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275 results for “Spatial resolution”
Microbial Observatory at North Temperate Lakes LTER High-resolution temporal and spatial dynamics of microbial community structure in freshwater bog lakes 2005 - 2009 original format (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/349/4. The abstract below was extracted from the Level 0 data package and is included for context: The North Temperate Lakes - Microbial Observatory seeks to study freshwater microbes over long time scales (10+ years). Observing microbial communities over multiple years using DNA sequencing allows in-depth assessment of diversity, variability, gene content, and seasonal/annual drivers of community composition. Combining information obtained from DNA sequencing with additional experiments, such as investigating the biochemical properties of specific compounds, gene expression, or nutrient concentrations, provides insight into the functions of microbial taxa. Our 16S rRNA gene amplicon datasets were collected from bog lakes in Vilas County, WI, and from Lake Mendota in Madison, WI. Ribosomal RNA gene amplicon sequencing of freshwater environmental DNA was performed on samples from Crystal Bog, North Sparkling Bog, West Sparkling Bog, Trout Bog, South Sparkling Bog, Hell’s Kitchen, and Mary Lake. These microbial time series are valuable both for microbial ecologists seeking to understand the properties of microbial communities and for ecologists seeking to better understand how microbes contribute to ecosystem functioning in freshwater.
Cascade project at North Temperate Lakes LTER - High-resolution spatial analysis of CASCADE lakes during experimental nutrient enrichment 2015 - 2016
This dataset contains high-resolution spatio-temporal water quality data from two experimental lakes during a whole-ecosystem experiment. Through gradual nutrient addition, we induced a cyanobacteria bloom in an experimental lake (Peter Lake) while leaving a nearby reference lake (Paul Lake) as a control. Peter and Paul Lakes (Gogebic county, MI USA), were sampled using the FLAMe platform (Crawford et al. 2015) multiple times during the summers of 2015 and 2016. In 2015 nutrient additions to Peter Lake began on 1 June, and ceased on 29 June, Paul Lake was left unmanipulated. In 2016 no nutrients were added to either lake. Measurements were taken using a YSI EXO2 probe and a Garmin echoMap 50s. Sensor- data were collected continuously at 1 Hz and linked via timestamp to create spatially explicit data for each lake. Crawford, J. T., L. C. Loken, N. J. Casson, C. Smith, A. G. Stone, and L. A. Winslow. 2015. High-speed limnology: Using advanced sensors to investigate spatial variability in biogeochemistry and hydrology. Environmental Science & Technology 49:442–450.
Microbial Observatory at North Temperate Lakes LTER High-resolution temporal and spatial dynamics of microbial community structure in freshwater bog lakes 2005 - 2009 original format
The North Temperate Lakes - Microbial Observatory seeks to study freshwater microbes over long time scales (10+ years). Observing microbial communities over multiple years using DNA sequencing allows in-depth assessment of diversity, variability, gene content, and seasonal/annual drivers of community composition. Combining information obtained from DNA sequencing with additional experiments, such as investigating the biochemical properties of specific compounds, gene expression, or nutrient concentrations, provides insight into the functions of microbial taxa. Our 16S rRNA gene amplicon datasets were collected from bog lakes in Vilas County, WI, and from Lake Mendota in Madison, WI. Ribosomal RNA gene amplicon sequencing of freshwater environmental DNA was performed on samples from Crystal Bog, North Sparkling Bog, West Sparkling Bog, Trout Bog, South Sparkling Bog, Hell’s Kitchen, and Mary Lake. These microbial time series are valuable both for microbial ecologists seeking to understand the properties of microbial communities and for ecologists seeking to better understand how microbes contribute to ecosystem functioning in freshwater.
Cascade Project at North Temperate Lakes LTER – High-resolution Spatial Data for Whole Lake Experiments 2018 - 2019
Spatial measurements of water quality from Peter and Paul lakes in 2018 and 2019. In 2019, inorganic nitrogen and phosphorus were added to Peter Lake daily to cause an algal bloom while Paul Lake was an unmanipulated reference lake. In 2018, both lakes were sampled 1 time per week, while in 2019 lakes were sampled three times per week. Measurements were taken using the FLAMe sampling platform (Crawford et al. 2015, Environmental Science and Technology 49:442-450), which was driven in a grid pattern and recorded GPS coordinates and water measurements at 1Hz to create high resolution spatial maps.
Annual time series of global VIIRS nighttime lights for 2000-2024 at 500-m spatial resolution extrapolated using logistic regression
<p>The <a href="https://eogdata.mines.edu/products/vnl/"><strong>Annual Visible Night Light (VNL) V2</strong></a> (VIIRS) images at 500-m spatial resolution for the period 2012 to 2024 (Elvidge et al., 2021) have been used to extrapolate the values backwards for years 2000–2011. This was done by fitting a logistic regression (per pixel) and then predicting the values for the previous years (see nightlights_stack_500m.R). After consistent time-series have been produced, I also derived the difference between year 2024 and year 2000 (nightlights.difference_viirs.v21_m_500m_s_2000_2024_go_epsg4326_v20230318.tif): this shows average rate of change for the 25 years period. Use with caution: extrapolation of values can lead to artifacts. For most of the land surface, however, it appears that the growth of night lights follows exponential growth function and hence nights in the past can be represented accurately by fitting decay / logistic regression function.</p> <p>Original values from the Annual VNL V2 product have been converted from 0–200 to 0–2000 scale and are available as Cloud-Optimized GeoTIFFs.</p> <p>Principal components (PC1, PC2, PC3, PC4) were derived using SAGA GIS (sums-of-squares-and-cross-products matrix) method. The first PC1 usually matches the long-term mean value, PC2 matches the 1st derivation in values. File "nightlights_dmsp.v10_m_1km_s_19920101_20241231_go_epsg4326_v20251006.tif" contains 33 years 1992 to 2024, but at 1 km resolution.</p> <p>To cite the Annual VNL V2, please use:</p> <ul> <li>Elvidge, C. D., Zhizhin, M., Ghosh, T., Hsu, F. C., & Taneja, J. (2021). <a href="https://doi.org/10.3390/rs13050922">Annual time series of global VIIRS nighttime lights derived from monthly averages: 2012 to 2019</a>. Remote Sensing, 13(5), 922. https://doi.org/10.3390/rs13050922</li> </ul> <p>Historic night light images (1 km resolution) are also available from <a href="https://doi.org/10.6084/m9.figshare.9828827.v10">Figshare</a>:</p> <ul> <li>Li, X., Zhou, Y., Zhao, M., & Zhao, X. (2020). <a href="https://doi.org/10.1038/s41597-020-0510-y">A harmonized global nighttime light dataset 1992–2018</a>. Scientific data, 7(1), 168. https://doi.org/10.1038/s41597-020-0510-y</li> </ul>
Predicted occurrence probability for ticks in Great Britain (2014 to 2021) at 1 km spatial resolution
<p>The dataset contains predictions of occurrence probability for ticks in Great Britain (2014 to 2021) at 1 km spatial resolution + all covariate layers used for modeling. Over seven million electronic health records (EHRs), among which 11,741 EHRs reported tick attachment, were used to evaluate climate, environmental and animal host factors affecting the risk of tick attachment in cats and dogs in Great Britain (GB). The tick presence/absence EHRs for dogs and cats were further overlaid with spatiotemporal time-series of climatic, vegetation, human influence, hydrological and terrain variables (slope, wetness index) to produce a spatiotemporal regression matrix; an Ensemble Machine Learning framework was used to fine-tune hyperparameters for Random Forest (classif.ranger), Gradient boosting (classif.xgboost) and GLM-net (classif.glmnet) algorithms, which were then used to produce a final ensemble meta-learner that predicts the probability of occurrence of ticks across GB with monthly intervals.</p> <ul> <li>gb1km_covariates.zip contains ALL covariate layers as GeoTIFFs (time-series) used for modeling ticks dynamics;</li> <li>data_1km_2014_M01.rds = contains all covariates for January 2014 prepared as SpatialGridDataFrame (R data object);</li> </ul> <p>Codes of files indicate e.g.:</p> <ul> <li>"monthly.tick.prob_savsnet.mar_p_1km_s_2014_2021" = monthly occurrence probability for January based on the training data from 2014 to 2021;</li> <li>"monthly.tick.prob_savsnet.oct_md_1km_s_20211001_20211031" = monthly prediction (model) error derived as the standard deviation from multiple base learners;</li> </ul> <p>The dataset is described in detail in the following publication:</p> <ul> <li>Arsevska, E., Hengl, T., Singelton, D. et al. (2023?) <strong>Risk factors for tick attachment in companion animals in Great Britain: a spatiotemporal analysis covering 2014–2021</strong>. Submitted to Parasites & Vectors (in review).</li> </ul> <p>The model summary shows:</p> <pre><code>Call: stats::glm(formula = f, family = "binomial", data = getTaskData(.task, .subset), weights = .weights, model = FALSE) Deviance Residuals: Min 1Q Median 3Q Max -1.4749 -0.0557 -0.0471 -0.0430 3.7611 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -7.64495 0.02095 -364.957 < 2e-16 *** classif.ranger 4.95061 0.63615 7.782 7.13e-15 *** classif.xgboost 189.75543 5.53109 34.307 < 2e-16 *** classif.glmnet 140.24208 5.05375 27.750 < 2e-16 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 170604 on 7303013 degrees of freedom Residual deviance: 162571 on 7303010 degrees of freedom AIC: 162579 Number of Fisher Scoring iterations: 9</code></pre> <p><em>Acknowledgements</em>: We are grateful to data providers in veterinary practice (VetSolutions, Teleos, CVS, and other practitioners). We are grateful to the INRAE MIGALE bioinformatics facility (MIGALE, INRAE, 2020. Migale Bioinformatics Facility, doi: <a href="https://entrepot.recherche.data.gouv.fr/dataverse/migale">10.15454/1.5572390655343293E12</a>) for providing computing resources. We are also grateful for<br> the help and support provided by <a href="https://www.liverpool.ac.uk/savsnet/">SAVSNET team members</a> Bethaney Brant, Susan Bolan and Steven Smyth.<br> This study was funded mainly by a grant from the <strong>Biotechnology and Biological Sciences Research Council</strong>,<br> BB/NO19547/1 and <strong>British Small Animal Veterinary Association</strong> (BSAVA). The research was partly funded by the National Institute for <strong>Health Research Health Protection Research Unit</strong> (NIHR HPRU) in Emerging and Zoonotic Infections at the <strong>University of Liverpool</strong> in partnership with <strong>Public Health England</strong> (PHE) and <strong>Liverpool School of Tropical Medicine</strong> (LSTM). This work has been partially funded by the <em>“Monitoring outbreak events for disease surveillance in a data science context"</em> (MOOD) project from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 874850 (<a href="https://mood-h2020.eu/">https://mood-h2020.eu/</a>). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, the Department of Health or Public Health England.</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>
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>
Soil clay content [%] 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: clay.tot = clay content [percent];</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 sand content [%] 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: sand.tot = sand content [percent];</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>
Global peatland, bare rock and bare sand extent at 100 m to 1 km spatial resolution based on multisource data
<p>Ensemble estimate of the global distribution of <a href="https://en.wikipedia.org/wiki/Peatland">peatlands</a> / extent (<strong>peatland.extent_wri.gfw.peatgrids_p</strong>). This is a simple average from three (3) sources of data:</p> <ol> <li><a href="https://data.globalforestwatch.org/datasets/gfw::global-peatlands/about">WRI Global Peatlands extent map</a> at 30-m (250-m effective);</li> <li><a href="https://doi.org/10.5281/zenodo.12559238">PEATGRIDS</a> at 1-km;</li> <li><a href="https://globalpeatlands.org/new-online-global-peatland-map-asian-peatlands-story-map-presenting-best-peatlands-mapping">Global Peatlands Map 2.0</a> produced by the Global Peatlands Initiative;</li> </ol> <p>The average between the three sources is an extent map with value 0–100%. The refence period is 2000–2020, although probably most of data is based on pre 2010. For more details about the source data please refer to the cited references below.</p> <p>Bare rock and bare sand estimates are based on the following two sources of data:</p> <ol> <li><a href="https://land.copernicus.eu/en/products/global-dynamic-land-cover">Copernicus GLC land cover</a> at 100-m for 2015 and 2019;</li> <li><a href="https://lcz-generator.rub.de/global-lcz-map">Local Climate zones</a> map at 100-m for 2018;</li> </ol> <p>Two classes are considered: (1) probability of occurrence of bare rock (<strong>bare.rock_glc.gfz_p</strong>), (2) probability of occurrence of bare sand i.e. shifting sand (<strong>bare.soil.sand_glc.gfz_p</strong>). We recommend using only the 1-km data for spatial modeling.</p> <p>The time-series of bare areas (<strong>bare.areas_esa.cci_p</strong>) are based on the <a href="https://climate.esa.int/en/odp/#/project/land-cover">ESA CCI Land Cover time-series</a> (2000–2022) 300-m resolution data; also available at 1-km resolution based on "average" resampling. </p>
MeteoEurope1km - TMAX (1991–2000): daily gridded meteorological dataset for Europe at a 1-km spatial resolution for the 1991–2020 period
<p>MeteoEurope1km is the daily gridded meteorological dataset for Europe at a 1-km spatial resolution for the 1991–2020 period. The dataset consists of five daily variables:</p> <ul> <li><strong>TMAX - maximum temperature</strong> (<strong>1991–2005 period</strong>, 2006–2020 period)</li> <li>TMIN - minimum temperature (1991–2005 period, 2006–2020 period)</li> <li>TMEAN - mean temperature (1991–2005 period, 2006–2020 period)</li> <li>SLP - mean sea level pressure</li> <li>PRCP - total precipitation</li> </ul> <p>Daily gridded temperature data were interpolated using the Regression Kriging, with digital elevation model (DEM) and topographic wetness index (TWI) as covariates.<br> Daily gridded sea level pressure data were interpolated using Ordinary Kriging.<br> Daily gridded precipitation data were interpolated using Indicator and Ordinary Kriging methodology in two steps:</p> <ol> <li>Indicator Kriging - prediction of precipitation occurence</li> <li>Ordinary Kriging - prediction of total daily precipitation for locations where precipitation occurs (1. step).</li> </ol> <p>File naming convention of the MeteoEurope1km files is <em>var_day_yyyymmdd_proj.tif</em> (e.g. <em>tmax_day_20201231_3035.tif</em>), 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 EPSG code - 3035</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> All dataset files are available as Cloud-Optimized GeoTIFFs (COGs).<br> Use the R <a href="https://github.com/AleksandarSekulic/Rmeteo">meteo</a> package, <em>europe1km</em> function to make a point query and obtain the values for a specific location and a specific period.</p>
Microbial Observatory at North Temperate Lakes LTER High-resolution temporal and spatial dynamics of microbial community structure in freshwater bog lakes 2005 - 2009 original format (Reformatted to a Darwin Core Archive)
This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/344/6, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-ntl/349/4. The abstract below was extracted from the Level 0 data package and is included for context: The North Temperate Lakes - Microbial Observatory seeks to study freshwater microbes over long time scales (10+ years). Observing microbial communities over multiple years using DNA sequencing allows in-depth assessment of diversity, variability, gene content, and seasonal/annual drivers of community composition. Combining information obtained from DNA sequencing with additional experiments, such as investigating the biochemical properties of specific compounds, gene expression, or nutrient concentrations, provides insight into the functions of microbial taxa. Our 16S rRNA gene amplicon datasets were collected from bog lakes in Vilas County, WI, and from Lake Mendota in Madison, WI. Ribosomal RNA gene amplicon sequencing of freshwater environmental DNA was performed on samples from Crystal Bog, North Sparkling Bog, West Sparkling Bog, Trout Bog, South Sparkling Bog, Hell’s Kitchen, and Mary Lake. These microbial time series are valuable both for microbial ecologists seeking to understand the properties of microbial communities and for ecologists seeking to better understand how microbes contribute to ecosystem functioning in freshwater.
The S&M-HSTPM2d5 dataset: High Spatial-Temporal Resolution PM 2.5 Measures in Multiple Cities Sensed by Static & Mobile Devices
<p>This S&M-HSTPM2d5 dataset contains the high spatial and temporal resolution of the particulates (PM2.5) measures with the corresponding timestamp and GPS location of mobile and static devices in the three Chinese cities: Foshan, Cangzhou, and Tianjin. Different numbers of static and mobile devices were set up in each city. The sampling rate was set up as one minute in Cangzhou, and three seconds in Foshan and Tianjin. For the specific detail of the setup, please refer to the Device_Setup_Description.txt file in this repository and the data descriptor paper.</p> <p>After the data collection process, the data cleaning process was performed to remove and adjust the abnormal and drifting data. The script of the data cleaning algorithm is provided in this repository. The data cleaning algorithm only adjusts or removes individual data points. The removal of the entire device's data was done after the data cleaning algorithm with empirical judgment and graphic visualization. For specific detail of the data cleaning process, please refer to the script (Data_cleaning_algorithm.ipynb) in this repository and the data descriptor paper.</p> <p>The dataset in this repository is the processed version. The raw dataset and removed devices are not included in this repository.</p> <p>The data is stored as a CSV file. Each CSV file which is named by the device ID represents the data that was collected by the corresponding device. Each CSV file has three types of data: timestamp as the China Standard Time (GMT+8), geographic location as latitude and longitude, and PM2.5 concentration with the unit of microgram per cubic meter. The CSV files are stored in either Static or Mobile folder which represents the devices' type. The Static and Mobile folder are stored in the corresponding city's folder.</p> <p>To access the dataset, any programming language that can access CSV files is appropriate. Users can also open the CSV file directly. The get_dataset.ipynb file in this repository also provides an option of accessing the dataset. To successfully execute ipynb file, Jupyter Notebook with Python 3.0 is required. The following python library is also required:</p> <p>get_dataset.ipynb:<br> 1. os library<br> 2. pandas library</p> <p>Data_cleaning_algorithm.ipynb:<br> 1. os library<br> 2. pandas library<br> 3. datetime library<br> 4. math library</p> <p>The instruction of installing the libraries above can be found online. After installing the Jupyter Notebook with Python 3.0 and the required libraries, users can try to open the ipynb file with Jupyter Notebook and follow the instruction inside the file. </p> <p>For questions or suggestions please e-mail Xinlei Chen <xinlei.chen@sv.cmu.edu></p>
Soil organic carbon stocks and trends (1984-2019) predicted at 30m spatial resolution for topsoil in natural areas of South Africa
<p>Link to scientific publication: <a href="https://doi.org/10.1016/j.scitotenv.2021.145384">https://doi.org/10.1016/j.scitotenv.2021.145384</a></p> <p>Soil organic carbon (SOC) stocks (kg C m-2) are predicted over natural areas (excluding water, urban, and cultivated) of South Africa using a machine learning workflow driven by optical satellite data and other ancillary climatic, morphometric and biological covariates. The temporal scope covers 1984-2019. The spatial scope covers 0-30cm topsoil in South Africa natural land area (84% of the country). See methodology in linked publication for details. Data are provided here at 30m spatial resolution in GeoTIFF files. There is a dataset for the long-term average SOC and trend in SOC. Each dataset is split into four files (suffix *_1, *_2 etc.) covering separate regions of South Africa for ease of download. The raster files are:</p> <ul> <li>"SOC_mean_30m..." - average of annual SOC predictions between 1984 and 2019. Values are expressed in kg C m-2</li> <li>"SOC_trend_30m..." - long-term trend in SOC derived from the Sens slope (M) across annual SOC values between 1984 and 2019. Pixel values (Y) are expressed as a percentage change over the 35 years relative to the long-term mean (X). Y = M / X * 100 * 35 years</li> </ul> <p>NB: All files are scaled by *100 and converted to floating data point to save space. To back-convert to original values, simply divide the raster values by 100.</p>
Global Pasture Watch - Annual grassland class and extent maps at 30-m spatial resolution (2000—2024)
<h2><strong>Sub-dataset: Dominant grassland class, 2018-2020</strong></h2> <h2>Description</h2> <p>Global annual grassland class and extent for 2000—2024 produced by <a href="https://doi.org/10.1038/s41597-024-04139-6">Parente et al. (2024)</a> within the scope of the <a href="https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring/">Global Pasture Watch initiative</a>. The mapped grassland extent includes any land cover type, which contains at least <strong>30% of dry or wet low vegetation</strong>, dominated by grasses and forbs (less than 3 meters) and a:</p> <ul> <li>maximum of 50% tree canopy cover (greater than 5 meters),</li> <li>maximum of 70% of other woody vegetation (scrubs and open shrubland), and</li> <li>maximum of 50% active cropland cover in mosaic landscapes of cropland & other vegetation.</li> </ul> <p>The grassland extent is classified into two classes:</p> <ul> <li><strong>Cultivated grassland</strong>: Areas where grasses and other forage plants have been intentionally planted and managed, as well as areas of native grassland-type vegetation where they clearly exhibit active and 'heavy' management for specific human-directed uses, such as directed grazing of livestock.</li> <li><strong>Natural/semi-natural grassland</strong>: Relatively undisturbed native grasslands/short-height vegetation, such as steppes and tundra, as well as areas that have experienced varying degrees of human activity in the past, which may contain a mix of native and introduced species due to historical land use and natural processes. In general, they exhibit natural-looking patterns of varied vegetation and clearly ordered hydrological relationships throughout the landscape.</li> <li><strong>Open shrubland (v2-beta): </strong>Land on which the vegetation is dominated by low-growing woody plants, characterized by a sparse distribution of shrubs and dominated by woody perennials. Typically covers 50—75% of the area, with significant open ground (with or without herbaceous understory) between them, where shrub canopies are less than 10 meters in diameter, and tree cover is below 10%, meaning they do not form a continuous or semi-continuous canopy.</li> </ul> <p>The dataset is organized in 69 global mosaics (25 years for each time series) in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 degrees, including:</p> <ul> <li><strong>Probabilities</strong> of cultivated grassland (values range from 0–100),</li> <li><strong>Probabilities</strong> of natural/semi-natural grassland (values range from 0–100), and</li> <li><strong>Probabilities</strong> of open shrubland (values range from 0–100), and</li> <li><strong>Dominant</strong> class (0-other land cover, 1-cultivated grassland and 2-natural/semi-natural grassland, 3-open shrubland).</li> </ul> <p>All raster files are in unsigned <code>8-bit integer format</code> and use <code>255</code> as no-data value (pixels ignored by prediction), following an specific naming convention:</p> <ol> <li>Project name: Global Pasture Watch (<code>gpw</code>)</li> <li>Class name: cultivated grassland (<code>cultiv.grassland</code>), natural/semi-natural grassland (<code>nat.semi.grassland</code>), open shrubland (<code>open.shrubland</code>) and dominant grassland (<code>grassland</code>)</li> <li>Procedure combination: Random Forest (<code>rf</code>), median filter (med.filt) and balanced threshold (<code>bthr</code>).</li> <li>Variable type: probability (<code>p</code>) and factor class (<code>c</code>)</li> <li>Spatial resolution: 30m</li> <li>Begin of time reference: date of first Landsat composite used by the modeling (<code>20240101</code>)</li> <li>End of time reference: date of last Landsat composite used by the modeling (<code>20241231</code>)</li> <li>Spatial extent: global (<code>go</code>)</li> <li>Coordinate system: World Geodetic System 1984, used in GPS (<code>epsg.4326</code>)</li> <li>Version: v2</li> </ol> <h3>Related resources</h3> <ul> <li><strong>Maps of dominant grassland:</strong><br><a href="http://doi.org/10.5281/zenodo.15646181">2000-2002</a><a href="http://doi.org/10.5281/zenodo.15644486"> 2003-2005</a><a href="http://doi.org/10.5281/zenodo.15644623"> 2006-2008</a><a href="http://doi.org/10.5281/zenodo.15647042"> 2009-2011</a><a href="http://doi.org/10.5281/zenodo.15647681"> 2012-2014</a><a href="http://doi.org/10.5281/zenodo.15648306"> 2015-2017</a><a href="http://doi.org/10.5281/zenodo.15648551"> 2018-2020</a><a href="http://doi.org/10.5281/zenodo.15648751"> 2021-2023</a> <a href="http://doi.org/10.5281/zenodo.15649332">2024</a></li> <li><strong>Probability maps of cultivated grassland:</strong><br><a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li><strong>Probability maps of natural/semi-natural grassland:</strong><br><a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li> <div><strong>Grassland reference samples based on VHR imagery (2000–2024):</strong><br><a href="https://doi.org/10.5281/zenodo.15631655">GeoPackage files</a></div> </li> <li><strong>Global machine learning models (Random Forest):</strong><br><a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li><strong>Reference sampling design derived by FSCV:</strong><br><a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li><strong>Harmonized reference samples based on existing LULC dataset:</strong><br><a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li><strong>Source code for reproducibility:<br></strong><a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a><strong><br></strong></li> <li><strong>Mapping feedback tool:</strong><br><a href="https://geo-wiki.org">GeoWiki</a></li> <li><strong>Data catalogues:</strong><br><a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> <p><strong>Support</strong></p> <p>For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a></p>
Global Pasture Watch - Annual grassland class and extent maps at 30-m spatial resolution (2000—2024)
<h2><strong>Sub-dataset: Dominant grassland class, 2021-2023</strong></h2> <h2>Description</h2> <p>Global annual grassland class and extent for 2000—2024 produced by <a href="https://doi.org/10.1038/s41597-024-04139-6">Parente et al. (2024)</a> within the scope of the <a href="https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring/">Global Pasture Watch initiative</a>. The mapped grassland extent includes any land cover type, which contains at least <strong>30% of dry or wet low vegetation</strong>, dominated by grasses and forbs (less than 3 meters) and a:</p> <ul> <li>maximum of 50% tree canopy cover (greater than 5 meters),</li> <li>maximum of 70% of other woody vegetation (scrubs and open shrubland), and</li> <li>maximum of 50% active cropland cover in mosaic landscapes of cropland & other vegetation.</li> </ul> <p>The grassland extent is classified into two classes:</p> <ul> <li><strong>Cultivated grassland</strong>: Areas where grasses and other forage plants have been intentionally planted and managed, as well as areas of native grassland-type vegetation where they clearly exhibit active and 'heavy' management for specific human-directed uses, such as directed grazing of livestock.</li> <li><strong>Natural/semi-natural grassland</strong>: Relatively undisturbed native grasslands/short-height vegetation, such as steppes and tundra, as well as areas that have experienced varying degrees of human activity in the past, which may contain a mix of native and introduced species due to historical land use and natural processes. In general, they exhibit natural-looking patterns of varied vegetation and clearly ordered hydrological relationships throughout the landscape.</li> <li><strong>Open shrubland (v2-beta): </strong>Land on which the vegetation is dominated by low-growing woody plants, characterized by a sparse distribution of shrubs and dominated by woody perennials. Typically covers 50—75% of the area, with significant open ground (with or without herbaceous understory) between them, where shrub canopies are less than 10 meters in diameter, and tree cover is below 10%, meaning they do not form a continuous or semi-continuous canopy.</li> </ul> <p>The dataset is organized in 69 global mosaics (25 years for each time series) in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 degrees, including:</p> <ul> <li><strong>Probabilities</strong> of cultivated grassland (values range from 0–100),</li> <li><strong>Probabilities</strong> of natural/semi-natural grassland (values range from 0–100), and</li> <li><strong>Probabilities</strong> of open shrubland (values range from 0–100), and</li> <li><strong>Dominant</strong> class (0-other land cover, 1-cultivated grassland and 2-natural/semi-natural grassland, 3-open shrubland).</li> </ul> <p>All raster files are in unsigned <code>8-bit integer format</code> and use <code>255</code> as no-data value (pixels ignored by prediction), following an specific naming convention:</p> <ol> <li>Project name: Global Pasture Watch (<code>gpw</code>)</li> <li>Class name: cultivated grassland (<code>cultiv.grassland</code>), natural/semi-natural grassland (<code>nat.semi.grassland</code>), open shrubland (<code>open.shrubland</code>) and dominant grassland (<code>grassland</code>)</li> <li>Procedure combination: Random Forest (<code>rf</code>), median filter (med.filt) and balanced threshold (<code>bthr</code>).</li> <li>Variable type: probability (<code>p</code>) and factor class (<code>c</code>)</li> <li>Spatial resolution: 30m</li> <li>Begin of time reference: date of first Landsat composite used by the modeling (<code>20240101</code>)</li> <li>End of time reference: date of last Landsat composite used by the modeling (<code>20241231</code>)</li> <li>Spatial extent: global (<code>go</code>)</li> <li>Coordinate system: World Geodetic System 1984, used in GPS (<code>epsg.4326</code>)</li> <li>Version: v2</li> </ol> <h3>Related resources</h3> <ul> <li><strong>Maps of dominant grassland:</strong><br><a href="http://doi.org/10.5281/zenodo.15646181">2000-2002</a><a href="http://doi.org/10.5281/zenodo.15644486"> 2003-2005</a><a href="http://doi.org/10.5281/zenodo.15644623"> 2006-2008</a><a href="http://doi.org/10.5281/zenodo.15647042"> 2009-2011</a><a href="http://doi.org/10.5281/zenodo.15647681"> 2012-2014</a><a href="http://doi.org/10.5281/zenodo.15648306"> 2015-2017</a><a href="http://doi.org/10.5281/zenodo.15648551"> 2018-2020</a><a href="http://doi.org/10.5281/zenodo.15648751"> 2021-2023</a> <a href="http://doi.org/10.5281/zenodo.15649332">2024</a></li> <li><strong>Probability maps of cultivated grassland:</strong><br><a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li><strong>Probability maps of natural/semi-natural grassland:</strong><br><a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li> <div><strong>Grassland reference samples based on VHR imagery (2000–2024):</strong><br><a href="https://doi.org/10.5281/zenodo.15631655">GeoPackage files</a></div> </li> <li><strong>Global machine learning models (Random Forest):</strong><br><a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li><strong>Reference sampling design derived by FSCV:</strong><br><a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li><strong>Harmonized reference samples based on existing LULC dataset:</strong><br><a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li><strong>Source code for reproducibility:<br></strong><a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a><strong><br></strong></li> <li><strong>Mapping feedback tool:</strong><br><a href="https://geo-wiki.org">GeoWiki</a></li> <li><strong>Data catalogues:</strong><br><a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> <p><strong>Support</strong></p> <p>For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a></p>
Global Pasture Watch - Annual grassland class and extent maps at 30-m spatial resolution (2000—2024)
<h2><strong>Sub-dataset: Dominant grassland class, 2015-2017</strong></h2> <h2>Description</h2> <p>Global annual grassland class and extent for 2000—2024 produced by <a href="https://doi.org/10.1038/s41597-024-04139-6">Parente et al. (2024)</a> within the scope of the <a href="https://landcarbonlab.org/data/global-grassland-and-livestock-monitoring/">Global Pasture Watch initiative</a>. The mapped grassland extent includes any land cover type, which contains at least <strong>30% of dry or wet low vegetation</strong>, dominated by grasses and forbs (less than 3 meters) and a:</p> <ul> <li>maximum of 50% tree canopy cover (greater than 5 meters),</li> <li>maximum of 70% of other woody vegetation (scrubs and open shrubland), and</li> <li>maximum of 50% active cropland cover in mosaic landscapes of cropland & other vegetation.</li> </ul> <p>The grassland extent is classified into two classes:</p> <ul> <li><strong>Cultivated grassland</strong>: Areas where grasses and other forage plants have been intentionally planted and managed, as well as areas of native grassland-type vegetation where they clearly exhibit active and 'heavy' management for specific human-directed uses, such as directed grazing of livestock.</li> <li><strong>Natural/semi-natural grassland</strong>: Relatively undisturbed native grasslands/short-height vegetation, such as steppes and tundra, as well as areas that have experienced varying degrees of human activity in the past, which may contain a mix of native and introduced species due to historical land use and natural processes. In general, they exhibit natural-looking patterns of varied vegetation and clearly ordered hydrological relationships throughout the landscape.</li> <li><strong>Open shrubland (v2-beta): </strong>Land on which the vegetation is dominated by low-growing woody plants, characterized by a sparse distribution of shrubs and dominated by woody perennials. Typically covers 50—75% of the area, with significant open ground (with or without herbaceous understory) between them, where shrub canopies are less than 10 meters in diameter, and tree cover is below 10%, meaning they do not form a continuous or semi-continuous canopy.</li> </ul> <p>The dataset is organized in 69 global mosaics (25 years for each time series) in COG (Cloud Optimized GeoTIFF) format, WGS84 Coordinate Systems (EPSG:4326) and pixel size equal to 0.00025 degrees, including:</p> <ul> <li><strong>Probabilities</strong> of cultivated grassland (values range from 0–100),</li> <li><strong>Probabilities</strong> of natural/semi-natural grassland (values range from 0–100), and</li> <li><strong>Probabilities</strong> of open shrubland (values range from 0–100), and</li> <li><strong>Dominant</strong> class (0-other land cover, 1-cultivated grassland and 2-natural/semi-natural grassland, 3-open shrubland).</li> </ul> <p>All raster files are in unsigned <code>8-bit integer format</code> and use <code>255</code> as no-data value (pixels ignored by prediction), following an specific naming convention:</p> <ol> <li>Project name: Global Pasture Watch (<code>gpw</code>)</li> <li>Class name: cultivated grassland (<code>cultiv.grassland</code>), natural/semi-natural grassland (<code>nat.semi.grassland</code>), open shrubland (<code>open.shrubland</code>) and dominant grassland (<code>grassland</code>)</li> <li>Procedure combination: Random Forest (<code>rf</code>), median filter (med.filt) and balanced threshold (<code>bthr</code>).</li> <li>Variable type: probability (<code>p</code>) and factor class (<code>c</code>)</li> <li>Spatial resolution: 30m</li> <li>Begin of time reference: date of first Landsat composite used by the modeling (<code>20240101</code>)</li> <li>End of time reference: date of last Landsat composite used by the modeling (<code>20241231</code>)</li> <li>Spatial extent: global (<code>go</code>)</li> <li>Coordinate system: World Geodetic System 1984, used in GPS (<code>epsg.4326</code>)</li> <li>Version: v2</li> </ol> <h3>Related resources</h3> <ul> <li><strong>Maps of dominant grassland:</strong><br><a href="http://doi.org/10.5281/zenodo.15646181">2000-2002</a><a href="http://doi.org/10.5281/zenodo.15644486"> 2003-2005</a><a href="http://doi.org/10.5281/zenodo.15644623"> 2006-2008</a><a href="http://doi.org/10.5281/zenodo.15647042"> 2009-2011</a><a href="http://doi.org/10.5281/zenodo.15647681"> 2012-2014</a><a href="http://doi.org/10.5281/zenodo.15648306"> 2015-2017</a><a href="http://doi.org/10.5281/zenodo.15648551"> 2018-2020</a><a href="http://doi.org/10.5281/zenodo.15648751"> 2021-2023</a> <a href="http://doi.org/10.5281/zenodo.15649332">2024</a></li> <li><strong>Probability maps of cultivated grassland:</strong><br><a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li><strong>Probability maps of natural/semi-natural grassland:</strong><br><a href="https://zenodo.org/records/13890401/files/ggc-30m.csv?download=1">2000-2024 (All URLs)</a></li> <li> <div><strong>Grassland reference samples based on VHR imagery (2000–2024):</strong><br><a href="https://doi.org/10.5281/zenodo.15631655">GeoPackage files</a></div> </li> <li><strong>Global machine learning models (Random Forest):</strong><br><a href="https://doi.org/10.5281/zenodo.13952806">Parquet and joblib python files</a></li> <li><strong>Reference sampling design derived by FSCV:</strong><br><a href="https://doi.org/10.5281/zenodo.11391517">GeoPackage and raster files</a></li> <li><strong>Harmonized reference samples based on existing LULC dataset:</strong><br><a href="https://doi.org/10.5281/zenodo.13951976">GeoPackage and raster files</a></li> <li><strong>Source code for reproducibility:<br></strong><a href="https://doi.org/10.5281/zenodo.13952867">GitHub release</a><strong><br></strong></li> <li><strong>Mapping feedback tool:</strong><br><a href="https://geo-wiki.org">GeoWiki</a></li> <li><strong>Data catalogues:</strong><br><a href="https://stac.openlandmap.org/gpw_ggc-30m/collection.json?.language=en">OpenLandMap STAC</a> <a href="https://global-pasture-watch.projects.earthengine.app/view/ggc-30m">Google Earth Engine</a></li> </ul> <p><strong>Support</strong></p> <p>For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in <a href="https://github.com/wri/global-pasture-watch">https://github.com/wri/global-pasture-watch</a></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.