closer to reality. This dataset is mainly intended as river forcing for biogeochemical-ocean models (i.e. NEMO-SCOBI), but can also provide information on rivers that are not included in monitoring programs. Information on the dataset and the methods used to create it is given as a downloadable PDF file (E-HYPE DecVar documentation.pdf) together with two datasets and the mesh grid file (area_NEMO-Nordic.nc). The datasets are yearly netCDF files one containing daily runoff and nutrient loads for phosphate, nitrate, ammonium, organic nitrogen and organic phosphorus (zip_ehypeDecVar.zip) and the other one provides monthly silica loads (zip_silica.zip). </p>
Estimated Inundation Periods in the Yolo Bypass, California, 1998 – 2024
Largely supported by the Interagency Ecological Program (IEP), California Department of Water Resources (DWR) has operated a fish monitoring program in the Yolo Bypass, a seasonal floodplain and tidal slough, since 1998. The objectives of the Yolo Bypass Fish Monitoring Program (YBFMP) are to: 1. Collect baseline data on water quality, chlorophyll, lower trophic level biota, and fish in the Yolo Bypass to monitor spatial and temporal changes in trends and abundance. 2. Analyze and communicate Yolo Bypass data with interested parties and the scientific and management communities to address pertinent management-related questions. 3. Provide technical expertise on Yolo Bypass aquatic ecology and monitoring and sampling methods. The YBFMP operates a rotary screw trap and fyke trap and conducts biweekly beach seine and lower trophic surveys in addition to maintaining water quality instrumentation in the bypass. The YBFMP serves to fill information gaps regarding environmental conditions in the bypass that trigger migrations and enhanced survival and growth of native fishes, as well as provide data for IEP synthesis efforts. YBFMP staff also conduct analyses of YBFMP monitoring data to address pertinent management related questions as identified by IEP. The Yolo Bypass has been identified as a high restoration priority by the National Marine Fisheries Service and US Fish and Wildlife Service Biological Opinions for Delta Smelt, Winter and Spring-run Chinook salmon and by California EcoRestore. The YBFMP informs the restoration actions that are mandated or recommended in these plans and provides critical baseline data on the ecology of the bypass and how it interacts with the broader San Francisco Estuary. YBFMP’s data is often accompanied by information on whether the Yolo Bypass is inundated, as water quality, and species composition and abundance can be greatly altered during inundation. This dataset was created to consistently estimate inundation over time. Estimating
Shell geochemistry and environmental instability along the Georgia Coast during the Late Archaic Period (5000 - 3800 BP)
This dataset includes stable oxygen isotope (δ18O) data collected from eastern oysters (n=19) (Crassostrea virginica) and hard clams (n=59) (Mercenaria spp.) from the Late Archaic (ca. 50000-3500 cal. BP) Sapelo Shell Rings on Sapelo Island, Georgia. A total of 1064 isotope samples were collected and analyzed from these shells. The data are part of a larger project reconstructing paleo-climate and Native American adaption and resilience in the context of climate instability along the South Atlantic coast of North America during the Late Archaic Period. Shell isotope samples were collected by multiple researchers over the last decade. Carey Garland added to and cleaned the data between June 2020 and December 2021. The dataset was structured to include site name, location, and provenience (e.g., unit, level, etc.) associated with each shell analyzed, as well as all raw isotope data. The original database contains sensitive information, such as the specific location of archaeological sites. If a professional archaeologist needs site location information, they can contact the Georgia Archaeological Site File.
Data and code of the article: "Early Warning Signals of the Termination of the African Humid Period(s)"
<p>Data and MATLAB Code of the article Trauth, M.H., Asrat, A., Fischer, M.L., Hopcroft, P.O., Foerster, V., Kaboth-Bahr, S., Kindermann, K., Lamb, H.F., Marwan, N., Maslin, M.A., Schaebitz, F., Valdes, P.J. (2024) Early Warning Signals of the Termination of the African Humid Period(s), Nature Communications, https://doi.org/10.1038/s41467-024-47921-1. The individual directories contain the data and the MATLAB code used to generate Fig. 1 and 2 and Supplementary Fig. 1 to 7 published with the article.</p>
German weather services (DWD) multi annual meteorological rasters for the climate period 1991-2020 refined to 25m grid
<h1>Overview</h1> <p>These are two multi-annual raster products from the german weather service, that got refined from a 1km grid to a 25m grid, by using a local regression model.</p> <p>The base rasters from DWD are:</p> <ul> <li>HYRAS precipitation</li> <li>REGNIE precipitation</li> <li>DWD-grid (precipitation, potential evapotranspiration and temperature 2m above ground)</li> </ul> <p>To refine the grids the Copernicus DEM with a resolution of 25m got used. For every cell a linear regression model got created, by selecting the multi-annual rasters value and the elevation, from the original digital elevation model that was used by the DWD to create the raster, in a certain window around the cell. This window was at least 2 cells around the considered cell, so 5x5=25 cells. If the standard deviation of the elevation in this window was less than 4m, more neighbooring cells are considered until a maximum of 13x13=169 cells are considered. This widening of the window was necessary for flat regions to get a reasonable regression model.</p> <p>Out of these combinations of elevation and climate parameter a linear regression model was build. These regression models are then applied to the finer digital elevation model with its 25m resolution from Copernicus.</p> <p>The following image illustrates the generation of the refined rasters on a small example window:</p> <p></p>
The mass of the lowermost stratosphere (LMS): LMS mass calculation and trends in five reanalyses for the time period 1979–2019
<p><strong>Description</strong></p> <p>Python code to calculate the mass of the lowermost stratosphere (LMS) and investigate LMS mass trends with the dynamic linear regression model (DLM, Laine et al. 2014, Alsing 2019) as presented in Weyland et al. (2024). The LMS mass is calculated via a three dimensioal integral, following Appenzeller et al. (1996), given an upper and lower LMS boundary surface (4D pressure fields). Here, the lateral boundary is determined via the intersection of the tropopause with the 350K isentrope (4D pressure field). The upper LMS boundary can be defined by the isentrope according to the potential temperature at the tropical lapse rate tropopause (PPT10mean) or the cold point (PPTcp10mean) or approximated by the 380K isentrope. See Weyland et. al (2024) for further description and context.</p> <p>The mass calculation is performed with calc_LMS_mass.py.</p> <p>The DLM trend analysis is conducted with dlm_LMS_mass.py, using dlm_modules.py. In order to be able to use the provided code, the dlmmc model code has to be downloaded from <a href="https://github.com/justinalsing/dlmmc">https://github.com/justinalsing/dlmmc</a> (Alsing 2019).</p> <p>The neccesary 3D (time, lat, lon) pressure fields to define the LMS boundaries are provided for the time period 1979–2019<sup>1</sup> from five modern reanalyses: ERA5<sup>2</sup> (Hersbach et al., 2020), ERA-Interim (Dee et al., 2011), MERRA-2 (Gelaro et al., 2017) and JRA-55 (Kobayashi et al., 2015) and JRA3Q (Kosaka et al., 2024):</p> <ul> <li>lrtp*.nc : <ul> <li>3D (time, lat, lon) pressure, temperature and potential temperature at the WMO lapse rate tropopause for the time period 1979-2019<sup>1</sup>, derived from monthly mean data on pressure levels from the respective reanalysis. The lapse rate detection algorithm closely follows that of Birner et al. (2010), based on the work of Reichler et al. (2003). The lapse rate tropopause can serve as the lower LMS boundary. The potential temperature at the lapse rate tropopause between 10°N-10°S is used to define a „dynamic“ upper LMS boundary (PPT10mean).</li> </ul> </li> </ul> <ul> <li>cp*.nc : <ul> <li>3D (time, lat, lon) pressure, temperature and potential temperature at the cold point for the time period 1979–2019<sup>1 </sup>, derived from monthly mean data on pressure levels from the respective reanalysis. The cold point here is defined by the pressure corresponding to a lapse rate of 0K/km. The potential temperature at the cold point between 10°N–10°S is used to define a „dynamic“ upper LMS boundary (PPTcp10mean).</li> </ul> </li> </ul> <ul> <li>ppt10mean*.nc : <ul> <li>3D (time, lat, lon) pressure at the isentrope accroding to the potential temperature at the tropical (10°N–10°S) lapse rate tropopause (PPT10mean) for the time period 1979–2019<sup>1</sup>, derived from lrtp*.nc. PPT10mean can be used to define the upper LMS boundary.</li> </ul> </li> </ul> <ul> <li>pptcp10mean*.nc : <ul> <li>3D (time, lat, lon) pressure at the isentrope accroding to the potential temperature at the cold point between 10°N-10°S (PPTcp10mean) for the time period 1979–2019<sup>1</sup>, derived from cp*.nc. PPTcp10mean can be used to define the upper LMS boundary.</li> </ul> </li> </ul> <ul> <li>p380K*.nc : <ul> <li>3D (time, lat, lon) pressure at the 380K isentrope for the time period 1979–2019<sup>1</sup>, derived from monthly mean data on pressure levels from the respective reanalysis. The 380K isentropic pressure field can be used to approximate the upper LMS boundary.</li> </ul> </li> </ul> <ul> <li>p350K*.nc : <ul> <li>3D (time, lat, lon) pressure at the 350K isentrope for the time period 1979–2019<sup>1</sup>, derived from monthly mean data on pressure levels from the respective reanalysis. The 350K isentrope is used to determine the lateral LMS boundaries via its intersection with the tropopause. This intersection approximates the location of the subtropical jet streams and the maximum PV-gradient, marking a transport barrier. It is determined by the sign change of the pressure difference between the tropopause and the 350K isentrope.</li> </ul> </li> </ul> <ul> <li>my_enso_79-19.txt : <ul> <li>Regressor to account for El-Niño/Southern Oscillation for the time period 1979–2019. Source: <a href="https://psl.noaa.gov/enso/mei/">https://psl.noaa.gov/enso/mei/</a>, last accessed: 11 July 2023. The data has been normalized and centered around zero. The use of regressors is optional.</li> </ul> </li> </ul> <ul> <li>my_qbo30_79-19.txt and my_qbo50_79-19.txt : <ul> <li>Regressor to account for the quasi-biennial oscillation at 30 and 50 hPa for the time period 1979–2019. Source: <a href="https://www.geo.fu-berlin.de/met/ag/strat/produkte/qbo/qbo.dat">https://www.geo.fu-berlin.de/met/ag/strat/produkte/qbo/qbo.dat</a>, last accessed: 11 July 2023. The data has been normalized and centered around zero. The use of regressors is optional.</li> </ul> </li> </ul> <ul> <li>my_SAOD_79-19.txt : <ul> <li>Regressor to account for stratospheric (volcanic) aerosol optical depth for the time period 1979-2019. Source: <a href="https://asdc.larc.nasa.gov/project/GloSSAC/GloSSAC_1.0">https://asdc.larc.nasa.gov/project/GloSSAC/GloSSAC_1.0</a>, last accessed: 11 July 2023. The data has been normalized. The use of regressors is optional.</li> </ul> </li> </ul> <p> </p> <p>For further details see Weyland et al. (2024).</p> <p><sup>1</sup>Note that the ERA-Interim time series ends in 2018 and that the MERRA-2 time series starts in 1980.</p> <p><sup>2</sup>For the time period 2000–2006, the sub-reanalysis ERA5.1 replaces ERA5, correcting the reanalysis for a cold bias in the lower stratosphere (Simmons et al., 2020).</p> <p> </p> <p><strong>How to use – example: </strong></p> <p>Assuming you are interested in the LMS mass between a lower boundary (-lb, e.g., the lapse rate tropopause) and an upper boundary (-ub, e.g., the 380K isentrope) in ERA5 for the entire Northern hemisphere (-lat=NH) covering the time period 1979-2019:</p> <p> </p> <ul> <li> <p>Calculate the respective LMS mass timeseries:</p> <p><strong>$ python calc_LMS_mass.py -lb=lrtp_ERA5.nc -ub=p380K_ERA5.nc -latb=p350K_ERA5.nc -lat=NH -fout=LMS_mass_ERA5_lrtp_p380K_NH.nc</strong></p> <p>Isentropic pressure at 350K (-latb) is required to determine the lateral LMS boundary. The LMS mass time series together with an uncertainty estimate is saved to a netCDF file (-fout), e.g. „LMS_mass_ERA5_lrtp_p380K_NH.nc“.</p> </li> </ul> <p> </p> <ul> <li> <p>Perform a DLM trend analysis for your LMS mass time series, here LMS_mass_ERA5_lrtp_p380K_NH.nc (-mf) :</p> <p>Download the DLM model code (dlmmc) from <a href="https://github.com/justinalsing/dlmmc">https://github.com/justinalsing/dlmmc</a> (Alsing 2019) and save the „dlmmc“ folder, containing the DLM modules in your working directory.</p> </li> </ul> <p><strong>$ python dlm_lms_mass.py -mf=LMS_mass_ERA5_lrtp_p380K_NH.nc -s=2000</strong></p> <p>In this example, the DLM will provide 2000 samples (-s) after an additional 1000 warm-up samples.</p> <p>The DLM time series, containing 2000 samples (-s) per time step, is saved to a netCDF file. The name of the output file can be specifyed with -fout. Default is „dlm_“ + mf, i.e. „dlm_ LMS_mass_ERA5_lrtp_p380K_NH.nc“ in this example.</p> <p>The function dlm_lms_mass.dlm_lms_mass contains an option to visualize the DLM result (plot=True). Furthermore, it can be specified whether the DLM should be run with regressors (use_regressors=True) or without regressors (use_regressors=False).</p> <p>See the DLM documentation (Laine et al. 2014, Alsing 2019) for further options.</p> <p> </p> <p><strong>Funding</strong>: This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – TRR 301 – Project-ID 428312742: “The tropopause region in a changing atmosphere”.</p>
Temporal patterns of leaf litter inputs into a stream over a four-year period (2011-2014), Arbúcies, Catalonia, Spain.
Data based on estimations of leaf litter inputs from riparian trees into a stream reach over a 4 years period (2011-2014). Data was collected in Arbucies, Barcelona is a forested stream with no human pressure (i.e., pristine). Data contains values from 4 riparian tree species: AL (alder), AS (ash), BL (Black Locust) and BP (Black Poplar). Units are in mg. Estimations were extracted from sampling leaf litter input into the stream during the study period (30 samplings per year) and fitting Gaussian-type models (P<0.001, r2>0.60). Data also includes daily-basis discharge flow estimations based on discrete measure of flow using salt dilution technique and water level sensor data.
SBC LTER: Daily averages of modeled significant wave height (Hs) and peak wave period (Tp) in the Santa Barbara Coastal area from the Coastal Data Information Program - Monitoring and Prediction System (CDIP MOP)
From http://cdip.ucsb.edu: The Coastal Data Information Program (CDIP) is a research group at Scripps Institution of Oceanography that monitors coastal waves and nearshore sand levels on regional scales. CDIP maintains a network of optimally-placed, directional wave buoys from San Diego to Eureka. The buoy measurements are used to initialize a high spatial resolution (100m x 100m) linear spectral wave propagation model. The resulting hourly hindcasts and nowcasts of CA coastal wave conditions have a level of accuracy that is not possible with more traditional wind-wave generation models that are initialized with modeled wind fields.
Data from: "Deep Generative Modeling of Periodic Variable Stars Using Physical Parameters"
<p>This dataset was used for the training of a conditioned Variational Autoencoder that generates physically informed light curves of periodic variable stars. The light curves correspond to data obtained from The Optical Gravitational Lensing Experiment (<a href="https://ui.adsabs.harvard.edu/abs/1992AcA....42..253U/abstract">OGLE</a>), while ancillary information was obtained from the Gaia Data Release 2 (<a href="https://ui.adsabs.harvard.edu/link_gateway/2016A&A...595A...1G/doi:10.1051/0004-6361/201629272">GAIA DR2</a>). This repository contains the preprocessed OGLE light curves and the GAIA measurements corresponding to each cross-matched source. We also provided a subsample of cross-matched sources that were carefully validated following several steps described in the companion article (paper reference).</p> <p>This dataset is realized in tandem with the corresponding <a href="https://github.com/jorgemarpa/PELS-VAE">GitHub</a> and <a href="https://arxiv.org/abs/2005.07773">article</a>.</p> <p> </p> <p> </p>
High-mountain Asia glacier elevation change trend (dh/dt) map for the period spanning 2000 to 2018
<p>See manuscript for methodology and dataset description:</p> <p>Shean DE, Bhushan S, Montesano P, Rounce DR, Arendt A and Osmanoglu B (2020) A Systematic, Regional Assessment of High-Mountain Asia Glacier Mass Balance. Front. Earth Sci. 7:363. DOI: 10.3389/feart.2019.00363</p> <p>https://www.frontiersin.org/articles/10.3389/feart.2019.00363/full</p> <p>GeoTiff header contains relevant metadata and georeferencing information (30 m pixel size, Albers Equal Area projection). Proj string is '+proj=aea +lat_1=25 +lat_2=47 +lat_0=36 +lon_0=85 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs'</p> <p>External overview file (.ovr) contains pyramidal overviews for improved visualization performance at different zoom levels.</p>
MeteoSerbia1km: the first daily gridded meteorological dataset at a 1-km spatial resolution across Serbia for the 2000–2019 period
<p>MeteoSerbia1km is the first daily gridded meteorological dataset at a 1-km spatial resolution across Serbia for the 2000–2019 period. The dataset consists of five daily variables: maximum, minimum and mean temperature, mean sea level pressure, and total precipitation. Besides daily summaries, it contains monthly and annual summaries, daily, monthly, and annual long term means (LTM). Daily gridded data were interpolated using the Random Forest Spatial Interpolation methodology based on Random Forest and using nearest observations and distances to them as spatial covariates, together with environmental covariates.</p> <p>Complete script in R and datasets used for modelling, tuning, validation, and prediction of daily meteorological variables are available <a href="https://github.com/AleksandarSekulic/MeteoSerbia1km">here</a>.</p> <p>If you discover a bug, artifact or inconsistency in the MeteoSerbia1km maps, or if you have a question please use <a href="https://github.com/AleksandarSekulic/MeteoSerbia1km/issues">this channel</a>.</p> <p>File naming convention of .zip files and containing MeteoSerbia1km files:</p> <ul> <li>Daily summaries per year: day_<em>yyyy</em>_<em>proj</em>.zip <ul> <li><em>var</em>_day_<em>yyyymmdd</em>_<em>proj</em>.tif</li> </ul> </li> <li>Monthly summaries: mon_<em>proj</em>.zip <ul> <li><em>var</em>_mon_<em>yyyymm</em>_<em>proj</em>.tif</li> </ul> </li> <li>Annual summaries: ann_<em>proj</em>.zip <ul> <li><em>var</em>_ann_<em>yyyy</em>_<em>proj</em>.tif</li> </ul> </li> <li>Daily, monthly and annual LTM: ltm_<em>proj</em>.zip <ul> <li>daily LTM: <em>var</em>_ltm_day_mmdd_<em>proj</em>.tif</li> <li>monthly LTM: <em>var</em>_ltm_mon_mm_<em>proj</em>.tif</li> <li>annual LTM: <em>var</em>_ltm_ann_<em>proj</em>.tif</li> </ul> </li> </ul> <p>where:</p> <ul> <li><em>var</em> is a daily meteorological variable name - tmax, tmin, tmean, slp, or prcp</li> <li><em>proj</em> is a dataset projection - wgs84 or utm34</li> </ul> <p>Units of the dataset values are</p> <ul> <li>temperature (Tmean, Tmax, and Tmin) - tenths of a degree in the Celsius scale (℃)</li> <li>SLP - tenths of a mbar</li> <li>PRCP - tenths of a mm</li> </ul> <p>All dataset values are stored as integers (INT32 data type) in order to reduce the size of the GeoTIFF files, i.e., temperature values should be divided by 10 to obtain degrees Celsius, and the same for SLP and PRCP to obtain millibars and millimeters.<br> </p>
Atrial Models with Personalized Effective Refractory Period
<h1>Impact of Effective Refractory Period Personalization on Prediction of Atrial Fibrillation Vulnerability</h1> <div> </div> <div> <div><strong>Authors:</strong> Patricia Martínez Díaz, Christian Goetz, Albert Dasi, Laura Anna Unger, Annika Haas, Olaf Dössel, Armin Luik, Axel Loewe</div> <div>patricia.martinez@kit.edu / publications@ibt.kit.edu</div> <div><a href="https://doi.org/10.1093/europace/euad122.542">doi:10.1093/europace/euad122.542</a></div> <div> </div> <div>This dataset contains 7 atrial meshes, 6 left atria and 1 right atrium, derived from electroanatomical mapping and measurements of the effective refractory period (ERP), bipolar voltage (bi) and local activation times (lat). The meshes include annotations and fibers and are ready for simulations in the cardiac electrophysiology simulator <a href="https://doi.org/10.1016/j.cmpb.2021.106223">openCARP</a>. We also provide the code to reproduce 272 reentries by reading the selected parameters.par and state.roe files. The meshes were generated using <a href="https://github.com/KIT-IBT/AugmentA">AugmentA code</a> and the simulated reentries were induced following the <a href="https://doi.org/10.3389/fphys.2021.656411">PEERP protocol</a> by Azzolin et al. </div> <div> </div> <h2>Folder structure</h2> <div>The code is located in the `src` folder, the meshes in the `data` folder and the reentries in the `results` folder. </div> <div>```</div> <div>src/</div> <div> |-- run.py</div> <div> |-- induceReentry.py</div> <div> |-- getStimPoints.py</div> <div> |-- element_tag.csv</div> <div> |-- al_mk_H.par</div> <div> |-- requirements.txt</div> <div> |-- reproduceReentry.py</div> <div>data/</div> <div> |-- meshes/</div> <div> |-- P1/ </div> <div> |-- P1_with_erp_lat_bi.vtk </div> <div> |-- ERP.pts</div> <div> |-- ERP_values.txt</div> <div> |-- ablation.pts</div> <div> |-- LA_stim_points_2cm.pts</div> <div> |-- bilayer/</div> <div> <div> |-- nodal_adjustment/</div> <div> |--PARAMETER_SCENARIO.adj (e.g. Gto_continuous.adj)</div> </div> <div>.</div> <div>.</div> <div>.</div> <div> |-- P7 </div> <div>results/</div> <div> |-- MESH_SCENARIO_CV/ (e.g P1_continuous_0.3) </div> <div> |-- point_X_beat_Y</div> <div> |-- MESH_SCENARIO_CV_PERTURBATION_SET/ (e.g P1_continuous_0.7_2_1) </div> <div> |-- point_X_beat_Y </div> <div>README.md</div> <div>```</div> <div> <ul> <li>`src`: contains the source files needed to run PEERP protocol <ul> <li>`run.py` This is the main function to run the pacing protocol (not needed to run if reentries are only reproduced, check reproduceReentry.py)</li> <li>`induceReentry.py` Contains a list of pacing protocols. The PEERP protocol is included here</li> <li>`getStimPoints.py` Extract the stimulation points</li> <li>`element_tag.csv` Region tag numbering</li> <li>`al_mk_H.par` Par file with ionic scaling factors for three states; H:Healthy, M:Mild, S:Severe</li> <li>`requirements.txt` Packages to create the virtual enviroment. (This was my output of ```pip3 list> requirements.txt```)</li> <li>`reproduceReentry.py` Reentries can be reproduced given a selected folder where the .par and .roe files are stored.</li> </ul> </li> <li>`data`: contains the `meshes` folder with the bilayer meshes in openCARP (.elem, .lon and .pts) and .vtk format. Synthetic fibrotic distributions are included in the the .regele files. <ul> <li>`meshes/P1/P1_with_erp_lat_bi.vtk` Mesh with ERP, LAT and bipolar voltage data</li> <li>`meshes/P1/ERP_values.txt/` measured ERP data</li> <li>`meshes/P1/ERP.pts/` electrode coordinates where ERP data was measured</li> <li>`meshes/P1/ablation.pts/` electrode coordinates where tissue was ablated</li> <li>`meshes/P1/LA_stim_points_2cm.pts` Stimulation points for the PEERP protocol</li> <li> `meshes/P1/bilayer/LA_bilayer_with_fiber_slow_conductive.regele` Element ids corresponding to regions of low voltage (< 0.5mV)</li> <li>`meshes/P1/bilayer/LA_bilayer_with_fiber_scar.regele` Element ids corresponding to regions of low voltage (< 0.1mV)</li> <li>`meshes/P1/bilayer/LA_bilayer_with_erp_regions_um.vtk` Bilayer mesh with a discrete split where each region has a single ERP value</li> <li>`meshes/P1/bilayer/LA_bilayer_with_fiber_with_fibrosis.vtk` Bilayer mesh with fibrosis informed by low voltage areas</li> <li>`meshes/P1/bilayer/LA_bilayer_with_erp_continuous_um.vtk` Bilayer mesh with a continuous ERP distribution by interpolation of measured ERP data</li> <li>`meshes/P1/bilayer/LA_bilayer_with_erp_continuous_2ms_um.vtk` Bilayer mesh with a continuous ERP distribution by interpolation of measured ERP data with +- 2ms perturbation</li> </ul> </li> </ul> <p>We studied 7 different scenarios: </p> </div> <ol> <li>Monoregion scenario with no ERP personalization, where all nodes had the same ERP</li> <li>Control scenario with no ERP personalization, where ERP nodes of certain defined anatomical regions where modified as reported in Loewe et al. 2015 </li> <li>Regional scenario with ERP personalization, where each region had a single ERP value derived from clinical measurement</li> <li>Continuous scenario with ERP personalization, where the ERP distribution was generated by interpolation of measured ERP data</li> <li>Control scenario with fibrosis, where elements corresponding to regions of low voltage (bi<0.5 mV) where set as slow or non conducing elements</li> <li>Continuous scenario with fibrosis, where elements corresponding to regions of low voltage (bi<0.5 mV) where set as slow or non conducing elements</li> <li>Continuous scenario where ERP measurements with additional perturbation draw from a uniform distribution. The perturbations were 2,5,10 and 20 ms, and we repeated this set 5 times for P6</li> </ol> <p>In summary, we provide the following data: </p> <div> <ul> <li>7 meshes for openCARP simulations</li> <li>7 meshes in vtk format with continuous ERP distribution</li> <li>27 meshes in vtk format with continuous ERP distribution with perturbed ERP with 2,5,10 and 20ms from a random uniform distribution</li> <li>7 meshes in vtk format with regional ERP</li> <li>7 meshes in vtk format with ERP, LAT and bipolar voltage</li> <li>7 ablation set points</li> <li>7 ERP set points with their corresponding values</li> <li>209 reentries generated under 4 ERP scenarios (monoregion, control,regional,continuous) run with a conduction velocity of 0.7 0.5 and 0.3 m/s</li> <li>26 reentries generated under 2 scenarios ERP+Fibrosis (control + continuous) run with a conduction velocity 0.3 m/s</li> <li>37 reentries induced with continuous ERP for patient P3 @CV 0.3 for the sensitivity analysis </li> </ul> </div> <h2>Create a dynamic Courtemanche model</h2> <p>As we will modify the ionic parameters on a nodel basis you will need to create a dynamic Courtemanche model and then declare the variables (ionic conductances) you need to modify. In your openCARP installation folder, go to the `limpet` copy the Courtemanche.model file</p> <p>```<br>cd openCARP/physics/model/limpet<br>cp Courtemanche.model Courtemanche_nodal.model<br>vim Courtemanche_nodal.model<br>```</p> <p>Then add on top the parameters that need to be modified on a nodal-basis:</p> <p>```<br>group {<br> GK1 ;<br> Gto ;<br> GKr ;<br> GKs ;<br> GCaL ;<br> factorGKur ;<br> maxINaCa ;<br> maxIpCa ;<br>} .nodal();</p> <p>```</p> <p>Then you would need to recompile openCARP. In the terminal, go to your openCARP's top level folder:<br>```<br>cd openCARP/ <br>```</p> <p>Configure CMake with updated imp_list.txt via:<br>```<br>cmake -S. -B_build -DUPDATE_IMPLIST=ON</p> <p>```<br>Run the CMake building process:<br>```<br> cmake --build _build<br>```<br> This will generate the `.h` and `.cc` files for your dynamic model inside `physics/limpet/src/imps_src`</p> <p>**Note:** If you want to add or modify a model file after openCARP was compiled, it is possible to first clean the previous generated files during compilation by running `make clean` before recompiling openCARP.</p> <p>If you compile your own version of openCARP, then you can modify the settings.yaml file, to point to your openCARP version with the dynamic model.<br>```<br>cd .config/carputils<br>subl settings.yaml <br>```<br>Add the build name:</p> <p>```<br>CARP_EXE_DIR:<br> CPU: /Users/lm104/Documents/OpenCARP/opencarp/_build/bin<br> NODAL: /Users/lm104/Documents/OpenCARP/openCARP_nodal_adj/_build/bin<br>```<br>You can check that the new dynamic model is there by calling bench<br>```<br>bench -—list-imps<br>bench —-imp Courtemanche_nodal --imp-info<br>```</p> <p>You can find additional information about dynamic models <a href="https://opencarp.org/documentation/examples/01_ep_single_cell/04_limpet_fe">here</a>.</p> <h2>Reproduce the reentries </h2> <div>You can generate the .igb file of a specific reentry by selecting the corresponding folder in the results directory. An example is given to reproduce the reentry in P1_bi_M_LA/point_0_beat_2/reproduce_reentry.igb. Select the folder `--par_file_directory`and set `--tend` to define the duration of the simulation in miliseconds.</div> <div>_HINT: We recommend keeping the folder structure so that the other parameters, such as: mesh, scenario, state and chamber, can be read from the --par_file_directory. Otherwise, the meshes and results directories need to be modified._</div> <div>```</div> <div>cd src/</div> <div>reproduceReentry.py --par_file_directory ../results/P1_bi_M_LA/point_0_beat_2 --tend 1500</div> <div> </div> <div>```</div> <div> </div> <h3>Preparation before running the PEERP pacing protocol</h3> <div>Follow the next steps if you want to run the PEERP pacing protocol, either for the provided meshes or for your own meshes. To run the PEERP protocol in a controlled environment, it is recommended, before running the run.py, to create a virtual environment. Go to your terminal and type: </div> <div>```</div> <div>cd src/</div> <div>python3 -m venv ./myEnv</div> <div>source ./myEnv/bin/activate</div> <div>pip3 install -r requirements.txt</div> <div>```</div> <div> </div> <div>You need to add carputils to your `PATH`. You can run the code in the terminal or use and IDE to debug the code. </div> <div>Note: I am using PyCharm 2020.3. and in Settings --> Python interpreter --> show all and then in the (+) symbol, add the path to carputils there:</div> <div> </div> <div>Otherwise you can add this extra lines at the beginning of `run.py``:</div> <div>```</div> <div># Replace '/path/to/carputils' with the actual path to your carputils package</div> <div>carputils_path = '/path/to/carputils'</div> <div> </div> <div># Add the carputils path to sys.path</div> <div>sys.path.append(carputils_path)</div> <div>```</div> <h3>Run the PEERP protocol</h3> <div> </div> <div>The following example runs the PEERP from a single stimulation point. If you want to run PEERP over all the points, simply add the flag --run_all_points 1 </div> <div>```</div> <div>cd src/</div> <div>python3 run.py --giL 0.4166 --geL 1.458 --cv 0.8 --mesh monoatrial --protocol PEERP --pacing 122718 --stim_file LA_stim_points.txt --geometry LA_bilayer_with_fiber_um --cell_bcl 500 --model Courtemanche --ionic_prop_file al_mk_S.par --max_n_beats_PEERP 1 --overwrite-behaviour overwrite</div> <div>```</div> <div> </div> <h3>Running your own experiment and making your own changes</h3> <div>Extract the stimulation points on your mesh, where the PEERP protocol will be run: </div> <div>```</div> <div>python3 getStimPoints.py --mesh monoatrial --tolerance 20000 --stim_file LA_stim_points.txt --chamber LA</div> <div>```</div> <div> </div> <div>Tune conduction velocity (CV) and conductivites. The code expects the intracellular end extracellular longitudinal conductivity values as an input. We used `tuneCV` to fit CV=0.7m/s with dx=0.4mm and dt=20us</div> <div>If you want to adjust the values, run in the terminal:</div> <div>```</div> <div>tuneCV --resolution 400 --model Courtemanche --velocity 0.7 --converge True --sourceModel monodomain --surf True --dt 20</div> <div>```</div> <div>You can provide the location of the start of the activation by selecting the desired point ID:</div> <div>- Load the mesh in Paraview (or Meshalyzer)</div> <div>- click on the ? symbol</div> <div>- save the ID and change the `--pacing` argument </div> <div> </div> <div>Call `run.py` with a new mesh. The protocol starts by prepacing the mesh and then using the last beat as initial condition tu run the PEERP.</div> <div>Be aware that for a monoatrial mesh you might need to give the new id for the location of the earliest activation. Change `12345` to your desired point ID.</div> <div>```</div> <div>python3 run.py --mesh newMesh --pacing 12345 --protocol prepace --stim_file LA_stim_points.txt</div> <div>```</div> <div> </div> <div>Run the protocol with different electrical remodelling stage. You can change the .par file or select one file from the three provided: </div> <div>```</div> <div>python3 run.py --mesh newMesh --pacing 12345 --protocol PEERP --stim_file LA_stim_points.txt --args.ionic_prop 'l_mk_M.par'</div> <div>```</div> <div> </div> <div>You can also try to run a biatrial example. The biatrial mesh is also provided. You need to extract the points on the RA surface using `getStimPoints.py`, to run the RA experiment: </div> <div>```</div> <div>cd src</div> <div>python3 getStimPoints.py --mesh biatrial --tolerance 20000 --stim_file RA_stim_points.txt --chamber RA</div> <div>```</div> <div>Then run PEERP twice, one per each chamber:</div> <div> </div> <div>```</div> <div>python3 run.py --mesh biatrial --geometry LA_RA_bilayer_with_fiber --pacing 12345 --stim_file LA_endo_2cm.txt --args.ionic_prop 'l_mk_M.par'</div> <div>python3 run.py --mesh biatrial --geometry LA_RA_bilayer_with_fiber --stim_file LA_stim_points.txt --args.ionic_prop 'l_mk_M.par'</div> <p> </p> </div> <p> </p>
Plant Atlas 2020 — British and Irish vascular plant and charophyte 10 x 10 km grid square locations, subdivided by survey period, up to 2019
<p><span>This resource provides the data behind the 10 × 10 km grid square (hectad) British and Irish distribution maps, for 3,497 taxa, presented in both the Plant Atlas 2020 book and website (</span><a href="http://www.plantatlas2020.org"><span><span>www.plantatlas2020.org</span></span></a><span><span>), subdivided by time period<a><span>.</span></a> These are presence-only data, indicating where a taxon was reported from a hectad, within a given</span><span><span></span></span></span><span> multi-year period, up to 2019. These time periods cover the 20<sup>th</sup> Century, but also extend back to the earliest botanical records known for Britain and Ireland in the first period (pre-1930). These 10 km square presences are based on over 30 million records, collected mainly by volunteer recorders of the Botanical Society of Britain and Ireland (BSBI) between 2000 and 2019, as well as previous nationwide surveys undertaken in the 1950s and 1990s.</span></p>
BioVars - bioclimatic datasets for Europe based on a large regional climate ensemble for periods between 1971 to 2098
<p>We present 26 bio-climatic variables that are calculated based on a large ensemble consisting of 70 bias-adjusted GCM-RCM (Global Climate Model – Regional Climate Model) simulations for 1971 to 2098. Both, the historic and the projection periods were calculated using the same models to ensure consistency between the periods. The variables are validated against E-OBS observations from which we calculated the same bio-climatic variables. For projection periods we chose 20 year ranges between 2021 to 2098. Here, we offer two versions of them 1) variables separated into RCP 2.6, 4.5 and 8.5 including the 5th, 50th and 95th percentiles among the realisations and within the RCPS. And 2) variables per realisation separately. We then extracted the temporal 5th, 50th and 95th percentile per period as representing values. Each zipped file contains these 26 bio-climatic variables according to their aggregation. The variables and the units are explained within the data descriptor publication. </p> <p> </p> <p><strong>File descriptions</strong></p> <ul> <li>bioVars_1971-2000_met.tar.gz >> Projections per realisations for period 1971-2000</li> <li>bioVars_2021-2040_met.tar.gz >> Projections per realisations for period 2021-2040</li> <li>bioVars_2041-2060_met.tar.gz >> Projections per realisations for period 2041-2060</li> <li>bioVars_2061-2080_met.tar.gz >> Projections per realisations for period 2061-2080</li> <li>bioVars_2079-2098_met.tar.gz >> Projections per realisations for period 2079-2098</li> <li>bioVars_2021-2040_rcp.tar.gz >> Projections per RCP for period 2021-2040</li> <li>bioVars_2041-2060_rcp.tar.gz >> Projections per RCP for period 2041-2060</li> <li>bioVars_2061-2080_rcp.tar.gz >> Projections per RCP for period 2061-2080</li> <li>bioVars_2079-2098_rcp.tar.gz >> Projections per RCP for period 2079-2098</li> <li>validation.tar.gz >> Validation using E-OBS (v20.0) and Worldclim (version 2.1)</li> </ul> <p> </p> <p><strong>References</strong> <br>Reichmuth, A., Rakovec, O., Boeing, F. <em>et al.</em> BioVars - A bioclimatic dataset for Europe based on a large regional climate ensemble for periods in 1971–2098. <em>Sci Data</em> <strong>12</strong>, 217 (2025). https://doi.org/10.1038/s41597-025-04507-w</p>
Regularized quantum periods for four-dimensional Fano manifolds
<p><strong>The database smooth_fano_4</strong></p> <p>This is a database of regularized quantum periods for four-dimensional Fano manifolds. The database will be updated as new four-dimensional Fano manifolds are discovered and new regularized quantum periods computed.</p> <p>Each entry in the database is a key-value record with keys and values as described in the paper [CK2021]. If you make use of this data, please cite that paper and the DOI for this data:</p> <p>doi:10.5281/zenodo.5708307</p> <p><strong>Names</strong></p> <p>The database describes Fano varieties via names, as follows:</p> <table> <caption>Names of Fano manifolds</caption> <thead> <tr> <th scope="col">Name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>P1</td> <td>one-dimensional projective space</td> </tr> <tr> <td>P2</td> <td>two-dimensional projective space</td> </tr> <tr> <td>dP(k)</td> <td>the del Pezzo surface of degree k given by the blow-up of P2 in 9-k points</td> </tr> <tr> <td>P3</td> <td>three-dimensional projective space</td> </tr> <tr> <td>Q3</td> <td>a quadric hypersurface in four-dimensional projective space</td> </tr> <tr> <td>B(3,k)</td> <td>the three-dimensional Fano manifold of Picard rank 1, Fano index 2, and degree 8k</td> </tr> <tr> <td>V(3,k)</td> <td>the three-dimensional Fano manifold of Picard rank 1, Fano index 1, and degree k</td> </tr> <tr> <td>MM(r,k)</td> <td>the k-th entry in the Mori-Mukai list of three-dimensional Fano manifolds of Picard rank r, ordered as in [CCGK2016]<br> </td> </tr> <tr> <td>P4</td> <td>four-dimensional projective space</td> </tr> <tr> <td>Q4</td> <td>a quadric hypersurface in five-dimensional projective space</td> </tr> <tr> <td>FI(4,k)</td> <td>the four-dimensional Fano manifold of Fano index 3 and degree 81k</td> </tr> <tr> <td>V(4,k)</td> <td>the four-dimensional Fano manifold of Picard rank 1, Fano index 2, and degree 16k</td> </tr> <tr> <td>MW(4,k)</td> <td>the k-th entry in Table 12.7 of [IP1999] of four-dimensional Fano manifolds of Fano index 2 and Picard rank greater than 1</td> </tr> <tr> <td>Obro(4,k)</td> <td>the k-th four-dimensional Fano toric manifold in Obro's classification [O2007]</td> </tr> <tr> <td>Str(k)</td> <td>the k-th Strangeway manifold in [CGKS2020]</td> </tr> <tr> <td>CKP(k)</td> <td>the k-th four-dimensional Fano toric complete intersection in [CKP2015]</td> </tr> <tr> <td>CKK(k)</td> <td>the k-th four-dimensional Fano quiver flag zero locus in Appendix B of [K2019]</td> </tr> </tbody> </table> <p>A name of the form "S1 x S2", where S1 and S2 are names of Fano manifolds X1 and X2, refers to the product manifold X1 x X2.</p> <p><strong>References</strong></p> <p>[CCGK2016] <em>Quantum periods for 3-dimensional Fano manifolds</em>; Tom Coates, Alessio Corti, Sergey Galkin, Alexander M. Kasprzyk; Geometry and Topology 20 (2016), no. 1, 103-256.</p> <p>[CGKS2020] <em>Quantum periods for certain four-dimensional Fano manifolds</em>; Tom Coates, Sergey Galkin, Alexander M. Kasprzyk, Andrew Strangeway; Experimental Math. 29 (2020), no. 2, 183-221.</p> <p>[CK2021] <em>Databases of quantum periods for Fano manifolds</em>; Tom Coates, Alexander M. Kasprzyk; 2021.</p> <p>[CKP2015] <em>Four-dimensional Fano toric complete intersections</em>; Tom Coates, Alexander M. Kasprzyk, Thomas Prince; Proc. Royal Society A 471 (2015), no. 2175, 20140704, 14.</p> <p>[IP1999] <em>Fano varieties</em>; V.A. Iskovskikh, Yu. G. Prokhorov; Encyclopaedia Math. Sci. vol. 47, Springer, Berlin, 1999, 1-247.</p> <p>[K2019] <em>Four-dimensional Fano quiver flag zero loci</em>; Elana Kalashnikov; Proc. Royal Society A 275 (2019), no. 2225, 20180791, 23. </p> <p>[O2007] <em>An algorithm for the classification of smooth Fano polytopes</em>; Mikkel Obro; arXiv:0704.0049 [math.CO]; 2007.</p>
Regularized quantum periods for one-dimensional Fano manifolds
<p><strong>The database smooth_fano_1</strong></p> <p>This is a database of regularized quantum periods for one-dimensional Fano manifolds. There is one entry in the database.</p> <p>Each entry in the database is a key-value record with keys and values as described in the paper:</p> <p><em>Databases of Quantum Periods for Fano Manifolds</em>, Tom Coates and Alexander M. Kasprzyk, 2021.</p> <p>If you make use of this data, please cite the above paper and the DOI for this data:</p> <p>doi:10.5281/zenodo.5708188</p>
Soil bulk density [10x kg/m3] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: db_od = bulk density over dry [kg/m3 ⨉ 10];</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p> <p>The bulk density maps are also provided in 10 kg / m-cubic to reduce total data size; to convert values to kg / m-cubic multiply by 10 e.g. 120 = 1200 kg / m-cubic = 1.2 t / m-cubic.</p>
Soil pH in H2O [-] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: soil pH in H2O;</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p>
Soil organic carbon content [g/kg] for continental Europe at 30 m spatial resolution for period 2000-2020: Open Soil Data Cube for Europe
<p>Predictions are based on the 3D Ensemble Machine Learning framework, as implemented in the R environment for statistical computing (Hengl & MacMillan, 2019; Hengl, et al., 2021). For each pixel we provide prediction errors as 1 standard deviation in either log or the original variable scale.</p> <p>The short description of currently available soil properties: log organic carbon [g/kg] to back-transform use exp(x/10)-1;</p> <p>Soil properties were predicted at fixed depths:</p> <p> Surface soil = s0..0cm,<br> Subsoil 1 = s30..30cm,<br> Subsoil 2 = s60..60cm,<br> Subsoil 3 = s100..100cm.</p> <p>To produce estimates for depth intervals e.g. 0–30 cm, 0–100 cm best use the trapezoidal rule formula.</p> <p>Periods: 2000 (2000–2003), 2004 (2004–2007), 2008 (2008–2011), 2012 (2012–2015), 2016 (2016–2019), 2020;</p> <p>To back-transform the log.oc maps use formula: exp(x/10)-1. These are examples of back-transformed values:</p> <p> log.oc = 15 → 0.3% SOC;<br> log.oc = 20 → 0.6% SOC;<br> log.oc = 25 → 1.1% SOC;<br> log.oc = 30 → 1.9% SOC;<br> log.oc = 35 → 3.2% SOC;<br> log.oc = 40 → 5.3% SOC;<br> log.oc = 50 → 14.8% SOC;</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.