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5,155 results for “Data Base”
Tower-based remote sensing data for understory vegetation at Delta Junction, Alaska 2019-2020
<p> Data includes remote sensing products from PhotoSpec (a scanning spectrometer) from August 2019-December 2020. We provide daily averaged vegetation indices for a mix of understory lichen and moss species in a black spruce dominated forest. We compute near-infrared vegetation index (NIRv), normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), and chlorophyll-carotenoid index (CCI) averaged for three understory targets at NEON Delta Junction. We also provide daily averaged photosynthetically active radiation (PAR) and solar zenith angle (SZA). Finally, we provide the average diurnal profiles of all the aforementioned metrics for 4 20-day windows in winter, spring, summer, and fall. </p>
Extra Testing Data for paper "OC_Finder: A deep learning-based software for osteoclast segmentation, classification, and counting"
<pre>Here we have 9 datasets we used to validate OC_Finder's performance on various imaging settings. The 9 datasets are inside the folder named "9 datasets for validation experiment". Each dataset is composed of image files and csv files for the coordination of osteoclasts and non-osteoclasts that were manually labelled by human examiner. csv files ending "_posi" has coordination of osteoclasts and "_nega" has coordination of non-osteoclasts. Images in dataset #4, #5, #6, #7, #8, and #9 were resized so the scale of the images matched to the OC_Finder's training dataset. Images in original size before resizing are also provided in "Original images before resizing". Detailed capture setting and resizing information of images in each dataset can be found in "capture setting.xlsx". The number of images in each dataset are as following: #1: 18 #2: 18 #3: 18 #4: 36 #5: 36 #6: 36 #7: 16 #8: 16 #9: 16</pre>
Training and test data for retrievals based on MiRAC-P observations during MOSAiC
<p>The dataset consists of one netCDF file that contains the entire training and test data for the retrieval of integrated water vapour (prw) from brightness temperatures (tb) measured by the MiRAC-P (microwave radiometer for Arctic clouds, aka. LHUMPRO-243-340). A neural network retrieval has been developed to derive the prw. The trained retrieval is applied on the MiRAC-P observations gathered onboard the research vessel Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. For the data to be specialized on Arctic conditions they are based on ERA-Interim reanalysis. An IDL-based radiative transfer model has been used to simulate brightness temperatures. The elevation angle (ele) is always 90° because the MiRAC-P performed zenith scans only throughout the MOSAiC campaign.</p>
Fatiando a Terra Data: Southern Africa - Ground-based gravity
<p>This is a public domain compilation of ground measurements of gravity from Southern Africa. The observations are the absolute gravity values in mGal. The horizontal datum is not specified and heights are referenced to "sea level", which we will interpret as the geoid (which realization is likely not relevant since the uncertainty in the height is probably larger than geoid model differences).</p> <p><strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please <strong>cite the original authors</strong> when using this dataset.</p> <p><strong>Changes made: </strong>Keep only coordinates, absolute gravity, and the (sea-level) observation height. Remove some points below sea-level (a bit suspicious and are potentially flawed heights from shipborne measurements). Convert from a custom text format to compressed CSV.</p> <p><strong>Source: </strong><a href="https://www.ngdc.noaa.gov/mgg/gravity/">NOAA NCEI</a></p> <p><strong>Source license: </strong><a href="https://ngdc.noaa.gov/ngdcinfo/privacy.html">public domain</a></p> <p><strong>Repository:</strong> <a href="https://github.com/fatiando-data/southern-africa-gravity">https://github.com/fatiando-data/southern-africa-gravity </a></p>
Region-based Annotation Data of Fire Images for Intelligent Surveillance System
<p>This data presents fire segmentation annotation data on 12 commonly used and publicly available “VisiFire Dataset” videos from <a href="http://signal.ee.bilkent.edu.tr/VisiFire/">http://signal.ee.bilkent.edu.tr/VisiFire/</a>. This annotations dataset was obtained by per-frame, manual hand annotation over the fire region with 2,684 total annotated frames. Since this annotation provides per-frame segmentation data, it offers a new and unique fire motion feature to the existing video, unlike other fire segmentation data that are collected from different still images. The annotations dataset also provides ground truth for segmentation task on videos. With segmentation task, it offers better insight on how well a machine learning model understood, not only detecting whether a fire is present, but also its exact location by calculating metrics such as Intersection over Union (IoU) with this annotations data. This annotations data is a tremendously useful addition to train, develop, and create a much better smart surveillance system for early detection in high-risk fire hotspots area. </p>
Revisiting the cumulative effects of drought on global gross primary productivity based on new long-term series data (1982-2018)
<p><strong>Aim:</strong> Drought has broad and deep impacts on vegetation. Studies on the effects of drought on vegetation have been conducted over years. However, global-scale and long-term (>30 years) studies on the cumulative<strong> </strong>effect of drought are still lacking. Thus, combining a new satellite based gross primary productivity (GPP) and multi-timescale Standardized Precipitation Evapotranspiration Index datasets, we investigated the cumulative effect of drought on global vegetation GPP.</p> <p><strong>Location: </strong>Global.</p> <p><strong>Time period: </strong>1982 – 2018 (37 years).</p> <p><strong>Major taxa studied: </strong>Forests and grasslands.</p> <p><strong>Method: </strong>Based on correlation analysis framework, we investigated the cumulative effect duration of drought on global vegetation GPP. Meanwhile, the variability of this cumulative effect across different elevation gradients and climatic zones was analyzed using variance analysis.</p>
mDRONES4rivers-project: Classification results based on UAV data of project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany
<p>Spatially and temporally high-resolution data was acquired with the aid of multispectral sensors mounted on UAV and a gyrocopter platform for the purpose of classification. The work was part of the research and development project „Modern sensors and airborne remote sensing for the mapping of vegetation and hydromorphology along Federal waterways in Germany“ (mDRONES4rivers) in cooperation of the German Federal Institute of Hydrology (BfG), Geocoptix GmbH, Hochschule Koblenz und JB Hyperspectral Devices. <br> Within the project period (2019-2022) an object oriented image classification was conducted based on UAV and gyrocopter data for different sites situated in Germany along the Rivers Rhine and Oder. All published data produced within the project can be found by searching for the keyword ‘mDRONES4rivers‘. <br> In this dataset, the following classification results and metadata of the project sites situated in riparian zones along federal waterways in Germany with focus on the Rhine River, Germany is available for download:<br> • Basic & Vegetation Classification (ESRI Shapefile; abbreviation: lvl2_vegetation_units)<br> • Classification of dominant stands (ESRI Shapefile; abbreviation: lvl4_dominant_stands )<br> • Classification of substrat types (ESRI Shapefile; abbreviation: lvl4_substrate_types)<br> • associated reports (PDF; statistical and additional information on the classifiaction results and workflow)<br> The above-mentioned files are provided for download as dataset stored in one directory per projekt site and season (e.g. mDRONES4rivers_Niederwerth_2019_03_Summer_Classification.zip = projectname_projectsite_year_no.season_name.season_product). To provide an overview of all files and general background information plus data preview the following files are additionally provided: <br> • Portfolios (PDF, Detailed description of classification products and classification workflow, 1x for basic surface types, 1x for classification of vegetation units, 1x for classification of dominant stands, 1x for classification of substrate types)<br> • Color Coding table for the visualization of the classifiaction units (.xlsx)</p>
Data used to evaluate ORBITS: Optimal Repair-Based Inconsistency-Tolerant Semantics
<p>This dataset provides the input files that were used in the evaluation of the ORBITS system (Optimal Repair-Based Inconsistency-Tolerant Semantics, <a href="https://github.com/bourgaux/orbits">https://github.com/bourgaux/orbits</a>). A detailed description is available in a technical report on arXiv (<a href="https://arxiv.org/abs/2202.07980">https://arxiv.org/abs/2202.07980</a>).</p> <p><strong>Content:</strong></p> <p>Folders <em>cqapri_benchmark</em>, <em>food_inspection_benchmark</em>, and <em>physicians_benchmark</em> contain JSON files of conflict graphs and candidate queries and their causes.<br> These files are named using the following pattern: files of candidate answers and their causes are named <database>_<query>_answers_causes.json, and conflict graphs are named <database>_conflictGraph_<priority relation>.json where <priority relation> says whether the priority relation is score-structured (prio_score) or not (prio_non_score) and the probability (p<proba>) or number of scores (n<number>) used to build the priority relation.</p> <p>Folder <em>original_datasets_and_queries</em> contains the Food Inspection and Physicians datasets used to generate files from <em>food_inspection_benchmark</em> and <em>physicians_benchmark</em>.<br> Files from <em>cqapri_benchmark</em> have been generated from the CQAPri benchmark available at <a href="https://lahdak.lri.fr/CQAPri/CQAPri.php">https://lahdak.lri.fr/CQAPri/CQAPri.php</a>.<br> In all cases, we use ProvSQL (<a href="https://github.com/PierreSenellart/provsql">https://github.com/PierreSenellart/provsql</a>) to build conflict graphs and causes from the datasets.</p>
Life cycle-based environmental impacts of energy scenarios - additional data
<p>This data set documents additional results of the paper "Life cycle-based environmental impacts of energy system transformation strategies for Germany: Are climate and environmental protection conflicting goals?" (Tobias Naegler and co-authors, published in Energy Reports (2020), https://doi.org/10.1016/j.egyr.2022.03.143). It shows life cycle-based environmental impacts for 10 different transformation strategies for the German energy and transport system.</p>
Data and Analysis for "On the Reliability of Coverage-based Fuzzer Benchmarking"
<pre><strong>Data and Analysis for "On the Reliability of Coverage-based Fuzzer Benchmarking"</strong> <strong>## Cite</strong> </pre> <pre><code>@inproceedings{benchmarking, author = {B{\"o}hme, Marcel and Szekeres, L{\'a}szl{\'o} and Metzman, Jonathan}, title = {On the Reliability of Coverage-based Fuzzer Benchmarking}, year = {2022}, booktitle = {Proceedings of the 44th International Conference on Software Engineering}, series = {ICSE '22}, pages = {1-13}, doi = {10.1145/3510003.3510230} }</code></pre> <pre> <strong>## Data Analysis</strong> The Jupyter notebook generating all tables and figures can be found in fuzzbench.manual.ipynb <strong>## Generated Images and Tables</strong> The generated data analysis artifacts are also available in this artifact. <strong>## Data</strong> All the data is available in the FuzzBench Reports and will be automatically downloaded. * 20 trials of 23 hours with 15 programs and 10 fuzzers. * Experiment name: 2021-02-17-bug-paper * Report: https://www.fuzzbench.com/reports/2021-02-17-bug-paper/index.html * Data: https://www.fuzzbench.com/reports/2021-02-17-bug-paper/data.csv.gz * Fuzzbench Commit: [38e344fef2f1079579391a0d9dcb52319f7051f2](https://github.com/google/fuzzbench/commits/38e344fef2f1079579391a0d9dcb52319f7051f2) * 30 trials of 23 hours with 11 programs and 10 fuzzers. * Experiment name: 2021-08-19-crash-s * Report: https://www.fuzzbench.com/reports/2021-08-19-crash-s/index.html and * Data: https://www.fuzzbench.com/reports/2021-08-19-crash-s/data.csv.gz * Fuzzbench Commit: db192b60815ac87f69ee0f7f3e37aeac71949e1b * 30 trials of 23 hours with 11 programs and 10 fuzzers. * Experiment name: 2021-08-19-crash-s2 * Report: https://www.fuzzbench.com/reports/2021-08-19-crash-s2/index.html and * Data: https://www.fuzzbench.com/reports/2021-08-19-crash-s2/data.csv.gz * Fuzzbench Commit: db192b60815ac87f69ee0f7f3e37aeac71949e1b The deduplicated data can be found in * 2021-02-17-bug-paper-fixed2.csv.gz * 2021-08-19-crash-s-fixed2.csv.gz * 2021-08-19-crash-s2-fixed2.csv.gz <strong>## Reproducibility</strong> </pre> <pre><code class="language-bash"># Download the precise version of FuzzBench used for the experiment git clone https://github.com/google/fuzzbench.git cd fuzzbench git checkout <Fuzzbench Commit> # Download the internal config file. curl https://storage.googleapis.com/[experiment-name]/config/experiment.yaml > /tmp/experiment-config.yaml make install-dependencies # Launch the experiment using paramters from the internal config file. PYTHONPATH=. python experiment/reproduce_experiment.py -c /tmp/experiment-config.yaml -e <new_experiment_name></code></pre> <p> </p>
Supporting publication for 'Prevalence sample-based guidance for reporting 2021 data'
<p>The record is aimed at helping the reporting countries to submit their sample-based level data to the EFSA Data Collection Framework. We include here two excel files and one XML file, and we give below specific information on their use.</p> <p>The two Excel documents help in mapping terms from the matrix catalogue ZOO_CAT_MATRIX used in the aggregated prevalence data model to FoodEx2 codes, and offer examples on how prevalence data can be reported using SSD2 and how data are aggregated afterwards. The XML file is the same example as in the Excel file with similar title but in the XML format that allows for it be uploaded in the Data Collection Framework.</p>
Data set for "Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2"
<p>Data sets for the publication "Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS<sub>2</sub>", doi:10.1021/acsnano.1c07065</p>
Data and scripts for: Genetic dissection of seasonal vegetation index dynamics in maize through aerial based high-throughput phenotyping
<p>Plant phenotyping under field conditions plays an important role in agricultural research. Efficient and accurate high-throughput phenotyping strategies enable a better connection between genotype and phenotype. Unmanned aerial vehicle-based high-throughput phenotyping platforms (UAV-HTPPs) provide novel opportunities for large-scale proximal measurement of plant traits with high efficiency, high resolution, and low cost. The objective of this study was to use time series normalized difference vegetation index (NDVI) extracted from UAV-based multispectral imagery to characterize its pattern across development and conduct genetic dissection of NDVI in a large maize population. The time series NDVI data from the multispectral sensor were obtained at 5 time points across the growing season for 1,752 diverse maize accessions with a UAV-HTPP. Cluster analysis of the acquired measurements classified 1,752 maize accessions into 2 groups with distinct NDVI developmental trends. To capture the dynamics underlying these static observations, penalized-splines (P-splines) model was used to obtain genotype-specific curve parameters. Genome-wide association study (GWAS) using static NDVI values and curve parameters as phenotypic traits detected signals significantly associated with the traits. Additionally, GWAS using the projected NDVI values from the P-splines models revealed the dynamic change of genetic effects, indicating the role of gene-environment interplay in controlling NDVI across the growing season. Our results demonstrated the utility of ultra-high spatial resolution multispectral imagery, as that acquired using a UAV-based remote sensing, for genetic dissection of NDVI.</p>
CCG Starter Kits - Technology-specific data for Base SAND file
<p>These files contain the Capacity Factors and Residual Capacity values for all countries needed for filling in the information in the base SAND file.</p> <p>This is published as part of the MethodsX paper titled <strong>How to put together a Starter Data Kit from scratch? An extensive methodology to compile zero-order energy transition models. </strong>The main goal of the files published for this paper is to develop a set of credible data and an initial investment model for several developing countries.</p>
Data for: Hematology and biochemistry of critically endangered radiated tortoises (Astrochelys radiata): reference intervals in previously confiscated subadults and variability based on common techniques
<p>Dataset for: Brenn-White M, Raphael BL, Rakotoarisoa NAT, Deem SL. 2022. Hematology and biochemistry of critically endangered radiated tortoises (<em>Astrochelys radiata</em>): reference intervals in previously confiscated subadults and variability based on common techniques. PLOS One.</p> <p>Dataset is contained in a.radiata_bloodwork.csv. Metadata and data definitions are contained in a.radiate_bloodwork_readme.txt.</p> <p>Dataset contains hematology and biochemistry values of 120 previously confiscated, clinically healthy subadult radiated tortoises living under human care within their native habitat at the Tortoise Conservation Center (TCC), Madagascar. To evaluate the effects of different commonly used techniques on these parameters, we compared results between two venipuncture sites (subcarapacial sinus and brachial vein) and three different WBC quantification methods (Natt and Herrick, Leukopet<sup>TM</sup>, and slide estimate). Data from tortoises removed from the final analyses due to sample quality issues are also included as frequency of sample quality issues varied by venipuncture site. See associated publication for detailed methods. </p> <p> </p>
Data for Falgenhauer, et al., "Transcriptional interference in toehold switch-based RNA circuits"
<p>Contains DNA sequences of the gene specific primers for RT-qPCR, the RT-qPCR raw data and output files used in "Transcriptional interference in toehold switch-based RNA circuits" by Falgenhauer et al.</p>
Projected changes in droughts and extreme droughts in Great Britain are strongly influenced by the choice of drought index: UKCP18-based SPI and SPEI data
<p>Standardised Precipitation Index (SPI; McKee et al., 1993) and Standardised Precipitation Evapotranspiration Index (SPEI; Vicente-Serrano et al., 2009) computed from UKCP18 Strand 3 simulations (Met Office Hadley Centre, 2018).</p> <p>This data was produced for the study by Reyniers et al. (<em>in prep</em>) analysing (diferences in) drought projections using these indicators. The methodology used to produce this data can be found there if/when the paper is accepted, however do not hesitate to reach out with any further questions. Please note the RCM data was bias adjusted prior to SPI and SPEI computation. There is one file per ensemble member containing the full simulated period on a monthly time step, using aggregation periods of 1, 3, 6, 12, 24 and 36 months for the computation of SP(E)I.</p> <p><strong>References</strong></p> <p>McKee, T. B., Doesken, N. J., Kleist, J., et al.: The relationship of drought frequency and duration to time scales, in: Proceedings of the 8th Conference on Applied Climatology, vol. 17, pp. 179–183, Boston, 1993</p> <p>Met Office Hadley Centre (2018): UKCP18 Regional Projections on a 12km grid over the UK for 1980-2080. Centre for Environmental Data Analysis, <em>date of citation</em>. <a href="https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604">https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604</a></p> <p>Reyniers, N., Osborn, T. J., Addor, N., Darch, G.: Projected changes in droughts and extreme droughts in Great<br> Britain are strongly influenced by the choice of drought index. Hydrology and Earth System Sciences, <em>in prep. for HESS</em></p> <p>Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I.: A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index, Journal of Climate, 23, 1696–1718, https://doi.org/10.1175/2009JCLI2909.1, 2009.</p>
Data for "Random forest-based modeling of stream nutrients at national level in a data-scarce region"
<p>The aim of the study was to model annual total nitrogen (TN) and total phosphorus (TP) concentrations at national level using an ML approach. We used water quality data originating from the Environmental Monitoring Database KESE to train RF models for nutrient concentration prediction in 242 catchments across Estonia. A total of 82 environmental variables were used as predictors in the models. In order to yield the best results, a feature selection strategy along with hyperparameter optimization was performed when building the models. The models are applicable for predicting nutrient loads on an annual level, e.g. for the purpose of reporting national level water quality statistics in regional projects, such as HELCOM. The results showed that this relatively basic RF modeling approach can have a performance similar to process-based models. Moreover, these models are easier to reuse and apply on a larger scale, since the required inputs can be derived from freely available datasets (e.g. satellite imagery)</p> <p>This repository contains the input data used for building the RF models and the files describing the modeling results.</p> <p>The description of the files is given in the README.txt file.</p> <p>Virro, H., Kmoch, A., Vainu, M. and Uuemaa, E., 2022. Random forest-based modeling of stream nutrients at national level in a data-scarce region. Science of The Total Environment, 840, p.156613.</p> <p><a href="https://doi.org/10.1016/j.scitotenv.2022.156613">https://doi.org/10.1016/j.scitotenv.2022.156613</a></p>
MetaPro: a web-based metabolomics application for MS data batch inspection and library curation
<p>MetaPro is a metabolomics web analysis platform built on the Aird data format with high performance and high compression. This platform includes a series of necessary functions for metabolomics analysis such as quality control, retention time(RT) alignment, target analysis, untarget analysis, manual integration, batch inspection, MS2 library establishment, and report export, providing efficient data analysis, management and visualization capabilities</p>
Dataset - Identification of early abandonment in cropland through radar-based coherence data and application of a Random-Forest model
<p>This dataset accompanies the manuscript titled "Identification of early abandonment in cropland through radar-based coherence data and application of a Random-Forest model", submitted by co-authors to the journal Global Change Biology (GCB) Bioenergy.</p> <p>Wouter Meijninger<sup>1</sup>, Berien Elbersen<sup>1</sup>, Michiel van Eupen<sup>1</sup>, Stephan Mantel<sup>2</sup>, Pilar Ciria Ciria<sup>3</sup>, Andrea Parenti<sup>4</sup>, Marina Sanz Gallego<sup>3</sup> and Paloma Perez Ortiz<sup>3</sup>, Marco Acciai<sup>4</sup>,and Andrea Monti<sup>4</sup><br> Institutes: 1) Wageningen University & Research, 2) ISRIC, 3) CIEMAT, 4) Bologna University,</p> <p><strong>Abstract (Manuscript)</strong></p> <p>In the context of increased pressures on land for food and non-food production it is relevant to understand better, which land resources have become unused and abandoned and where these lands are. Data on where these lands are and what their extend is are not collected in regular statistics. In this paper we present an approach to detect signs of abandonment in cropping land using radar coherence data. The methodology was tested in the Spanish regions of Albacete and Soria where agricultural land abandonment is a common process. The results show that land abandonment detection using radar coherence data works well for the region of Albacete in arable lands. The radar-based analysis is a relatively simple method to detect land abandonment in an early to longer-term state and can therefore be applied once developed and tested further in other regions to larger areas of the EU where land abandonment is serious and needs monitoring and policy response. The applicability of the method to Soria and Emilia Romagna (Italy) regions show that there are still challenges to overcome to make the method more widely applicable for detecting land abandonment in other environmental zones of Europe. Lack of reliable training and validation data, like LPIS data, in regions is one of the challenges in this respect.</p> <p><strong>Readme data files</strong></p> <p><em>Coherence_quarterly_statisitcs_2017_to_2020.zip</em></p> <p>Radar coherence quarterly statistics - Albacete (Spain)</p> <p>Radar coherence data is based on Sentinel-1B<br> Period: 2017 to 2020</p> <p>File naming (.tif files) per year (<em>YYYY</em>):</p> <ul> <li>Mean coherence: <em>mean_YYYY_1to4.tif</em></li> <li>Standard deviation coherence: <em>std_YYYY_1to4.tif</em></li> <li>Range coherence: <em>range_YYYY_1to4.tif</em></li> <li>Mean delta coherence: <em>mean_delta_YYYY_1to4.tif</em></li> <li>Standard deviation delta coherence: <em>std_delta_YYYY_1to4.tif</em></li> <li>Maximum delta coherence: <em>max_delta_YYYY_1to4.tif</em></li> </ul> <p>Each file consists of 4 bands:</p> <ul> <li>band 1: 1st quarter [Jan-Feb-March]</li> <li>band 2: 2nd quarter [April-May-June]</li> <li>band 3: 3rd quarter [July-Aug-Sept]</li> <li>band 4: 4th quarter [Oct-Nov-Dec]</li> </ul> <p>Statistics are based on radar coherence data, which is scaled between >0 and 1<br> No data: 0-values</p> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p> <p><em>SIGPAC_data_Albacete_2018_to_2020.zip</em></p> <ul> <li>More than 5 year fallow (20m raster files)</li> <li>Land Use Land Cover LULC (20m raster files)</li> </ul> <p>More than 5 year fallow (according to SIGPAC)<br> Period: 2018 to 2020<br> File naming (ENVI files):</p> <ul> <li>Albacete_SIGPAC_MoreThan5YrsFallowAreas_2018_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2018_20m.hdr)</li> <li>Albacete_SIGPAC_MoreThan5YrsFallowAreas_2019_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2019_20m.hdr)</li> <li>Albacete_SIGPAC_MoreThan5YrsFallowAreas_2020_20m.dat (+ Albacete_SIGPAC_MoreThan5YrsFallowAreas_2020_20m.hdr)</li> </ul> <p>Pixel values:<br> 0: Not fallow<br> 1: Fallow more than 5 years</p> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p> <p>Land Use Land Cover LULC (according to SIGPAC)<br> Period: 2018 to 2020<br> File naming (ENVI files):</p> <ul> <li>LULC_SIGPAC_Albacete_2018_20m.dat (+ LULC_SIGPAC_Albacete_2018_20m.hdr)</li> <li>LULC_SIGPAC_Albacete_2019_20m.dat (+ LULC_SIGPAC_Albacete_2019_20m.hdr)</li> <li>LULC_SIGPAC_Albacete_2020_20m.dat (+ LULC_SIGPAC_Albacete_2020_20m.hdr)</li> </ul> <p>Pixel values:</p> <ul> <li>0 - Nan</li> <li>1 - Arable land</li> <li>2 - Vineyards</li> <li>3 - Olives</li> <li>4 - Fruits</li> <li>5 - Nuts</li> <li>6 - Citrus</li> <li>7 - Permanent grassland</li> <li>8 - Forest</li> <li>9 - Rest, small elements</li> <li>10 - Built-up areas</li> <li>11 - Water</li> <li>12 - Roads</li> <li>13 - Unproductive land</li> </ul> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p> <p><em>Annual_unused_used_land_maps_Albacete_2017_to_2020.zip</em></p> <p>Derived annual unused/used land maps - Albacete (Spain), based on Random-Forest model<br> Period: 2017-2020<br> File naming (ENVI files):</p> <ul> <li>predict_RF_Albacete_2017_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2017_quarterly_stats_LU1_v181920.hdr)</li> <li>predict_RF_Albacete_2018_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2018_quarterly_stats_LU1_v181920.hdr)</li> <li>predict_RF_Albacete_2019_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2019_quarterly_stats_LU1_v181920.hdr)</li> <li>predict_RF_Albacete_2020_quarterly_stats_LU1_v181920.dat (+ predict_RF_Albacete_2020_quarterly_stats_LU1_v181920.hdr)</li> </ul> <p>Pixel values:<br> 0 - Used (and/or Nan)<br> 1 - Unused</p> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p> <p><em>Four_year_abandoned_land_Albacete_2017_to_2020.zip</em></p> <p>Four-year abandonment map is based on the 4 annual unused/used land maps<br> File naming (ENVI):</p> <ul> <li>Four_year_abandoned_land_Albacete_2017_to_2020.dat (+ Four_year_abandoned_land_Albacete_2017_to_2020.hdr)</li> </ul> <p>Pixel values:<br> 0 - (Nan)<br> 1 - Used (1 year unused in period 2017 - 2020)<br> 2 - Used (2 year unused in a row in period 2017 - 2020)<br> 3 - Abandoned (3 year unused in a row in period 2017 - 2020)<br> 4 - Abandoned (4 year unused in a row in period 2017 - 2020)</p> <p>Projection:<br> EPSG:32630 - WGS 84 / UTM zone 30N<br> Pixel size: 20m</p>
ScienceDex guides
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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.