Find research datasets worth reusing

opencc-by-4.0Aug 2019View details →

WASH in households dataset (from the Joint Monitoring Programme database). June 2019

This dataset compromises all country files included in the WHO/UNICEF Joint Monitoring Programme (JMP) global database (<a href="https://washdata.org/data/household">https://washdata.org/data/household</a>, downloaded June 2019). It includes: Country: ISO 3 code + Complete name Service: Water or Sanitation Setting: Urban or Rural Source: Category of the household survey Year: Date of the household survey X1, X2 and X3: percentage of the population using… <ul> <li>In Water: X1 all improved drinking water sources; X2 piped drinking water sources and X3 no drinking water facility (surface water).</li> <li>In Sanitation: X1 all improved sanitation facilities; X2 improved sanitation facilities connected to sewers and X3 no sanitation facilities (open defecation).</li> </ul> The dataset is used in the following paper: Ezbakhe, F. and Pérez-Foguet, A. (2019) Estimating access to drinking water and sanitation: The need to account for uncertainty in trend analysis. Science of the Total Environment. DOI: 10.1016/j.scitotenv.2019.133830 <a href="https://doi.org/10.1016/j.scitotenv.2019.133830">https://doi.org/10.1016/j.scitotenv.2019.133830</a>

opencc-by-4.0Sep 2019View details →

High Tatra Mts. NDWIs collection from 2012 to 2019

Normalized Difference Water Index (NDWI) (signed integer file, scale factor 0.001) of High Tatra Mts. Protected Area (PA).

opencc-by-4.0Sep 2019View details →

HighTatra NDVIs collection from 2012 to 2019

Collection of Normalized Difference Vegetation Index (NDVI) (double data type) of HighTatra Protected Area (PA)

opencc-by-4.0Sep 2019View details →

British English Cued Speech--interspeech 2019

The first British English Cued Speech (CS) dataset is recorded for this work in Cued Speech UK association without using any artificial mark, and it is also the first one especially for the continuous recognition in British English CS. A professional CS interpreter (with no hearing impairment) is asked to simultaneously utter and encode a set of 97 British English sentences (e.g., I feel it is a time to move to a new chapter in my career). 97 sentences are chosen from the uploaded text file. There are totally 907 monophthongs and 138 diphthongs in the dataset. Color video images of the interpreter’s upper body are recorded at 25 fps, with a spatial resolution of 720x1280.  If you use this dataset, please cite the following: <pre>@inproceedings{Liu2019, author={Li Liu and Jianze Li and Gang Feng and Xiao-Ping Zhang}, title={{Automatic Detection of the Temporal Segmentation of Hand Movements in British English Cued Speech}}, year=2019, booktitle={Proc. Interspeech 2019}, pages={2285--2289}, doi={10.21437/Interspeech.2019-2353}, url={http://dx.doi.org/10.21437/Interspeech.2019-2353} }</pre>

opencc-by-4.0Oct 2019View details →

Festival of Frequency Measurement 1 October 2019 for WWV 5 MHz - Toby Haynes VE7CNF

Festival of Frequency Measurement 1 October 2019 for WWV 5 MHz Toby Haynes VE7CNF Location:    49.266918N   122.900887W   Grid CN89ng   Burnaby, BC, Canada Start Time:  2019 Sept 30 at 23:58 UTC End time:    2019  Oct  2 at 00:02 UTC Antenna:     "Inverted L" vertical with ICOM AH-4 auto tuner, 50 ft high, 70 ft horizontal top wire. Receiver:    ICOM IC-7410 with internal TCXO reference and internal USB sound card chip with separate clock crystal.             Receiver in USB mode with carrier frequency 4998.5 kHz to give a 1500 Hz audio tone for the WWV 5 MHz carrier. Measurement:  1500 Hz tone frequency estimated using program FLDigi v4.1.08 in frequency analysis mode. There was a drop in the WWV 5 MHz signal level around 15:00 UTC, after which the reduced SNR caused more variation in the FLDigi frequency measurements. Frequency calibration: Before and after the data set, the receiver frequency error was determined by using the receiver and FLDigi to measure a reference 5 MHz sine wave from a Rigol DG1032 function generator with external 10 MHz GPS-disciplined frequency reference (Trimble). The "VE7CNF raw_2019-09-30-1810 UTC.csv" file includes the reference measurements and the data set. The two reference signal measurements were:    Receiver frequency error +0.066 Hz averaged for 1 minute starting 2019 Sept 30 at 18:10:25 UTC     Receiver frequency error +0.028 Hz averaged for 1 minute starting 2019  Oct  2 at 00:03:10 UTC Receiver frequency error at each time during the data set was then estimated, assuming that the error changed linearly over time betwen the two reference measurements. The estimated receiver frequency errors were applied to correct the data set in "VE7CNF CN89ng 5 MHz_2019-09-30-2358 UTC.csv." The measured WWV 5 MHz frequency error vs time is graphed in "VE7CNF_WWV5MHz_2019-10-01UTC.JPG," with a moving 1-minute average applied to the frequency error. The frequency dropped around 01:30 (local sunset was 01:51 UTC) and increased around 14:00 (local sunrise was 14:13 UTC).

opencc-by-2.0Oct 2019View details →

The Biodata of Legislative Candidates for Indonesian General Election 2019

The dataset of biodata of Legislative Candidates for General Election 2019 is crawled from the Indonesian General Election Committee. We remove privacy information, such as the birth of data and home address. We only store the year of birth and the city of home. The dataset is in CSV file.

opencc-zeroNov 2019View details →

NOAA NCCOS Assessment: Prioritizing Areas for Future Seafloor Mapping, Research, and Exploration Offshore of California, Oregon, and Washington from 2019-03-01 to 2019-04-01

Spatial information about the seafloor is critical for decision-making by marine resource science, management and tribal organizations. Coordinating data needs can help organizations leverage collective resources to meet shared goals. To help enable this coordination, the National Oceanic and Atmospheric Administration (NOAA) National Centers for Coastal Ocean Science (NCCOS) developed a spatial framework, process and online application to identify common data collection priorities for seafloor mapping, sampling and visual surveys offshore of the West Continental United States Coast (WCC). Twenty-six participants from NOAA’s West Coast Deep Sea Coral Initiative (WCDSCI) and Expanding Pacific Research and Exploration of Submerged Systems (EXPRESS) entered their priorities in an online application, using virtual coins to denote their priorities in 10x10 minute grid cells. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important and what data types were needed. Results were analyzed and mapped using statistical techniques to identify significant relationships between priorities, reasons for those priorities and data needs. Ten high priority locations were broadly identified for future mapping, sampling and visual surveys. These locations were distributed throughout the WCC, primarily in depths less than 1,000 m. Participants consistently selected (1) Exploration, (2) Biota/Important Natural Area and (3) Research as their top reasons (i.e., justifications) for prioritizing locations, and (1) Benthic Habitat Map and (2) Bathymetry and Backscatter as their top data or product needs. This ESRI shapefile summarizes the results from this spatial prioritization effort. This information will enable NOAA WCDSCI, EXPRESS and other WCC organization to more efficiently leverage resources and coordinate their mapping of high priority locations along California, Oregon and Washington.  This effort was funded by NOAA’s Deep Sea Coral Research and Technology Program (DSCRTP) through its WCDSCI. The overall goal of the project was to systematically gather and quantify suggestions for seafloor mapping, sampling and visual surveys for the WCDSCI and EXPRESS. The results are expected to help WCDSCI, EXPRESS and other organizations on the WCC to identify locations where their interests overlap with other organizations, to coordinate their data needs and to leverage collective resources to meet shared goals. There were four main steps in the WCC spatial prioritization process. The first step was to identify the technical advisory team, which included the 11 members of the DSCRTP WCDSCI Steering Committee and all of the participants involved in the EXPRESS campaign. This advisory team invited 37 participants for the prioritization. Step two was to develop the spatial framework and an online application. To do this, the WCC was divided into five subregions and 3,265 square grid cells approximately 10x10 minutes in size. Existing relevant spatial datasets (e.g., bathymetry, protected area boundaries, etc.) were compiled to help participants understand information and data gaps and to identify areas they wanted to prioritize for future data collections. These spatial datasets were housed in the online application, which was developed using Esri’s Web AppBuilder. In step three, this online application was used by 26 participants to enter their priorities in each subregion of interest. Participants allocated virtual coins in the 10x10 minute grid cells to denote their priorities. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important and what data types were needed. Coin values were standardized across the subregions and used to identify spatial patterns across the WCC region as a whole. The number of coins were standardized because each subregion had a different number of grid cells and participants. Standardized coin values were analyzed and mapped using statistical techniques, including hierarchical cluster analysis, to identify significant relationships between priorities, reasons for those priorities and data needs. This ESRI shapefile contains the 10x10 minute grid cells used in this prioritization effort and associated the standardized coin values overall, as well as by organization, justification and product. For a complete description of the process and analyses please see: Costa et al. 2019.

Chilean Tomography - CSN version - 2019

This repository contains the velocity models built for the routine processing of the Centro Sismológico Nacional (CSN, Universidad de Chile, <a href="http://www.sismologia.cl" target="_blank" rel="nofollow noreferrer noopener">http://www.sismologia.cl</a>). Contact: <ul> <li>Bertrand Potin, DGF, University of Chile (<a href="mailto:bertrand.potin@uchile.cl">bertrand.potin@uchile.cl</a>)</li> <li>Sergio Barrientos, CSN, University of Chile (<a href="mailto:sbarrien@csn.uchile.cl">sbarrien@csn.uchile.cl</a>)</li> <li>Sergio Ruiz, DGF, University of Chile (<a href="mailto:sruiz@uchile.cl">sruiz@uchile.cl</a>)</li> </ul> <h1>How to cite this material</h1> <h2>Material doi</h2> <a href="https://doi.org/10.5281/zenodo.13146424" target="_blank" rel="nofollow noreferrer noopener">https://doi.org/10.5281/zenodo.13146424</a> <h2>Related article</h2> Potin, B., S. Ruiz, F. Aden-Antoniow, R. Madariaga, and S. Barrientos (2024). A Revised Chilean Seismic Catalog from 1982 to Mid-2020, Seismol. Res. Lett.. <a href="https://doi.org/10.1785/0220240047" target="_blank" rel="nofollow noreferrer noopener">https://doi.org/10.1785/0220240047</a> <h1>Velocity models</h1> 6 velocity models are available, for both P- and S-wave velocities. Model extenion is roughly 8° of latitude with 4° overlap between models. Grid cells are cubes of 4 km. Velocity models are identified by their (rough) latitude range: for example, <code>18_26</code> indicates the model extanding from 18°S to (almost) 26°S. Models are distributed in <code>NonLinLoc</code> format (Lomax, 2000 ; see <a href="http://alomax.free.fr/nlloc/" target="_blank" rel="nofollow noreferrer noopener">http://alomax.free.fr/nlloc/</a> for details and usage).

MOOD_2019_SurfaceWater_PROBA_1k

PROBA V 2019 Permanent and Seasonal water 1km.  Abstract: These layers were extracted for the MOOD extent from the Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe (Marcel Buchhorn, Bruno Smets, Luc Bertels, Bert De Roo, Myroslava Lesiv, Nandin-Erdene Tsendbazar, Martin Herold, & Steffen Fritz. (2020). Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe (V3.0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3939050).  Several kilometre resolution derivatives have been produced from the original 100m resolution dataset for seasonal and permanent water categories:    File naming scheme:   Distance to permanent or seasonal water (erprobapermseaswatdistm1kmll.tif);  Percentage of seasonal water (erprobavLLC2019pcseaswat1km.tif); Percentage of permanent water (erprobavLLC2019pcpermwat1km.tif)   Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: Extent  -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716 Spatial resolution: 0.0083333 deg (approx. 1000 m)   Temporal resolution: seasonal and permament  Pixel values: Meter and Percentage Source:  The Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019 Software used: The software used for map production is ESRI ArcMap 10.8 License: CC-BY-SA 4.0 Processed by: ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project

2001_2019_ERA5_MonthlyPrecipitation_FourierProcessed

This is a set of images produced by temporal Fourier analysis of Monthly precipitation provided by the ERA5 dataset for 2001-2019 from the European Centre for Medium-Range Weather Forecasting. The imagery summarises a key environmental indicator, incorporating seasonal dynamics, for the MOOD study area.  Abstract: Monthly precipitation values were extracted from ERA5 files for the years 2019 through 2020, and then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)  Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to Geotiff format  to give data users more flexibility   File naming scheme:  The ER at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection.    The next two characters identify the channel:  20 for precipitation and 19 refers to the year timeline of 2001-2019.    The last two characters of each file name denote the output from Fourier processing:  a0 - mean  mn - minimum  mx - maximum  a1 - amplitude of annual cycle  a2 - amplitude of bi-annual cycle  a3 - amplitude of tri-annual cycle  p1 - phase of annual cycle  p2 - phase of bi-annual cycle  p3 - phase of tri-annual cycle  d1 - variance in annual cycle  d2 - variance in bi-annual cycle  d3 - variance in tri-annual cycle  da - combined variance in annual, bi-annual, and tri-annual cycles  vr - variance in raw data       Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: Extent  -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716 Spatial resolution: 0.0083333 deg (approx. 1000 m)   Temporal resolution: 2001-2019 Pixel values: Parameter Fourier Variable Image values are   A0, A1, A2, A3, Index Value * 10  ALL D1,D2,D3,Da Percentages  ALL E1,E2,E3 Percentages  ALL P1,P2.P3 Months*100. (Jan=100)   Source:   Monthly Precipitation for ERA5 from the European Centre for Medium-Range Weather Forecasting (ECMWF) Software used: Codes for modelling are in Python and C++ The software used for map production is ESRI ArcMap 10.8 License: CC-BY-SA 4.0 Processed by: ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project

opencc-by-4.0Jul 2021View details →

CLDF dataset derived from Ivani's "Basic Words in Suansu" from 2019

Cite the source of the dataset as: <blockquote> Ivani, J. K. (2019): A first overview on Suansu, a Tibeto-Burman language from Northeastern India. Talk, held at the 29th conference of the Southeast Asian Linguistic Society (27-29 May, Tokyo). https://zenodo.org/record/3383006 </blockquote>

2001_2019_MODIS_Fourier Processed_1k_ER

Overview: This is a set of images produced by Temporal Fourier Analysis of global MODIS data NDVI: Normalised Difference Vegetation Index EVI: Enhanced Vegetation Index MIR: Middle Infra-Red DLST: Day-time Land Surface Temperature NLST: Night-time Land Surface Temperature   Abstract: MODIS is a sensor on board two NASA satellites, providing near-daily coverage of the entire Earth. The MOD11A2 product contains an 8-day average of land surface temperature at 1-kilometre resolution. Reflectance values have been adjusted to remove the distortion caused by the view angle and land surface texture. The MOD13A2 Product used for NDVI, EVI, and Middle Infra-red from USGS. The imagery summarises key environmental indicators, incorporating seasonal dynamics, for the MOOD study area. This is the original version of the 2001-2019 series, which was then processed by a temporal Fourier processing algorithm. A stepwise system of thresholds and interpolations screened erroneous values and bridged gaps in the time series. The smoothed series was sampled at 5-day intervals and transformed into a set of sine curves describing annual, bi-annual, and tri-annual fluctuations. For each of these curves, the Fourier algorithm generated images expressing the amplitude, phase, and variance. Other outputs recorded the mean, minimum, and maximum of the time series, and error measured during the Fourier transform. For a detailed description of the Fourier algorithm and its output, please see the article by Scharlemann et al., 2008 (https://doi.org/10.1371/journal.pone.0001408)  Sea pixels were masked with a MODIS land/sea layer and the images were projected from sinusoidal to geographic. The MOOD study region was a subset of global images. Idrisi rasters were converted to Geotiff format to give data users more flexibility   File naming scheme:  The er at the start of each file name indicates that the image covers the wider Europe and North Africa region included in the MOOD study area and is in geographic projection. 19 refers to the year timeline of 2001-2019.    The next two characters identify the channel:  03 - middle infra-red  07 - daytime land surface temperature  08 - nighttime land surface temperature  14 - NDVI: Normalised Difference Vegetation Index  15 - EVI: Enhanced Vegetation Index    The last two characters of each file name denote the output from Fourier processing:  a0 - mean  mn - minimum  mx - maximum  a1 - amplitude of annual cycle  a2 - amplitude of bi-annual cycle  a3 - amplitude of tri-annual cycle  p1 - phase of annual cycle  p2 - phase of bi-annual cycle  p3 - phase of tri-annual cycle  d1 - variance in annual cycle  d2 - variance in bi-annual cycle  d3 - variance in tri-annual cycle  da - combined variance in annual, bi-annual, and tri-annual cycles  vr - variance in raw data Projection + EPSG code: Latitude-Longitude/WGS84 (EPSG: 4326) Spatial extent: Extent  -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716 Spatial resolution: 0.0083333 deg (approx. 1000 m)   Temporal resolution:  8-day and 16-day  from 2001 to 2019 Pixel values Parameter Fourier Variable Image values are MIR (03) A0, A1, A2, A3, Min, Max, Vr Reflectance values * 10000 LST (07 day,08 night) A0, A1, A2, A3, Min, Max, Vr (Degrees Centigrade+273)*50 NDVI (14) and EVI (15) A0, A1, A2, A3, Index Value * 1000 NDVI (14) and EVI (15) A0, Min, Max, Index Value * 1000 + 10000 NDVI (14) and EVI (15) VR Value * 10000 ALL D1,D2,D3,Da Percentages ALL E1,E2,E3 Percentages ALL P1,P2.P3 Months*100. (Jan=100) Source:  MODIS  NASA :MOD11A2 and MOD13A2 Software used: Codes for modelling are in Python and C++ The software used for map production is ESRI ArcMap 10.8 License: CC-BY-SA 4.0 Processed by: ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project

opencc-zeroAug 2024View details →

EOL Dynamic Hierarchy Trunk (trunk): Dynamic Hierarchy Trunk March 2019

This is the trunk for the EOL reference hierarchy. It determines the relationships among the higher taxa and adds a few taxa that are not covered by other resources. The EOL DH trunk is maintained in [TTT](http://ttt.biodinfo.org/) developed by Colin (Congtian Lin) and Jiangning Wang from Biodiversity Informatics Group of the Institute of Zoology, Chinese Academy of Sciences. ##References Adl, S. M., et al. 2019. Revisions to the classification, nomenclature, and diversity of eukaryotes. Journal of Eukaryotic Microbiology 66, 4–119. https://doi.org/10.1111/jeu.12691 Aguiar, A.P., Deans, A.R., Engel, M.S., Forshage, M., Huber, J.T., Jennings, J.T., Johnson, N.F., Lelej, A.S., Longino, J.T., Lohrmann, V., Mikó, I., Ohl, M., Rasmussen, C., Taeger, A., Yu, D.S.K., 2013. Order Hymenoptera . In : Zhang, Z.-Q. (Ed.) Animal Biodiversity: An Outline of Higher-level Classification and Survey of Taxonomic Richness (Addenda 2013). 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Trends in Microbiology 19, 580–587. https://doi.org/10.1016/j.tim.2011.09.002 Hinchliff, C.E., Smith, S.A., Allman, J.F., Burleigh, J.G., Chaudhary, R., Coghill, L.M., Crandall, K.A., Deng, J., Drew, B.T., Gazis, R., Gude, K., Hibbett, D.S., Katz, L.A., Laughinghouse, H.D., McTavish, E.J., Midford, P.E., Owen, C.L., Ree, R.H., Rees, J.A., Soltis, D.E., Williams, T., Cranston, K.A., 2015. Synthesis of phylogeny and taxonomy into a comprehensive tree of life. PNAS 112, 12764–12769. https://doi.org/10.1073/pnas.1423041112 Holzenthal, R.W., Morse, J.C., Kjer, K.M., 2011. Order Trichoptera Kirby, 1813. In: Zhang, Z.-Q. (Ed.) Animal biodiversity: An outline of higher-level classification and survey of taxonomic richness. Zootaxa 3148, 209. https://doi.org/10.11646/zootaxa.3148.1.40 Hormiga, G., Griswold, C.E., 2014. Systematics, Phylogeny, and Evolution of Orb-Weaving Spiders. Annu. Rev. 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Whole-genome analyses resolve early branches in the tree of life of modern birds. Science 346, 1320–1331. https://doi.org/10.1126/science.1253451 Kathirithamby, J., Engel, M.S., 2014. A Revised Key to the Living and Fossil Families of Strepsiptera, with the Description of a New Family, Cretostylopidae. Journal of the Kansas Entomological Society 87, 385–388. https://doi.org/10.2317/JKES140407.1 Kjer, K.M., Simon, C., Yavorskaya, M., Beutel, R.G., 2016. Progress, pitfalls and parallel universes: a history of insect phylogenetics. Journal of The Royal Society Interface 13, 20160363. https://doi.org/10.1098/rsif.2016.0363 Klopfstein, S., Vilhelmsen, L., Heraty, J.M., Sharkey, M., Ronquist, F., 2013. The Hymenopteran Tree of Life: Evidence from Protein-Coding Genes and Objectively Aligned Ribosomal Data. PLoS ONE 8, e69344. https://doi.org/10.1371/journal.pone.0069344 Kocot, Kevin M., Torsten H. Struck, Julia Merkel, Damien S. Waits, Christiane Todt, Pamela M. Brannock, David A. 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BMC Evolutionary Biology 13, 93. https://doi.org/10.1186/1471-2148-13-93 Robertson, J.A., Ślipiński, A., Moulton, M., Shockley, F.W., Giorgi, A., Lord, N.P., Mckenna, D.D., Tomaszewska, W., Forrester, J., Miller, K.B., Whiting, M.F., Mchugh, J.V., 2015. Phylogeny and classification of Cucujoidea and the recognition of a new superfamily Coccinelloidea (Coleoptera: Cucujiformia): Systematics of Cucujoidea and Coccinelloidea. Systematic Entomology 40, 745–778. https://doi.org/10.1111/syen.12138 Rouse, Greg W., Nerida G. Wilson, Jose I. Carvajal, and Robert C. Vrijenhoek. 2016. New Deep-Sea Species of Xenoturbella and the Position of Xenacoelomorpha. Nature 530(7588):94–97. https://doi.org/10.1038/nature16545. Ruhfel, B.R., Gitzendanner, M.A., Soltis, P.S., Soltis, D.E., Burleigh, J.G., 2014. From algae to angiosperms–inferring the phylogeny of green plants (Viridiplantae) from 360 plastid genomes. BMC Evolutionary Biology 14, 23. https://doi.org/10.1186/1471-2148-14-23 Schiffer, Philipp H., Helen E. Robertson, and Maximilian J. Telford. 2018. Orthonectids Are Highly Degenerate Annelid Worms. Current Biology 28(12):1970-1974.e3. https://doi.org/10.1016/j.cub.2018.04.088. The Angiosperm Phylogeny Group, 2016. An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG IV. Bot. J. Linn. 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The Encyclopedia of Life (EOL, eol.org) aggregates biodiversity information from more than 400 sources and provides access to the data through taxon pages, visual query and application programming interfaces. Scientific names are essential elements of the data integration infrastructure, but their shortcomings as key identifiers are well documented (Patterson et al., 2016). Complex automated workflows and continuous manual curation are required to address idiosyncrasies of source taxonomies, variation in data quality, and conflicting taxonomic opinions. To achieve a harmonized taxonomic view of EOL content, names from data sources are mapped to a dynamic reference hierarchy ([see current version here](https://opendata.eol.org/dataset/tram-807-808-809-810-dh-v1-1/resource/00adb47b-57ed-4f6b-8f66-83bfdb5120e8)) using an algorithm that leverages canonical name strings, hierarchical information (ancestry, descendants), taxonomic ranks, synonym data, and author strings. Names that cannot be associated with a reference taxon are still accessible, but their unmapped status excludes them and any associated content from certain core EOL functions. For more information about the EOL taxonomy, see [EOL Dynamic Hierarchy](https://eol.org/docs/eol-dynamic-hierarchy)

opencc-zeroAug 2024View details →

EOL Dynamic Hierarchy, digest form, January 2019: Dynamic hierarchy digest January 2019

__Digest__ of the EOL dynamic hierarchy prepared by querying the online traits database. Unlike the full DH files, this file provides only the bare minimum information for each taxon: page id, parent page id, canonical name.Zip file containing a README and a three-column CSV (pages.csv) with header row

openodc-odblAug 2024View details →

Global mining deforestation footprint data from 2000 to 2019

The data in this repository is available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/ This repository includes two datasets. The first is a collection of polygons covering mines globally and the associated forest cover loss from 2000 to 2019. The polygons were derived by merging the "global-scale mining polygons version 2" (Maus et al., 2022) and mining and quarry polygon features extracted from the OpenStreetMap database (OpenStreetMap contributors, 2017). To remove double counting of areas the overlaps between the datasets were resolved by uniting intersecting features into single polygon features, i.e. keeping only the external borders of intersecting features. A random visual check was conducted, and a few small manual editing of polygons was performed where errors were identified. The resulting dataset is encoded as a Geopackage in the file 'global_mining_polygons.gpkg'. The GeoPackage includes a single layer with 192,584 entries called 'mining_polygons' with the following attributes: <ul> <li>id<string> unique feature identifier</li> <li>isoa3<string> ISO 3166-1 alpha-3 country codes</li> <li>country<string> country names</li> <li>area<double> area of the polygon in squared kilometres</li> <li>geom<polygon> the geometry of the features in geographical coordinates WGS84</li> </ul> The second dataset provides annual time series of global tree cover loss within mines from 2000 to 2019, covering all polygons in the above dataset. The area of tree cover loss for each polygon was calculated from the Global Forest Change database (Hansen et al., 2013). Each polygon also has additional string attributes with biomes derived from Ecoregions 2017 © Resolve (Dinerstein et al., 2017) and the level of protection derived from The World Database on Protected Areas (UNEP-WCMC and IUCN, 2022). This dataset is encoded in CSV format in the file 'global_mining_forest_loss.csv', which includes 416,412 entries and 53 variables, such that: <ul> <li>id<string> unique feature identifier</li> <li>id_hcluster<string> unique feature identifier</li> <li>list_of_commodities<string> a comma-separated list of commodities</li> <li>isoa3<string> ISO 3166-1 alpha-3 country codes</li> <li>country<string> country names</li> <li>ecoregion<string> ecoregion name</li> <li>biome<string> biome name</li> <li>year<numeric> the year</li> <li>area_forest_loss_XXX_YYY<double> the area of forest cover loss within a polygon per year in squared kilometres.</li> </ul> The values of tree cover loss are disaggregated per initial percentage of tree cover (XXX) and per protection level (YYY). <ul> <li>XXX can take one of: <ul> <li>000: total tree cover loss independently from the initial tree cover</li> <li>025: tree cover loss on pixels with initial tree cover between 0 and 25%</li> <li>050: tree cover loss on pixels with initial tree cover between 25 and 50%</li> <li>075: tree cover loss on pixels with initial tree cover between 50 and 75%</li> <li>100: tree cover loss on pixels with initial tree cover between 75 and 100%</li> </ul> </li> <li>YYY can take one of: <ul> <li>la: tree cover loss within strict nature reserve</li> <li>Ib: tree cover loss within wilderness area</li> <li>II: tree cover loss within national park</li> <li>III: tree cover loss within natural monument or feature</li> <li>IV: tree cover loss within habitat/species management area</li> <li>V: tree cover loss within protected landscape/seascape</li> <li>VI: tree cover loss within PA with sustainable use of natural resources</li> <li>p: tree cover loss within any type of protection, including not applicable, not assigned, or not reported</li> <li>none: when YYY is omitted, total tree cover loss within the polygon</li> </ul> </li> </ul> For details about the protection levels definition see the UNEP-WCMC and IUCN (2022). The id can be used to link polygons to forest loss data.

opencc-by-4.0Feb 2024View details →

CLDF dataset derived from Heggarty, Paul & Anderson, Cormac & Scarborough, Matthew's "Indo-European Cognate Relationships database" ([IE-CoR version 1.0](https://github.com/lexibank/iecor/releases/tag/v1.0)) from 2019

Cite the source of the dataset as: <blockquote> Heggarty, Paul & Anderson, Cormac & Scarborough, Matthew 2024. Indo-European Cognate Relationships database (IE-CoR version 1.1). Leipzig: Max Planck Institute for Evolutionary Anthropology </blockquote>

Water Body Checklists 2019: Yellow Sea Species List

Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the Yellow Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.

Water Body Checklists 2019: Tyrrhenian Sea Species List

Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the Tyrrhenian Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.

Water Body Checklists 2019: Tasman Sea Species List

Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the Tasman Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.

Water Body Checklists 2019: Sulu Sea Species List

Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.List of species collected from the Sulu Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.