Find research datasets worth reusing

openCC (other)Oct 2023View details →

SBC LTER: Beach: Time-series of beach wrack cover and biomass, ongoing since 2008

These data describe the composition, cover, depth, and wet biomass of macrophyte wrack accumulated in the intertidal zone measured on five selected sandy beaches of the mainland coast of the Santa Barbara Channel. Data collection began in 2008 and this dataset is updated annually. Two data tables in this data package: 1. Wrack_Cover_All_Years. This is long-term time-series dataset starting in 2008 with fixed wrack species codes. 2. The Wrack_Cover_NON_FILLED_Partialkep dataset is added in 2019, to distinguish whether the fresh or old blade, stipe and holdfast was a fragment or attached to a whole plant.

openCC (other)Aug 2024View details →

SBC LTER: Ocean: Time-series: Mid-water SeaFET and CO2 system chemistry at Alegria (ALE), 2011-2014

Calibrated pH (Total scale, SeaFET sensor) data was collected from Alegria in the Santa Barbara Channel (site ID: ALE) along with in situ temperature and associated carbonate chemistry parameters. The SeaFET instrument is located about 4 meters from the surface, with other moored instruments. Associated carbonate chemistry parameters were calculated with the CO2calc programs from USGS, and include: partial pressure and fugosity of CO2, concentrations of bicarbonate, carbonate and hyrdroxide ion, Omega (saturation state) of calcite and aragonite. Data have been interpolated to a 20 minute time interval for compatibility with other SBC LTER moored instrument data. Data coverage is 2011-06-21 to 2014-01-07.The update of this dataset was terminated in 2019. All data from this site have been concatenated with the pH data from the other sites and merged into one data package: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-sbc&identifier=6005

openCC (other)Jun 2025View details →

SBC LTER: Ocean: Time-series: nearshore dissolved oxygen and temperature outside of reefs, ongoing since 2011

Dissolved oxygen (miniDOT sensors) data was collected from 6 reefs in the Santa Barbara Channel along with in situ temperature. Most dissolved oxygen sensors are deployed together with SBC long-term mooring instruments. Data collection intervals and miniDOT sensor depths vary based on the site location.

openCC (other)Sep 2025View details →

SBC LTER: Beach: Time-series of beach wrack consumers, ongoing since 2011

These data contain the composition, count, and wet biomass of macroinvertebrates along intertidal transects at five selected sandy beaches of the mainland coast of the Santa Barbara Channel. Data collection began in 2012 and is ongoing. This dataset is updated annually. Macroinvertebrates were collected from uniformly spaced cores along the shore-normal transects also used for physical measurements and macrophyte wrack sampling. Cores were pooled and animals and organic material sieved. In the laboratory, all macroinvertebrate species were identified, enumerated, blotted dry and weighed.

opencc-by-4.0Nov 2023View details →

Labeled high-resolution orthoimagery time-series of an alluvial river corridor; Elwha River, Washington, USA.

<h2>Labeled high-resolution orthoimagery time-series of an alluvial river corridor; Elwha River, Washington, USA.</h2><h4>Daniel Buscombe, Marda Science LLC</h4>There are two datasets in this data release:1. Model training dataset. A manually (or semi-manually) labeled image dataset that was used to train and evaluate a machine (deep) learning model designed to identify subaerial accumulations of large wood, alluvial sediment, water, and vegetation in orthoimagery of alluvial river corridors in forested catchments. 2. Model output dataset. A labeled image dataset that uses the aforementioned model to estimate subaerial accumulations of large wood, alluvial sediment, water, and vegetation in a larger orthoimagery dataset of alluvial river corridors in forested catchments. All of these label data are derived from raw gridded data that originate from the U.S. Geological Survey (Ritchie et al., 2018). That dataset consists of 14 orthoimages of the Middle Reach (MR, in between the former Aldwell and Mills reservoirs) and 14 corresponding Lower Reach (LR, downstream of the former Mills reservoir) of the Elwha River, Washington, collected between the period 2012-04-07 and 2017-09-22. That orthoimagery was generated using SfM photogrammetry (following Over et al., 2021) using a photographic camera mounted to an aircraft wing. The imagery capture channel change as it evolved under a ~20 Mt sediment pulse initiated by the removal of the two dams. The two reaches are the ~8 km long Middle Reach (MR) and the lower-gradient ~7 km long Lower Reach (LR). The orthoimagery have been labeled (pixelwise, either manually or by an automated process) according to the following classes (inter class in the label data in parentheses):1. vegetation / other (0)2. water (1)3. sediment (2)4. large wood (3)<h3>1. Model training dataset.</h3>Imagery was labeled using a combination of the open-source software Doodler (Buscombe et al., 2021; <a href="https://github.com/Doodleverse/dash_doodler">https://github.com/Doodleverse/dash_doodler</a>) and hand-digitization using QGIS at 1:300 scale, rasterizeing the polygons, and gridded and clipped in the same way as all other gridded data. Doodler facilitates relatively labor-free dense multiclass labeling of natural imagery, enabling relatively rapid training dataset creation. The final training dataset consists of 4382 images and corresponding labels, each 1024 x 1024 pixels and representing just over 5% of the total data set. The training data are sampled approximately equally in time and in space among both reaches. All training and validation samples purposefully included all four label classes, to avoid model training and evaluation problems associated with class imbalance (Buscombe and Goldstein, 2022). Data are provided in geoTIFF format. The imagery and label grids (imagery) are reprojected to be co-located in the NAD83(2011) / UTM zone 10N projection, and to consist of 0.125 x 0.125m pixels.Pixel-wise labels measurements such as these facilitate development and evaluation of image segmentation, image classification, object-based image-analysis (OBIA), and object-in-image detection models, and numerous potential other machine learning models for the general purposes of river corridor classification, description, enumeration, inventory, and process or state quantification. For example this dataset may serve in transfer learning contexts for application in different river or coastal environments or for different tasks or class ontologies.<h4>Files:</h4>1. Labels_used_for_model_training_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 63 MB, label tiffs2. Model_training_ images1of4.zip, 1.5 GB, imagery tiffs3. Model_training_ images2of4.zip, 1.5 GB, imagery tiffs4. Model_training_ images3of4.zip, 1.7 GB, imagery tiffs5. Model_training_ images4of4.zip, 1.6 GB, imagery tiffs<h3>2. Model output dataset.</h3>Imagery was labeled using a deep-learning based semantic segmentation model (Buscombe, 2023) trained specifically for the task using the Segmentation Gym (Buscombe and Goldstein, 2022) modeling suite. We use the software package Segmentation Gym (Buscombe and Goldstein, 2022) to fine-tune a Segformer (Xie et al., 2021) deep learning model for semantic image segmentation. We take the instance (i.e. model architecture and trained weights) of the model of Xie et al. (2021), itself fine-tuned on ADE20k dataset (Zhou et al., 2019) at resolution 512x512 pixels, and fine-tune it on our 1024x1024 pixel training data consisting of 4-class label images.The spatial extent of the imagery in the MR is [455157.2494695878122002,5316532.9804129302501678 : 457076.1244695878122002,5323771.7304129302501678] (NAD83(2011) / UTM zone 10N). Imagery width is 15351 pixels and imagery height is 57910 pixels. The spatial extent of the imagery in the LR is [457704.9227139975992031,5326631.3750646486878395 : 459241.6727139975992031,5333311.0000646486878395] (NAD83(2011) / UTM zone 10N). Imagery width is 12294 pixels and imagery height is 53437 pixels. Data are provided in Cloud-Optimzed geoTIFF (COG) format. The imagery and label grids (imagery) are reprojected to be co-located in the NAD83(2011) / UTM zone 10N projection, and to consist of 0.125 x 0.125m pixels. All grids have been clipped to the union of extents of active channel margins during the period of interest.Reach-wide pixel-wise measurements such as these facilitate comparison of wood and sediment storage at any scale or location. These data may be useful for studying the morphodynamics of wood-sediment interactions in other geomorphically complex channels, wood storage in channels, the role of wood in ecosystems and conservation or restoration efforts. <h4>Files:</h4>1. Elwha_MR_labels_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 9.67 MB, label COGs from Elwha River Middle Reach (MR)2. ElwhaMR_ imagery_ part1_ of_ 2.zip, 566 MB, imagery COGs from Elwha River Middle Reach (MR)3. ElwhaMR_ imagery_ part2_ of_ 2.zip, 618 MB, imagery COGs from Elwha River Middle Reach (MR)3. Elwha_LR_labels_Buscombe_Labeled_high_resolution_orthoimagery_time_series_of_an_alluvial_river_corridor_Elwha_River_Washington_USA.zip, 10.96 MB, label COGs from Elwha River Lower Reach (LR)4. ElwhaLR_ imagery_ part1_ of_ 2.zip, 622 MB, imagery COGs from Elwha River Middle Reach (MR)5. ElwhaLR_ imagery_ part2_ of_ 2.zip, 617 MB, imagery COGs from Elwha River Middle Reach (MR)  This dataset was created using open-source tools of the Doodleverse, a software ecosystem for geoscientific image segmentation, by Daniel Buscombe (<a href="https://github.com/dbuscombe-usgs">https://github.com/dbuscombe-usgs</a>) and Evan Goldstein (<a href="https://github.com/ebgoldstein">https://github.com/ebgoldstein</a>). Thanks to the contributors of the Doodleverse!. Thanks especially Sharon Fitzpatrick (<a href="https://github.com/2320sharon">https://github.com/2320sharon</a>) and Jaycee Favela for contributing labels. <h3>References</h3>• Buscombe, D. (2023). Doodleverse/Segmentation Gym SegFormer models for 4-class (other, water, sediment, wood) segmentation of RGB aerial orthomosaic imagery (v1.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8172858">https://doi.org/10.5281/zenodo.8172858</a>• Buscombe, D., Goldstein, E. B., Sherwood, C. R., Bodine, C., Brown, J. A., Favela, J., et al. (2021). Human-in-the-loop segmentation of Earth surface imagery. Earth and Space Science, 9, e2021EA002085. <a href="https://doi.org/10.1029/2021EA002085">https://doi.org/10.1029/2021EA002085</a>• Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. <a href="https://doi.org/10.1029/2022EA002332">https://doi.org/10.1029/2022EA002332</a> See: <a href="https://github.com/Doodleverse/segmentation_gym">https://github.com/Doodleverse/segmentation_gym</a>• Over, J.R., Ritchie, A.C., Kranenburg, C.J., Brown, J.A., Buscombe, D., Noble, T., Sherwood, C.R., Warrick, J.A., and Wernette, P.A., 2021, Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6—Structure from motion workflow documentation: U.S. Geological Survey Open-File Report 2021–1039, 46 p., <a href="https://doi.org/10.3133/ofr20211039">https://doi.org/10.3133/ofr20211039</a>.• Ritchie, A.C., Curran, C.A., Magirl, C.S., Bountry, J.A., Hilldale, R.C., Randle, T.J., and Duda, J.J., 2018, Data in support of 5-year sediment budget and morphodynamic analysis of Elwha River following dam removals: U.S. Geological Survey data release, <a href="https://doi.org/10.5066/F7PG1QWC">https://doi.org/10.5066/F7PG1QWC</a>.• Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M. and Luo, P., 2021. SegFormer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, 34, pp.12077-12090.• Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A. and Torralba, A., 2019. Semantic understanding of scenes through the ade20k dataset. International Journal of Computer Vision, 127, pp.302-321.

opencc-by-4.0Dec 2023View details →

Sentinel-5P Methane Density at 2 km from 2021-12 to 2023-11 Monthly Aggregation Time-series Reconstructed

General DescriptionThe monthly aggregated Methane Volume Mixing Ratio dataset is derived from Sentinel-5P to generate a time-series reconstructed monthly aggregated map. The dataset time spans from December 2021 to November 2023 and provides data that covers the entire globe. The mission is still underway and expected to update periodically.For more info about the s5p Methane product see: <a href="">https://maps.s5p-pal.com/ch4/</a>.The dataset can be used in many applications like emission tracing, livestock monitor, and greenhouse gas monitor.<ul><li>Monthly time-series:</li></ul>Methane monthly average value December 2021 – November 2023. Derived using the <a href="https://eumap.readthedocs.io/en/latest/">eumap</a> and <a href="https://github.com/openlandmap/scikit-map">scikitmap</a> package in Python . We derived three standard statistics: (1) 10th percentile (p10), median (p50), and 90th percentile (p90).Data Details<ul><li>Time period: December 2021 – November 2023</li><li>Type of data: Methane Volume Mixing Ratio (Unit: ppbv)</li><li>How the data was collected or derived: Derived from 2km Sentinel-5P Menthane using Python running in a local HPC. The time-series analysis were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> and <a href="https://eumap.readthedocs.io/en/latest/">eumap </a>Python package.</li><li>Statistical methods used: percentiles 10, 50, and 90.</li><li>Limitations or exclusions in the data: The dataset is not completed gap-filled. Certain areas have no data in the whole time series</li><li>Coordinate reference system: EPSG:4326</li><li>Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -61.9966697, 180.0000072, 87.37000)</li><li>Spatial resolution: 1/60 d.d. = 0.016666667 (2km)</li><li>Image size: 21,600 x 8,962</li><li>File format: Cloud Optimized Geotiff (COG) format.</li></ul>SupportIf you discover a bug, artifact or inconsistency, or if you have a question please use some of the following channels:<ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">https://gitlab.com/openlandmap/global-layers/-/issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li></ul>Name conventionTo ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:<ol><li>generic variable name: ch4.vmr = methane density methane volume mixing ratio</li><li>variable procedure combination: m.seacov = monthly aggregated and gap filled by seasonal convolution</li><li>Position in the probability distribution / variable type: p10/p50/p90 = 10th/50th/90th percentile</li><li>Spatial support: 2km</li><li>Depth reference: a = above surface</li><li>Time reference begin time: 20211201 = 2021-12-01</li><li>Time reference end time: 20231131 = 2023-11-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230628 = 2023-12-08 (creation date)</li></ol>

opencc-by-4.0Dec 2021View details →

DeepOrchidSeries: A Sentinel-2 Dataset to inform convolutional SDMs with twelve-month Sentinel-2 image time-series, Orchid family

Deep Species Distribution Modelling from Sentinel-2 Image Time-series: a Global Scale Analysis on the Orchid Family  <ul> <li>DeepOrchidSeries dataset gathers Sentinel-2 image time-series around geolocated orchid occurrences. Seasonal evolutions of the habitats are captured in the twelve-month RGB/IR time-series with 640x640m spatial resolution. It allows novel Species Distribution Models (SDMs) coupled with convolutional networks to take advantage of both spatial and temporal information.</li> <li>Our associated article is describing the modeling choices made to shape this ambitious dataset. It is submitted to <a href="https://www.frontiersin.org/research-topics/18336/plant-biodiversity-science-in-the-era-of-artificial-intelligence">https://www.frontiersin.org/research-topics/18336/plant-biodiversity-science-in-the-era-of-artificial-intelligence</a>. We believe such global data, methods and scripts are valuable to the conservation ecology community and especially deep-SDMs users. To our knowledge, no similar ready-to-use dataset is available. In the article, the dataset's temporal dimension is proven to significantly improve SDMs performances.</li> <li>sen2patch is the gitlab project gathering the code to create such dataset. It is available at <a href="https://gitlab.inria.fr/jestopin/sen2patch">https://gitlab.inria.fr/jestopin/sen2patch</a>.</li> <li>DeepOrchidSeries.csv contains all occurrences-level information. <ul> <li>We advice to load it with: <pre><code class="language-python">import pandas as pd df = pd.read_csv("path/to/DeepOrchidSeries.csv", sep=';') df.columns ['gbifid', 'canonical_name', 'decimallatitude', 'decimallongitude', 'speciesKey', 'cell_index', 'bot_country', 'bot_code', 'lvl2_code', 'continent_code']</code></pre> <ul> <li>'gbifid' is the occurrences GBIF ID</li> <li>'canonical_name', is the species canonical name</li> <li>'decimallatitude', 'decimallongitude' are the species coordinates in decimal degrees</li> <li>'speciesKey' is the species GBIF unique identifier</li> <li>'cell_index' is a unique cell ID in a 0.0025° lon/lat grid partitioning the Earth (used to stratify train/val/test set by geographic blocks)</li> <li>'bot_country', 'bot_code', 'lvl2_code', 'continent_code' are geographic subdivisions defined in <a href="https://github.com/tdwg/wgsrpd">https://github.com/tdwg/wgsrpd</a> (code and string for WGSRPD level 1, the botanical countries)</li> </ul> </li> </ul> </li> <li> Initial <a href="https://www.gbif.org/">GBIF</a> query DOI is <a href="http://https://doi.org/10.15468/dl.4bijtu">https://doi.org/10.15468/dl.4bijtu</a> (26 August 2019). </li> <li>DeepOrchidSeries.tar file contains the satellite image time-series and is available at <a href="https://lab.plantnet.org/deeporchidseries/">https://lab.plantnet.org/deeporchidseries/</a> <ul> <li>.tar archive measure 286 GB and extends to 432 GB once decompressed.</li> <li>Image time-series relative tree paths are constructed from the occurrences unique GBIF IDs.</li> <li>For a given occurence gbifid, matching patches are located in: final_dataset_by_gbifid/gbifid[-2:]/gbifid[-4:-2], i.e. in a first folder named with the gbifid last two numbers and a subfolder with the previous two ones. Example: the time-series files matching occurrence 2236837714 are located at final_dataset_by_gbifid/14/77/. </li> <li>Image time-series are composed of twelve 16 bits RGB .png  and twelve 16 bits IR .png files containing data identical to the original L1C products, no lossy compression was made. There are one RGB and one IR .png file per month.</li> <li>Patches from month MM/YYYY of occurrence gbifid are named RGB_YYYY_MM_gbifid_.png and IR0_YYYY_MM_gbifid_.png.</li> </ul> </li> <li>models.zip is the archive containing the four PyTorch models weights described in our article and inception_env.py the used Inception V3 architecture. index.json contains the dictionnary linking the models class indexes from 0 to 14128 with our labels speciesKey: {"class_index":speciesKey}.</li> </ul>   <ul> <li>ACKNOWLEDGMENTS: We warmly thank Alexander Zizka et al. for providing us the geographically and taxonomically curated set of Orchids occurrences. This dataset contains modified Copernicus Sentinel data and Copernicus Service information (2018). Sentinel-2 MSI data used were available at no cost from ESA Sentinels Scientific Data Hub.</li> </ul>

opencc-by-4.0Sep 2017View details →

Wikipedia time-series graph

Wikipedia temporal graph. The dataset is based on two Wikipedia SQL dumps: (1) English language articles and (2) user visit counts per page per hour (aka pagecounts). The original datasets are publicly available on the Wikimedia website. Static graph structure is extracted from English language Wikipedia articles. Redirects are removed. Before building the Wikipedia graph we introduce thresholds on the minimum number of visits per hour and maximum in-degree. We remove the pages that have less than 500 visits per hour at least once during the specified period. Besides, we remove the nodes (pages) with in-degree higher than 8 000 to build a more meaningful initial graph. After cleaning, the graph contains 116 016 nodes (out of total 4 856 639 pages), 6 573 475 edges. The graph can be imported in two ways: (1) using edges.csv and vertices.csv or (2) using enwiki-20150403-graph.gt file that can be opened with open source Python library Graph-Tool. Time-series data contains users' visit counts from 02:00, 23 September 2014 until 23:00, 30 April 2015. The total number of hours is  5278. The data is stored in two formats: CSV and H5. CSV file contains data in the following format [page_id :: count_views :: layer], where layer represents an hour. In H5 file, each layer corresponds to an hour as well.

opencc-by-sa-4.0Oct 2018View details →

Soil organic carbon stock (0–30 cm) in kg/m2 time-series 2001–2015 based on the land cover changes

Estimated SOC loss based on the European Space Agency (ESA) Climate Change Initiative (ESACCI-LC) land cover maps 2001–2015. This only shows estimated SOC loss (in kg/m2) as a result of change in land use / land cover (assuming standard change factors based on the literature and IPCC reports). Methodology produced for the purpose of the Land Degradation Neutrality (UNCCD) project. Processing steps are described in detail <a href="https://gitlab.com/openlandmap/global-layers/tree/master/soil/LDN">here</a>. Antartica is not included. To access and visualize maps use:  <a href="http://www.openlandmap.org/">OpenLandMap.org</a> If you discover a bug, artifact or inconsistency in the maps, or if you have a question please use some of the following channels: <ul> <li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/issues">https://gitlab.com/openlandmap/global-layers/issues</a> </li> <li>General questions and comments: <a href="https://disqus.com/home/forums/landgis/">https://disqus.com/home/forums/landgis/</a></li> </ul> All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention: <ul> <li>sol = theme: soil,</li> <li>organic.carbon.stock = variable: soil organic carbon stock in kg/m2,</li> <li>msa.kgm2 = determination method: derived from carbon content, bulk density and coarse fragments,</li> <li>m = mean value,</li> <li>250m = spatial resolution / block support: 250 m,</li> <li>b0..30cm = vertical reference: standard layer 0-30 cm below surface,</li> <li>2014 = time reference: year 2014,</li> <li>v0.2 = version number: 0.2,</li> </ul>

opencc-by-4.0Jun 2021View details →

18S V4 rDNA sequences organized at the OTU level for the SOMLIT-Astan time-series (2009-2016)

The present file includes metadata for each 18S V4 rDNA OTU from the SOMLIT-Astan time series (2009-2016) including the following fields: amplicon = identifier of the representative (most abundant) sequence; total = total number of reads; spread = number of samples in which the OTU has been found; cloud = number of unique sequences constituting the OTU;  sequence =  nucleic acid sequence of the representative sequence; length = length of the representative sequence; quality = minimum expected error observed for the representative sequence, divided by sequence length;  taxonomy = taxonomic path assigned to the representative sequence; identity = percentage of identity of the representative sequence to the closest reference sequence from PR2; references = best hit reference sequence(s) ;  RA090107_02:RA161222_3 = 375 samples from January 2009 to December 2016, the first two number are the year followed by the month and the day (sampling twice a month during 8 years). Values after “_” indicate the size of the filter used for the filtration: 02 for 0.2 µm and 3 for 3 µm. Generation of 18S V4 rDNA Operational Taxonomic Units (OTUs) from the raw sequencing reads and their assembly into a OTUtable was obtained according to the following pipeline (https://doi.org/10.5281/zenodo.5791089). The V4 region was extracted from the 18S rDNA reference sequences from PR2 v4.12 (Guillou et al., 2013) with Cutadapt. The representative sequences of each OTU were compared to these V4 reference sequences by pairwise global alignment (usearch_global VSEARCH’s command). Each OTU inherits the taxonomy of the best hit or the last common ancestor in case of ties. OTUs with a score below 80% similarity were considered as unassigned (Mahé et al., 2017; Stoeck et al., 2010). The final dataset (filtered OTU table) contains 375 samples (sampled twice per month from 2009 to 2016) with a total of ~30 million sequence reads and 21,418 OTUs.

opencc-by-4.0Apr 2023View details →

SCShores: time-series of shorelines from Spanish Sandy beaches from citizen-science monitoring program.

This repository contains 5 years of sandy beaches shorelines deriverd from a citizen-science monitoring program in the Spanish coast. The methodology and the dataset are described in: González-Villanueva, R., Soriano-González, J., Alejo, I., Criado-Sudau, F., Plomaritis, T., Fernàndez-Mora, À., Benavente, J., Del Río, L., Nombela, M. Á., and Sánchez-García, E.: SCShores: a comprehensive shoreline dataset of Spanish sandy beaches from a citizen-science monitoring programme, Earth System Science Data. V. 15, 4613-4629 , <a href="https://essd.copernicus.org/articles/15/4613/2023/essd-15-4613-2023.html">https://doi.org/10.5194/essd-15-4613-2023</a>, 2023.  The shoreline dataset is provided in 1 GEOJSON file: SCShores.geojson. This dataset covers five sandy beaches located on the Atlantic and Mediterranean coasts of Spain where CoastSnap stations were available, and it includes a total of 1721 shorelines. The coordinate system for the geospatial layer is WGS84. <ul> <li>SCShores.geojson: this layer contains the sandy shorelines . Each feature in this layer is a multipoint with the following attributtes: <ul> <li>site: CoastSnap station name id, e.g. agrelo, samarador, cadiz, ….</li> <li>date: date and time of the shoreline, yyyyy-mm-dd hh:mm:ss</li> <li>timezone: Coordinated Universal Time, UTC</li> <li>timestampQuality: quality flag indicating the confidence in the date-time indicated by the image provider, e.g. 1, 2</li> <li>imageSource: source of the original image from which the shoreline has been derived, e.g. Instagram, Twitter, Facebook, Email, CoastSnapApp</li> <li>elevation_m: same as Z coordinate, defined by the observed tide and the tidal offset, in meters, Tide+tide offset</li> <li>verticalDatum: mean sea level in Alicante, which is considered the zero topographic reference in the Spanish territory, NMMA</li> <li>geometry: type of geometry used in the file, MultiPoint</li> <li>coordinates: Geographic WGS84 coordinates for each point in the geometry, longitude, latitude, Z</li> </ul> </li> </ul>

Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Monthly time-series (2003-2005)

This data is part of the Monthly aggregated Water Vapor MODIS MCD19A2 (1 km) dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.General DescriptionThe monthly aggregated water vapor dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD19A2"><abbr title="MCD19A2 MODIS/Terra+Aqua daily product">MCD19A2 v061</abbr></a>. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:<ul><li>Monthly time-series: Derived from MCD19A2 v061, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the <abbr title="Moving Window Median">TMWM</abbr> algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.</li><li>Yearly time-series: Derived from monthly time-series, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li>Long-term data (2000-2022): Derived from monthly time-series, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul>Data Details<ul><li>Time period: 2003–2005</li><li>Type of data: Water vapor column above the ground (0.001cm)</li><li>How the data was collected or derived: Derived from MCD19A2 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li><li>Statistical methods used: Four statistics were derived: mean, standard deviation, smoothed mean, smoothed standard deviation.</li><li>Limitations or exclusions in the data: The dataset does not include data for Antarctica.</li><li>Coordinate reference system: EPSG:4326</li><li>Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.00081, 179.99994, 87.37000)</li><li>Spatial resolution: 1/120 d.d. = 0.008333333 (1km)</li><li>Image size: 43,200 x 17,924</li><li>File format: Cloud Optimized Geotiff (COG) format.</li></ul>SupportIf you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:<ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li></ul>Name conventionTo ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:<ol><li>generic variable name: wv = Water vapor</li><li>variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm</li><li>Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li><li>Spatial support: 1km</li><li>Depth reference: s = surface</li><li>Time reference begin time: 20030101 = 2003-01-01</li><li>Time reference end time: 20051231 = 2005-12-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230619 = 2023-06-19 (creation date)</li></ol>

Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Monthly time-series (2021-2022)

This data is part of the Monthly aggregated Water Vapor MODIS MCD19A2 (1 km) dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.General DescriptionThe monthly aggregated water vapor dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD19A2"><abbr title="MCD19A2 MODIS/Terra+Aqua daily product">MCD19A2 v061</abbr></a>. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:<ul><li>Monthly time-series: Derived from MCD19A2 v061, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the <abbr title="Moving Window Median">TMWM</abbr> algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.</li><li>Yearly time-series: Derived from monthly time-series, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li>Long-term data (2000-2022): Derived from monthly time-series, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul>Data Details<ul><li>Time period: 2021–2022</li><li>Type of data: Water vapor column above the ground (0.001cm)</li><li>How the data was collected or derived: Derived from MCD19A2 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li><li>Statistical methods used: Four statistics were derived: mean, standard deviation, smoothed mean, smoothed standard deviation.</li><li>Limitations or exclusions in the data: The dataset does not include data for Antarctica.</li><li>Coordinate reference system: EPSG:4326</li><li>Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.00081, 179.99994, 87.37000)</li><li>Spatial resolution: 1/120 d.d. = 0.008333333 (1km)</li><li>Image size: 43,200 x 17,924</li><li>File format: Cloud Optimized Geotiff (COG) format.</li></ul>SupportIf you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:<ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li></ul>Name conventionTo ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:<ol><li>generic variable name: wv = Water vapor</li><li>variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm</li><li>Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li><li>Spatial support: 1km</li><li>Depth reference: s = surface</li><li>Time reference begin time: 20210101 = 2021-01-01</li><li>Time reference end time: 20221231 = 2022-12-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230619 = 2023-06-19 (creation date)</li></ol>

Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Monthly time-series (2015-2017)

This data is part of the Monthly aggregated Water Vapor MODIS MCD19A2 (1 km) dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.General DescriptionThe monthly aggregated water vapor dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD19A2"><abbr title="MCD19A2 MODIS/Terra+Aqua daily product">MCD19A2 v061</abbr></a>. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:<ul><li>Monthly time-series: Derived from MCD19A2 v061, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the <abbr title="Moving Window Median">TMWM</abbr> algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.</li><li>Yearly time-series: Derived from monthly time-series, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li>Long-term data (2000-2022): Derived from monthly time-series, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul>Data Details<ul><li>Time period: 2015–2017</li><li>Type of data: Water vapor column above the ground (0.001cm)</li><li>How the data was collected or derived: Derived from MCD19A2 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li><li>Statistical methods used: Four statistics were derived: mean, standard deviation, smoothed mean, smoothed standard deviation.</li><li>Limitations or exclusions in the data: The dataset does not include data for Antarctica.</li><li>Coordinate reference system: EPSG:4326</li><li>Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.00081, 179.99994, 87.37000)</li><li>Spatial resolution: 1/120 d.d. = 0.008333333 (1km)</li><li>Image size: 43,200 x 17,924</li><li>File format: Cloud Optimized Geotiff (COG) format.</li></ul>SupportIf you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:<ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li></ul>Name conventionTo ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:<ol><li>generic variable name: wv = Water vapor</li><li>variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm</li><li>Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li><li>Spatial support: 1km</li><li>Depth reference: s = surface</li><li>Time reference begin time: 20150101 = 2015-01-01</li><li>Time reference end time: 20171231 = 2017-12-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230619 = 2023-06-19 (creation date)</li></ol>

Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Monthly time-series (2000-2002)

This data is part of the Monthly aggregated Water Vapor MODIS MCD19A2 (1 km) dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.General DescriptionThe monthly aggregated water vapor dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD19A2"><abbr title="MCD19A2 MODIS/Terra+Aqua daily product">MCD19A2 v061</abbr></a>. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:<ul><li>Monthly time-series: Derived from MCD19A2 v061, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the <abbr title="Moving Window Median">TMWM</abbr> algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.</li><li>Yearly time-series: Derived from monthly time-series, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li>Long-term data (2000-2022): Derived from monthly time-series, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul>Data Details<ul><li>Time period: 2000–2002</li><li>Type of data: Water vapor column above the ground (0.001cm)</li><li>How the data was collected or derived: Derived from MCD19A2 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li><li>Statistical methods used: Four statistics were derived: mean, standard deviation, smoothed mean, smoothed standard deviation.</li><li>Limitations or exclusions in the data: The dataset does not include data for Antarctica.</li><li>Coordinate reference system: EPSG:4326</li><li>Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.00081, 179.99994, 87.37000)</li><li>Spatial resolution: 1/120 d.d. = 0.008333333 (1km)</li><li>Image size: 43,200 x 17,924</li><li>File format: Cloud Optimized Geotiff (COG) format.</li></ul>SupportIf you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:<ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li></ul>Name conventionTo ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:<ol><li>generic variable name: wv = Water vapor</li><li>variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm</li><li>Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li><li>Spatial support: 1km</li><li>Depth reference: s = surface</li><li>Time reference begin time: 20000101 = 2000-01-01</li><li>Time reference end time: 20021231 = 2002-12-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230619 = 2023-06-19 (creation date)</li></ol>

Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Monthly time-series (2009-2011)

This data is part of the Monthly aggregated Water Vapor MODIS MCD19A2 (1 km) dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.General DescriptionThe monthly aggregated water vapor dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD19A2"><abbr title="MCD19A2 MODIS/Terra+Aqua daily product">MCD19A2 v061</abbr></a>. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:<ul><li>Monthly time-series: Derived from MCD19A2 v061, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the <abbr title="Moving Window Median">TMWM</abbr> algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.</li><li>Yearly time-series: Derived from monthly time-series, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li>Long-term data (2000-2022): Derived from monthly time-series, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul>Data Details<ul><li>Time period: 2009–2011</li><li>Type of data: Water vapor column above the ground (0.001cm)</li><li>How the data was collected or derived: Derived from MCD19A2 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li><li>Statistical methods used: Four statistics were derived: mean, standard deviation, smoothed mean, smoothed standard deviation.</li><li>Limitations or exclusions in the data: The dataset does not include data for Antarctica.</li><li>Coordinate reference system: EPSG:4326</li><li>Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.00081, 179.99994, 87.37000)</li><li>Spatial resolution: 1/120 d.d. = 0.008333333 (1km)</li><li>Image size: 43,200 x 17,924</li><li>File format: Cloud Optimized Geotiff (COG) format.</li></ul>SupportIf you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:<ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li></ul>Name conventionTo ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:<ol><li>generic variable name: wv = Water vapor</li><li>variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm</li><li>Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li><li>Spatial support: 1km</li><li>Depth reference: s = surface</li><li>Time reference begin time: 20090101 = 2009-01-01</li><li>Time reference end time: 20111231 = 2011-12-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230619 = 2023-06-19 (creation date)</li></ol>

Monthly aggregated Water Vapor MODIS MCD19A2 (1 km): Monthly time-series (2006-2008)

This data is part of the Monthly aggregated Water Vapor MODIS MCD19A2 (1 km) dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.General DescriptionThe monthly aggregated water vapor dataset is derived from <a href="https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD19A2"><abbr title="MCD19A2 MODIS/Terra+Aqua daily product">MCD19A2 v061</abbr></a>. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:<ul><li>Monthly time-series: Derived from MCD19A2 v061, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the <abbr title="Moving Window Median">TMWM</abbr> algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.</li><li>Yearly time-series: Derived from monthly time-series, this data provides a yearly time-series aggregated statistics of the monthly time-series data.</li><li>Long-term data (2000-2022): Derived from monthly time-series, this data provides long-term aggregated statistics for the whole series of monthly observations.</li></ul>Data Details<ul><li>Time period: 2006–2008</li><li>Type of data: Water vapor column above the ground (0.001cm)</li><li>How the data was collected or derived: Derived from MCD19A2 v061 using <a href="https://earthengine.google.com">Google Earth Engine</a>. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li><li>Statistical methods used: Four statistics were derived: mean, standard deviation, smoothed mean, smoothed standard deviation.</li><li>Limitations or exclusions in the data: The dataset does not include data for Antarctica.</li><li>Coordinate reference system: EPSG:4326</li><li>Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.00081, 179.99994, 87.37000)</li><li>Spatial resolution: 1/120 d.d. = 0.008333333 (1km)</li><li>Image size: 43,200 x 17,924</li><li>File format: Cloud Optimized Geotiff (COG) format.</li></ul>SupportIf you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:<ul><li>Technical issues and questions about the code: <a href="https://gitlab.com/openlandmap/global-layers/-/issues">GitLab Issues</a></li><li>General questions and comments: <a href="https://disqus.com/home/forums/landgis">LandGIS Forum</a></li></ul>Name conventionTo ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:<ol><li>generic variable name: wv = Water vapor</li><li>variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm</li><li>Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment</li><li>Spatial support: 1km</li><li>Depth reference: s = surface</li><li>Time reference begin time: 20060101 = 2006-01-01</li><li>Time reference end time: 20081231 = 2008-12-31</li><li>Bounding box: go = global (without Antarctica)</li><li>EPSG code: epsg.4326 = EPSG:4326</li><li>Version code: v20230619 = 2023-06-19 (creation date)</li></ol>