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FIGURE 13 in Molecular delimitation of the seasonal killifishes of the Hypsolebias antenori species group (Cyprinodontiformes, Rivulidae), with description of two new species from the Caatinga coastal basins, northeastern Brazil
FIGURE 13. Land filling for civil construction projects in the type-locality of Hypsolebias gongobira and H. longignatus.
FIGURE 9 in Molecular delimitation of the seasonal killifishes of the Hypsolebias antenori species group (Cyprinodontiformes, Rivulidae), with description of two new species from the Caatinga coastal basins, northeastern Brazil
FIGURE 9. Bayesian inference based on the mitochondrial gene cox1 used for lineage delimitation of the Hypsolebias antenori species-group. Vertical bars represent the number of lineages delimited by ABGD (7), sGMYC (6), mGMYC (11), and b-PTP (10). Numbers adjacent to nodes represent posterior probabilities; values <0.50% are not shown.
FIGURE 12 in Molecular delimitation of the seasonal killifishes of the Hypsolebias antenori species group (Cyprinodontiformes, Rivulidae), with description of two new species from the Caatinga coastal basins, northeastern Brazil
FIGURE 12. Environmental impact of the duplication of the CE-40 highway in the type-locality of Hypsolebias gongobira and H. longignatus.
FIGURE 11. Hypsolebias longignatus, UFRN 5846, male 35.8 in Molecular delimitation of the seasonal killifishes of the Hypsolebias antenori species group (Cyprinodontiformes, Rivulidae), with description of two new species from the Caatinga coastal basins, northeastern Brazil
FIGURE 11. Hypsolebias longignatus, UFRN 5846, male 35.8 mm SL: Brazil, Ceará, Aquiraz, rio Pacoti basin.
FIGURE 5. Hypsolebias bonita new species, MZUSP 129608 in Molecular delimitation of the seasonal killifishes of the Hypsolebias antenori species group (Cyprinodontiformes, Rivulidae), with description of two new species from the Caatinga coastal basins, northeastern Brazil
FIGURE 5. Hypsolebias bonita new species, MZUSP 129608, male, paratype, 38.6 mm SL: Brazil, Rio Grande do Norte, Baraúna, Furna Feia National Park.
FIGURE 4 in Molecular delimitation of the seasonal killifishes of the Hypsolebias antenori species group (Cyprinodontiformes, Rivulidae), with description of two new species from the Caatinga coastal basins, northeastern Brazil
FIGURE 4. Type-locality of Hypsolebias gongobira new species, Brazil, Ceará, Aquiraz, rio Pacoti basin.
FIGURE 15 in Molecular delimitation of the seasonal killifishes of the Hypsolebias antenori species group (Cyprinodontiformes, Rivulidae), with description of two new species from the Caatinga coastal basins, northeastern Brazil
FIGURE 15. Locality of H. antenori, Brazil, Ceará, Russas, seasonal pool in the floodplain of the rio Jaguaribe basin.
FIGURE 3. Hypsolebias gongobira new species, MZUSP 129607 in Molecular delimitation of the seasonal killifishes of the Hypsolebias antenori species group (Cyprinodontiformes, Rivulidae), with description of two new species from the Caatinga coastal basins, northeastern Brazil
FIGURE 3. Hypsolebias gongobira new species, MZUSP 129607, female, paratype, 35 mm SL: Brazil, Ceará, Aquiraz, rio Pacoti basin.
FIGURE 1 in Molecular delimitation of the seasonal killifishes of the Hypsolebias antenori species group (Cyprinodontiformes, Rivulidae), with description of two new species from the Caatinga coastal basins, northeastern Brazil
FIGURE 1. Map of northeastern Brazil showing the distribution of the Hypsolebias antenori species-group. Stars represent type localities, and circles indicate sampled localities. Hypsolebias bonita new species in pink, H. antenori in red, H. gongobira new species in orange (syntopic with H. longignatus), H. martinsi in yellow, H. coamazonicus in green, H. faouri in light blue, H. igneus in dark blue, and H. nudiorbitatus in purple. Dark blue lines represent hydrographic basins of the Caatinga ecoregions, Maranhão-Piauí (MAPE), Mid-Northeastern Caatinga (MNCE), Northeastern Atlantic Forest (NAFE) São Francisco (SFRE).
FIGURE 8 in Molecular delimitation of the seasonal killifishes of the Hypsolebias antenori species group (Cyprinodontiformes, Rivulidae), with description of two new species from the Caatinga coastal basins, northeastern Brazil
FIGURE 8. Caudal fin of living males: Hypsolebias gongobira new species (A), H. antenori (B) e H. bonita new species (C).
FIGURE 7 in Molecular delimitation of the seasonal killifishes of the Hypsolebias antenori species group (Cyprinodontiformes, Rivulidae), with description of two new species from the Caatinga coastal basins, northeastern Brazil
FIGURE 7. Type-locality of Hypsolebias bonita new species, Brazil, Rio Grande do Norte, Baraúna, seasonal pool in the Furna Feia National Park.
Data from: Seasonal host community dynamics constrain the risk of parasite transmission between migrant and resident species
<p>Seasonal migration shapes the community dynamics that influence pathogen transmission between migrants and resident species. While theoretical and empirical evidence has accumulated, whether migration increases or decreases the risk of cross-species infection remains inconclusive. We studied how the seasonal arrival and departure of a single avian migrant species change the composition of local communities in breeding areas affecting the haemosporidian infection dynamics. The seasonal reordering of resident species abundances induced by migrants, minimizes the opportunities for contact between highly infected hosts and susceptible species, either migrants or residents, thereby limiting the transmission of parasites to occasional spillover events. The occurrence of spillover dynamics during the seasonal sympatry between migrants and residents provides a plausible explanation that reconciles empirical inconsistencies in the intersection of animal migration and infection risk at the host community level. Our findings underscore the critical role played by seasonality in shaping infection dynamics in migratory systems.</p>
Disentangling the seasonal effects of agricultural intensification on birds and bats in Mediterranean olive groves
Open the record for dataset details and reuse information.
Historical tree phenology data reveal the seasonal rhythms of the Congo Basin rainforest
<p>Tropical forest phenology directly affects regional carbon cycles, but the relation between species-specific and whole-canopy phenology remains largely uncharacterized. We present a unique dataset of historical tropical tree phenology collected in the central Congo Basin, before large-scale impacts of human-induced climate change.</p> <p>Historical data was recovered from a phenological study carried out between 1937 and 1956 in the central Congo Basin at the Yangambi Research Station (N00°48’, E24°29’) in what is now the Democratic Republic of Congo (DRC). The data were retrieved from the archives of the INERA (Institut National pour l'Etude et la Recherche Agronomique) herbarium at the Yangambi Research Station.</p> <p>Ground-based phenological observations of local tropical trees were made four times each month on a rotating schedule (resolution of 7.2 days on average) from 1937 until 1956 by the forestry division of the INEAC (Institut National pour l'Étude Agronomique du Congo). The sampling protocol was recovered at the State Archives of Belgium, including details on the observation methods, the observational routes and the training and schedules of the observers. Canopy leaf senescence was defined as a distinct period during which leaves fall and trees remain bare, while canopy turnover was defined as a period during which leaf-fall comes in peaks with concomitant flushes of new leaves (INEAC archives). Summarised data sheets of these long-term observations were digitized using 12 MP resolution cameras. The hand-written notes and annotations depicting phenophases during the observational period were digitized to binary data (yes or no phenophase event at each time-step) through an online citizen science project ‘Jungle Rhythms’ (Hufkens & Kearsley 2023, https://www.zooniverse.org/projects/khufkens/jungle-rhythms). </p> <p>A selection of species found in the historical forest inventories (Pierlot 1966) are presented, representing 96.0% of the basal area within these inventories. The phenological data comprises 668 individuals covering 140 species (representing 112 genus and 38 families) for a total of 5011 individual observation years (overview of species in Supplementary Table S1). We did not include individuals that were only identified to genus-level.</p> <p>This data accompanies the publication ‘Kearsley, E., Verbeeck, H., Stoffelen, P., Janssens, S. B., Yakusu, E. K., Kosmala, M., De Mil, T., Bauters, M., Kitima, E. R., Ndiapo, J. M., Chuda, A. L., Richardson, A. D., Wingate, L., Ilondea, B. A., Beeckman, H., van den Bulcke, J., Boeckx, P., & Hufkens, K. (2024). Historical tree phenology data reveal the seasonal rhythms of the Congo Basin rainforest. Plant-Environment Interactions, 5, e10136. https://doi.org/10.1002/pei3.10136’.</p> <p>Reference<br>Pierlot, R. (1966). Structure et composition de forêts denses d’Afrique Centrale, spécialement celles du Kivu. Academie Royale des Sciences d’Outre-Mer. Classe des Sciences naturelles et medicales. N.S. XVI-4, Bruxelles, p. 367.</p>
Data from: Seasonality and relative abundance within an elasmobranch assemblage near a major biogeographic divide
<p>Nearshore waters are utilized by elasmobranchs in various ways, including foraging, reproduction, and migration. Multiple elasmobranch species have been previously documented in the nearshore waters of North Carolina, USA, which has a biogeographic break at Cape Hatteras on the Atlantic coast. However, comprehensive understanding of the elasmobranch community in this region is still lacking. Monthly year-round trawling conducted along two ocean transects (near Cape Lookout and Masonboro Inlet in 5 to 18 m depth) in Onslow Bay, North Carolina provided the opportunity to examine the dynamics and seasonal patterns of this community using a multivariate approach, including permutational multivariate analysis of variance and nonparametric BIO-ENV analysis. From November 2004 to April 2008, 21,149 elasmobranchs comprised of 20 species were caught, dominated by spiny dogfish (<em>Squalus acanthias</em>) and clearnose skate (<em>Rostroraja eglanteria</em>). All species exhibited seasonal variation in abundance, but several key species contributed the most to seasonal differences in species composition within each transect. Spiny dogfish was most abundant in the winter at both locations, comprised mainly of mature females. Although clearnose skate was caught in all seasons, the species was most abundant during the spring and fall. Atlantic sharpnose (<em>Rhizoprionodon terraenovae</em>) was one of the most abundant species in the summer, and two distinct size cohorts were documented. Temperature appeared to be the main abiotic factor driving the community assemblage. The extensive year-round sampling provided the ability to better understand the dramatic seasonal variation in species composition and highlights the relative abundance of several understudied elasmobranch species that may be of significant ecological importance. Our results underscore the importance of inner continental shelf waters as important elasmobranch habitat and provide baseline data to examine for future shifts in timing and community structure at the northern portion of the biogeographic break at Cape Hatteras.</p>
Skillful Seasonal Prediction of Wind Energy Resources in the contiguous United States
<p>This dataset contains data files used to replicate figures in a paper published in communications earth & environment. These datasets are in .mat format, which can be readable by Matlab software.</p>
Seasonal and spatial variation of paraben concentrations in urban coastal waters from freshwater lagoons to the open ocean of Brazil
Open the record for dataset details and reuse information.
BSC Post-processed Seasonal Climate Forecast for vineyard management
<p>The Climate Services Team at the Barcelona Supercomputing Center has deployed a climate service for vineyard management in the context of the vitiGEOSS project. This dataset results from post-processing, i.e. by downscaling, calibrating and assessing, the seasonal climate prediction system SEAS5 (ECMWF).</p> <p>Probabilistic predictions have as output several solutions (ensemble members) to account for forecast uncertainty. The forecast information is conveyed as probabilities, in this case as the probabilities of occurrence of three categories or terciles (below normal, normal and above normal). The categories are defined based on the terciles of the model climatology distribution over a period in the past. Additional information regarding the probability of occurrence of extremes is also provided, considered as the probability of not reaching the 10th percentile or surpassing the 90th percentile of the model climatology distribution. The skill scores provide information on the forecast quality (fair Ranked Probability Skill Score for the tercile categories and fair Brier Skill Score for the probabilities of extremes). A positive skill score indicates that the prediction is good (better than using average past conditions) in the long term. In contrast, a negative skill score indicates a prediction is not beating the climatological forecast.</p> <ul> <li> <p>Prediction system: European Center for Medium-Range Weather Forecasts (ECMWF) SEAS5 and post-processed by BSC.</p> </li> <li> <p>Issue frequency: Monthly (~15th of each month)</p> </li> <li> <p>Lead times: months 1 to 3 (e.g. For a forecast initialised in June, forecasts will be monthly averages for July, August and September). The initialization date is indicated in the name of each file (e.g. 20210701). </p> </li> <li> <p>Variables: mean, minimum and maximum 2 m temperature, accumulated precipitation, incoming solar radiation</p> </li> <li> <p>Ensemble size: 51 members</p> </li> <li> <p>Postprocessing: Downscaling from original (1°x 1°) resolution to 0.1°x 0.1° for three domains and monthly calibration with variance inflation. </p> </li> <li> <p>Spatial coverage of the domains: </p> </li> <ul> <li> <p>Catalonia region is indicated by ‘cat’ and covers latitudes [10 N, 44 N], and longitudes [1 W, 4 E]. The latitude indices range [1:41], and the longitude indices range [1:51].</p> </li> <li> <p>Douro region is indicated by ’douro’ and covers latitudes [40 N, 43N ] and longitudes [9 W, 6 W]. The latitude indices range [1:31], and the longitude indices range [1:31].</p> </li> <li> <p>Campana region is indicated by ‘campania’ and covers latitudes [39 N, 43 N] and longitudes [13 E,17.3 E]. The latitude indices range [1:41], and the longitude indices range [1:44]. </p> </li> </ul> </ul> <p> </p> <p>For each prediction, there are several files containing the seasonal variables values, probabilities, definition of the categories and skill scores.</p> <ul> <li> <p>Forecast probabilities</p> </li> </ul> <p>E.g t2_campania_prob_20210701.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘prob’ and the initialization date of the forecasts (1st of the month).</p> <p>It contains the forecast probabilities in (%) of each tercile category below normal (prob_bn), normal (prob_n) and above normal (prob_an) and the probability of lower extreme (prob_bp10) and the probability of upper extreme (prob_ap90). The latitude, longitude, and lead time (months 1 to 3) can be selected.</p> <p><strong> </strong></p> <ul> <li> <p>Forecast ensemble members</p> </li> </ul> <p> E.g. t2_campania_20210701.ncml</p> <p>The file name contains the name of the variable, domain and initialization date of the forecasts (1st of the month).</p> <p>It contains the 51 absolute values of the forecast variables in their corresponding units (see Table 2). The latitude, longitude, and lead time (months 1 to 3) can be selected.</p> <p><strong> </strong></p> <ul> <li> <p>Category limits</p> </li> </ul> <p>E.g. t2_campania-percentiles_month07.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘percentiles’ and the month for which the category limits apply. </p> <p>It contains the limits of the predicted categories ( below normal, normal and above normal). These categories are defined with respect to a period in the past. The 33rd, 66th percentiles (p33 and p66) divide the model climatological distribution into 3 equiprobable categories. The 33th percentile is the boundary between below normal and normal, and the 66th percentile is the boundary between the normal and above normal categories. The 10th and 90th percentiles, which define the threshold for the lower and upper extreme conditions, are also provided (p10 and p90). It should be noted that the definition of the categories is specific to each location (latitude and longitude), initialization month and lead time (valid month).</p> <p><strong> </strong></p> <ul> <li> <p>Skill scores</p> </li> </ul> <p>E.g t2_campania-skill_month07.ncml</p> <p>The file name contains the name of the variable, domain, the label ‘skill’ and the month for which the skill scores apply. </p> <p>It contains the measures of forecast quality, the fair Ranked probability score for terciles (rpss) and the fair Brier Skill Score for lower and upper extremes (bsp10 and bsp90). It should be noted that the skill level is specific to each location (latitude and longitude), initialization month and lead time (valid month).</p>
Women's team soccer positioning data, collected during 2023/2024 season. Third category of female soccer, Spain.
<p>Positioning data of 20 female football players collected during the first 5 matchdays of the regular league during the season 2023/2024. They correspond to the third Spanish female category, considered semi-professional.</p>
High-resolution wall-to-wall time series predictions of seasonal maize area and yield for Rwanda over 2019-2023
<p>This is the companion dataset to publication {TBD}. It contains 1) seasonal composites of predicted maize cover and yield at 10 m resolution in Rwanda for two annual agricultural seasons over five years, 2) scripts for the end-to-end machine learning pipeline that produces these data products, and 3) data or references needed as inputs to the pipeline. </p> <h2>1) Maize cover and yield seasonal composites</h2> <p>The data are provided here as netCDF4 files with four dimensions for x, y, band, and season. They can also be accessed as Google Earth ImageCollections at: </p> <ul> <li>https://code.earthengine.google.com/?asset=projects/b2p-geospatial/assets/lulc_classifier_composite</li> <li>https://code.earthengine.google.com/?asset=projects/b2p-geospatial/assets/maize_yield_composite </li> </ul> <h3>Land cover and maize classification</h3> <p>The land cover classification file is found at <code>data/composites/lulc_classifier_Rwanda_2019to2023.nc</code>.</p> <p>The land cover classification images contain 3 bands/variables: <em>maizeProb</em>, the raw predicted probability of the pixel being maize given by the gradient boosted tree model; <em>majorityClass</em>, the categorical land cover class with the highest predicted probability among any of the nine classes in the respective pixel; and <em>optimalClass</em>, the categorical land cover class adjusted to agree with national statistics for expected maize area.</p> <p>The land cover classes map to the raster values as follows: </p> <div> <div> <pre>{<br> 1: 'maize',<br> 2: 'nonmaize_annual',<br> 3: 'nonmaize_perennial',<br> 4: 'scrub_shrub_land',<br> 5: 'forest',<br> 6: 'flooded_vegetation',<br> 7: 'water',<br> 8: 'structure',<br> 9: 'bare'<br>}</pre> </div> </div> <p>The dataset includes 5 years (2019-2023) and 10 seasons - the available time period at time of publication. In Rwanda, maize is typically planted and harvested during two distinct agricultural seasons per year: Season A from September to February and Season B from March to June. Therefore the seasons in the data are: 2019_Season_A, 2019_Season_B, 2020_Season_A, 2020_Season_B, 2021_Season_A, 2021_Season_B, 2022_Season_A, 2022_Season_B, 2023_Season_A, 2023_Season_B.</p> <h3>Maize yield</h3> <p>The maize yield file is found at <code>data/composites/maize_yield_Rwanda_2019to2023.nc</code>.</p> <p>Each of the images in the yield composites has 3 bands/variables also: <em>maizeYield</em>, the model's output of continuous predicted yield (kg/ha) in each pixel regardless of land class; <em>maizeYield_majorityClass</em>, predicted maize yield masked to the majority class land classification; and <em>maizeYieldAdj_optimalClass</em>, where the raw predicted yields were masked to the optimal maize classification land cover layer and normalized to national statistics. </p> <p>The dataset includes the same seasons as the classification product; see above for a description.</p> <h2>2) End-to-end machine learning pipeline</h2> <p>All earth observation imagery, analysis, and outputs unless otherwise stated were hosted in the Google Earth Engine (GEE) environment and developed with the Earth Engine Python API in Python v3.10. To set up a local conda environment use the <code>scripts/environment.yml</code> file. The user must have <a href="https://cloud.google.com/storage">Google Cloud Storage (GCS)</a> and <a href="https://cloud.google.com/earth-engine">Google Earth Engine (GEE)</a> accounts. The pipeline, at this scale, will incur some processing and storage fees, although Google offers a free trial to all new users and the total cost of the high-resolution wall-to-wall predictions is nominal (~$20 for one season). </p> <p>The scripts needed to perform the pipeline are located in the <code>scripts</code> folder. </p> <p>The files contained in the <code>scripts/helpers</code> directory will be called by various subsequent scripts and do not to be run interactively by the user. </p> <p>Follow the script in the order described below. The user should pause after running each script and confirm that all outputs were created and loaded to GCS before continuing the pipeline; for some steps this may take hours to days depending on processing speed. </p> <h3>Google Cloud Storage and Earth Engine set-up</h3> <p>Users should specify the names of the bucket and asset project that were chosen during set up of their GCS and GEE environments in the <em>Objects</em> section of <code>scripts/helpers/maize_pipeline_0_workspace.py</code>.</p> <h3>Pipeline set-up</h3> <p>In <code>scripts/pipeline_setup</code>, you will find the following scripts to perform data preparation of inputs into model building and prediction. </p> <ul> <li><code>maize_pipeline_1_clean_training_data.py</code> - Cleans and merges all available crop label and yield data for model training and validation</li> <li><code>maize_pipeline_2_dwnld_data_training.py</code> - Downloads satellite-derived and auxiliary features at training data points for model building</li> <li><code>maize_pipeline_3_dwnld_data_inference.py</code> - Downloads satellite-derived and auxiliary features at every 10 m pixel in Rwanda on a district-wise basis for prediction</li> </ul> <h3>Land cover and maize classification</h3> <p>In <code>scripts/maize_classification</code>, you will find the following scripts to perform model building, prediction, and post-processing for the classificaton of land cover type and maize cover.</p> <ul> <li><code>maize_classifier_1_feature_selection.py</code> - Selects features subset for land cover classification with mutual information score or variable importance</li> <li><code>maize_classifier_2_build_model.py</code> - Builds gradient boosted tree model for land cover classification from training data</li> <li><code>maize_classifier_3_prediction.py</code> - Applies model for land cover classification to every 10 m pixel in Rwanda by season and district</li> <li><code>maize_classifier_4_postprocess.py</code> - Mosaics district-wise predictions and normalizes maize cover predictions to national agricultural statistics</li> </ul> <h3>Maize yield</h3> <p>In <code>scripts/maize_yield</code>, you will find the following scripts to perform modeling building, prediction, and post-processing for maize yield estimation. </p> <ul> <li><code>maize_yield_1_build_model.py</code> - Builds gradient boosted tree model and performs bias correction for maize yield estimation from training data</li> <li><code>maize_yield_2_prediction.py</code> - Applies model for maize yield estimation to every 10 m pixel in Rwanda by season and district</li> <li><code>maize_yield_3_postprocess.py</code> - Mosaics district-wise predictions and normalizes maize yield predictions to national agricultural statistics</li> </ul> <p>If you are running the entire pipeline with refreshed training data and model building, run each of these scripts, in order. By default, the script will run all A and B seasons from 2019A to current. Otherwise, if you just wish to re-run or update seasonal predictions from the existing classification or yield model run <code>maize_pipeline_3_dwnld_data_inference.py</code> to download the seasonal feature data across Rwanda and <code>maize_classifier_3_prediction.py</code>and <code>maize_classifier_4_postprocess.py</code> for classification predictions or <code>maize_yield_2_prediction.py</code> and <code>maize_yield_3_postprocess.py</code> for yield predictions, making sure to specify which season(s) are of interest in each script. However to do this, you also need to have a copy of the previously built models in your GCS (provided at <code>data/models</code>). </p> <h2>3) Input data into machine learning pipeline</h2> <p>A description of datasets that must be sourced outside of the GEE platform is provided below. When available, the primary data source is also included in the directory <code>data/baselayers</code>. All other data, including Sentinel-2 imagery, auxiliary data, and other existing global land cover classificaiton products are hosted on GEE and called by the scripts directly. All datasets last accessed on 12 March 2024.</p> <h3>Administrative and geological boundaries</h3> <ul> <li>World Countries - Downloaded from <a href="https://datacatalog.worldbank.org/search/dataset/0038272/World-Bank-Official-Boundaries">The World Bank Official Boundaries</a> and included here at <code>data/baselayers/World_Countries</code>.</li> <li>Rwanda district boundaries - Downloaded from <a href="https://datacatalog.worldbank.org/search/dataset/0041453/Rwanda-Admin-Boundaries-and-Villages">The World Bank Rwanda Admin Boundaries And Villages</a> and included here at <code>data/baselayers/WB_NISR_2018</code>. This should be loaded into a FeatureCollection GEE asset named <em>districts_fc</em> for use in the pipeline. </li> <li>Rwanda agro-ecological zones - Downloaded from <a href="https://doi.org/10.1371/journal.pone.0149239">Nzeyimana, Hartemink & Geissen (2016)</a> and included here at <code>data/baselayers/MINAGRI_AEZ_1980</code>. This should be loaded into a FeatureCollection GEE asset named <em>aez_rwanda</em> for use in the pipeline. </li> </ul> <h3>Global land cover classification product</h3> <ul> <li>Microsoft/Impact Observatory LULC - Although the <a href="https://planetarycomputer.microsoft.com/dataset/io-lulc-9-class">10m Annual Land Use Land Cover (9-class) V1</a> product contains data from 2017-2022, only the LULC map from the year 2021 was used, provided here at <code>data/baselayers/impactobs_lulc_rwa_2021.tif</code>. This should be loaded into an ImageCollection GEE asset named <em>impact_obs_lulc</em> for use in the pipeline.</li> </ul> <p>(The others - Dynamic World and ESA's WorldCover - are hosted on GEE directly.)</p> <h3>Land cover labels and maize yield crop cuttings</h3> <ul> <li>One Acre Fund - Contact authors to request access as this dataset is not hosted publicly. </li> <li>RTI International - The original source of this data (Radiant MLHub) has been discontinued, but users may be able to access it via <a href="https://beta.source.coop/repositories/rti/rwanda-crop-type/">Source Cooperative</a>. The data is also included here at <code>data/baselayers/rti_rwanda_crop_type_labels</code>. </li> <li>Crop Harvest - Downloaded from <a href="../records/7257688">Tseng et al. (2021, v13)</a> and included here at <code>data/baselayers/CropHarvest</code>. These data points were ultimately not used in the training data, but are provided here for others that may find this dataset useful in their context.</li> </ul> <h3>Rwanda national agricultural surveys</h3> <ul> <li>National Institute of Statisitcs Rwanda (NISR) - Downloaded from <a href="https://statistics.gov.rw/datasource/seasonal-agricultural-survey">NISR Seasonal Agricultural Survey</a> and existing seasons included here at <code>data/baselayers/NISR_Seasonal_Ag_Surveys</code>. For each subsequent season, the user will have to download the spreadsheet of survey results from the NISR webpage (linked) and add the respective season to the <code>get_nisr_data</code> function in the <code>helpers/maize_pipeline_0_helpers_postprocess.py</code> script to clean and read in the data for use in the pipeline. </li> </ul> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.