Find research datasets worth reusing

June 2002 bin-averaged CTD profiles for the Georgia Coastal Ecosystems Sapelo River transect

Three hydrographic surveys were performed on June 21, 2002, along a transect from Sapelo Sound up the Sapelo River to Eulonia, Georgia (Sapelo River Transect, GCE-SP). Vertical CTD profiles were collected at approximately 2km intervals from 0km to 36km during low and high tidal regimes. Conductivity, temperature, pressure and optical backscatter were measured, and depth, salinity and sigma-t were calculated for each profile. Data values collected on the upcast were deleted, and the remaining data were averaged within 0.5m depth bins and interpolated to produce a smooth profile for contouring. This data set was collected as part of the Georgia Coastal Ecosystems LTER quarterly hydrographic monitoring program.

June 2002 CTD, PAR, oxygen and chlorophyll profiles for the Georgia Coastal Ecosystems Doboy Sound transect

Two hydrographic surveys were performed on June 19, 2002, along an east-to-west transect through Doboy Sound near Sapelo Island, Georgia (Doboy Sound Transect, GCE-DB). Vertical CTD profiles were collected at approximately 2km intervals from -2km to 10km along the transect during low tide and high tide conditions. Conductivity, temperature, pressure, optical back scatter, photosynthetically-available radiation, oxygen and chlorophyll a fluorescence were measured, and depth, salinity and sigma-t were calculated for each profile. Data values collected during the upcast were deleted. This data set was collected as part of the Georgia Coastal Ecosystems LTER quarterly hydrographic monitoring program.

June 2002 CTD, PAR, oxygen and chlorophyll profiles for the Georgia Coastal Ecosystems Altamaha River transect

Four hydrographic surveys were performed on June 20, 2002, along an east-to-west transect up the Altamaha River in Georgia (Altamaha River Transect, GCE-AL). Vertical CTD profiles were collected at 1-2km intervals from the line of demarcation (station +00) to 28km upriver along the transect during low and high tidal regimes. Conductivity, temperature, pressure, optical back scatter, photosynthetically-available radiation, oxygen and chlorophyll a fluorescence were measured, and depth, salinity and sigma-t were calculated for each profile. Data values collected during the upcast were deleted. This data set was collected as part of the Georgia Coastal Ecosystems LTER quarterly hydrographic monitoring program.

June 2002 CTD, PAR, oxygen and chlorophyll profiles for the Georgia Coastal Ecosystems Sapelo River transect

Three hydrographic surveys were performed on June 21, 2002, along a transect from Sapelo Sound up the Sapelo River to Eulonia, Georgia (Sapelo River Transect, GCE-SP). Vertical CTD profiles were collected at approximately 2km intervals from 0km to 36km during low and high tidal regimes. Conductivity, temperature, pressure, optical back scatter, photosynthetically-available radiation, oxygen and chlorophyll a fluorescence were measured, and depth, salinity and sigma-t were calculated for each profile. Data values collected during the upcast were deleted. This data set was collected as part of the Georgia Coastal Ecosystems LTER quarterly hydrographic monitoring program.

Surface water DIC, total alkalinity, and pH for the June 2001 Georgia Coastal Ecosystems LTER oceanographic survey

Surface water samples for total dissolved inorganic carbon (DIC), total alkalinity (TAlk), and pH were collected from the Altamaha River, Doboy Sound, Sapelo River and the Duplin River (anchor station near Marsh Landing) during June 26-28, 2001. DIC was measured using a custom automated DIC analyzer. Total alkalinity was determined by Gran titration. pH of surface water at stations was measured on board using a glass electrode. This study was part of the GCE oceanographic monitoring program, and will be repeated periodically.

June 2001 surface water bacterial productivity at ten Georgia Coastal Ecosystems LTER sampling sites

Surface water samples were collected during low tide survays near ten Georgia Coastal Ecosystem LTER sampling sites in June, 2001. The incorporation of tritiated leucine in unfiltered samples during one hour incubations was measured using a standard microcentrifuge method to estimate bacterial productivity in each sample. This study was part of the GCE-LTER hydrographic monitoring program, and will be repeated quarterly.

opencc-by-sa-4.0Jul 2014View details →

Numbers and shares of Open Access Journals in Sociology using Creative Commons Licenses, June 2014

a) The data for the year 2014 was retrieved as a CSV-file (doaj_2014-05-07_1330_utf8.csv) from the Directory of Open Access Journals (DOAJ) homepage at 2014-06-08. b) A subset of journals assigned to the subject category Sociology was generated (n=109). c) I manually checked the information on CC-licenses for each of the 109 journals d) Where necessary I added correct information on licenses, see column k in the CSV-file for the updated information. Column l marks entries that were updated.

Final Results from the RDM Survey - LEARN project (June 2017)

Data obtained from the open survey developed by the LEARN project (http://www.learn-rdm.eu/) as a self-assessment tool to assist institutions discover how ready they are for managing research data. This dataset replaces the previous ones published at http://doi.org/10.5281/zenodo.61903 and http://doi.org/10.5281/zenodo.290635. The survey is based on the issues posed to institutions by the LERU Roadmap for Research Data published at the end of 2013, and available at: http://www.learn-rdm.eu/material/leru_roadmap_for_research_data The survey has thirteen questions addressing the main elements to be taken into account in developing an institutional strategy for research data management. Each question has three possible answers representing green, yellow or red light. The more ‘green light’ responses recorded, the readier an institution probably is for managing its research data. The survey is available in English at http://learn-rdm.eu/en/rdm-readiness-survey/ and in Spanish at http://learn-rdm.eu/encuesta-rdm/

Landslides from Space - Amyntaio Lignite Coal Mine Landslide (10th June 2017)

On Saturday, 10th of June 2017 a massive landslide occurred in a lignite pit in Amyntaio, Greece. It buried 25 million tons lignite worth about 500 Million Euro and caused the permanent evacuation of Anargyroi, a village nearby. The pre-event acquisition is from 1st June 2017 (Sentinel-2) and the post-event acquisition is from 24th June 2017 (Sentinel-2). A false colour composite with near-infrared, red and green band is visualised as RGB image. Contains modified Copernicus Sentinel data (2017)

Landslides from Space - Nuugaatsiaq: landslide-induced tsunami, Greenland (17th June 2017)

On 17th June 2017 the village of Nuugaatsiaq was struck by a large, isolated tsunami. This Tsunami was caused by a landslide on a nearby cliff. The pre-event acquisition is from 2nd June 2017 (Sentinel-2) and the post-event acquisition is from 19th June 2017 (Sentinel-2). A false colour composite with near-infrared, red and green band is visualised as RGB image. Contains modified Copernicus Sentinel data (2017)

Landslides from Space - Glacier Bay Landslide, Alaska USA (28th June 2016)

On 28th June 2016 seismometer recorded an event with a magnitude of 5.2. Later it was visually confirmed that this was caused by a landslide and not an earthquake. The pre-event acquisition is from 5th February 2016 (Sentinel-2) and the post-event acquisition is from 29th September 2016 (Sentinel-2). Contains modified Copernicus Sentinel data (2016)

opencc-zeroJun 2024View details →

World Flora Online Plant List June 2025

The consensus taxonomy of plants used as the backbone for the <a href="https://www.worldfloraonline.org/">World Flora Online</a> (WFO) portal, and issued as editions of the <a href="https://wfoplantlist.org/">WFO Plant List</a>. New versions of this checklist are released every six months in June and December: this is release 2025-06. The history of data development for the WFO taxonomic backbone is given on the WFO Plant List <a href="https://wfoplantlist.org/background">background page</a>. Taxonomic names are incorporated into WFO from nomenclators <a href="https://www.ipni.org/">International Plant Name Index</a> (IPNI) for vascular plants, and <a href="https://www.tropicos.org/home">Tropicos</a> for bryophytes. Taxonomic and nomenclatural updates are incorporated from the WFO's <a href="https://about.worldfloraonline.org/tens">Taxonomic Expert Networks</a> (TENs) and the <a href="https://powo.science.kew.org/about-wcvp">World Checklist of Vascular Plants</a> (WCVP), facilitated by the Royal Botanic Gardens, Kew. This data repository includes the following files: <ul> <li>wfo_plantlist_2025-06.zip The Catalogue of Life Data Package of the WFO Plant List. This is the most expressive standards based form of the list.</li> <li>plant_list_2025-06.json.gz JSON formatted version of the WFO Plant List. This has been designed for direct import into a schemaless instance of a SOLR index and is used to drive the WFO Plant List API (<a href="https://list.worldfloraonline.org">https://list.worldfloraonline.org</a>) which in turn drives the WFO Plant List in the portal. This is recommended if you want a local, read only version of the list rather than use the API.</li> <li>plant_list_2025-06.sql.gz This is the complete production database (minus API keys) as a MySQL backup file. It can be restored directly to a MySQL 8.0 or later instance if you require the list in SQL format.</li> <li>ipni_to_wfo.csv.gz A file mapping all the IPNI IDs we track to their associated WFO IDs.</li> <li>families_dwc.tar.gz Individual Darwin Core Archive files for each of 733 recognized families. If you want a single family in DwC but can't load the whole list download and expand this file. Family and genus files are also available for download through the portal. These files exclude deprecated names.</li> <li>_DwC_backbone_R.zip A single Darwin Core Archive file containing non deprecated names and taxa for use in the existing R package.</li> <li>_uber.zip A single Darwin Core Archive file containing all names and taxa even those that are deprecated along with some extra columns</li> </ul>

openodc-byDec 2024View details →

Public opinion poll "War, Peace, Victory and the Future" – National face-to-face opinion poll representative of the population in government-controlled territories of Ukraine on the war-related issues (June 2023)

The face-to-face survey was conducted by the Ilko Kucheriv Democratic Initiatives Foundation in cooperation with the Centre for Political Sociology from 5 to 15 June 2023. A total of 2,001 respondents aged 18 or older took part in the survey in Vinnytsia, Volyn, Dnipropetrovsk, Zhytomyr, Zakarpattia, Zaporizhzhia, Ivano-Frankivsk, Kyiv, Kirovohrad, Lviv, Mykolaiiv, Odesa, Poltava, Rivne, Sumy, Ternopil, Kharkiv, Kherson, Khmelnytskyi, Cherkasy, Chernihiv, and Chernivtsi regions, and the city of Kyiv (in Zaporizhzhia, Kharkiv, and Kherson regions – only in the territories controlled by Ukraine and not affected by hostilities). The sampling technique used in the survey is multi-stage, with a random selection of localities in the first stage and a quota-based selection of respondents in the final stage. The random selection is representative of the demographic structure of the adult population in the areas covered by the survey at the beginning of 2022. The maximum sampling error shall not exceed 2.3%. At the same time, it is necessary to take into account systematic deviations in the sample caused by the forced migration of millions of citizens due to the Russian-Ukrainian war. COMPOSITION OF MACRO-REGIONS: West – Volyn, Zakarpattia, Ivano-Frankivsk, Lviv, Rivne, Ternopil, and Chernivtsi regions; Center – Vinnytsia, Zhytomyr, Kyiv, Kirovohrad, Poltava, Sumy, Khmelnytskyi, Cherkasy, and Chernihiv regions, and the city of Kyiv; South – Zaporizhzhia, Mykolaiiv, Kherson, and Odesa regions; East – Dnipropetrovsk and Kharkiv regions. This dataset contains the original survey data. The SPSS file (.sav) is the original file. It has been exported to an Excel file. The content of the corresponding XLSX file should be identical to the original SAV file. The SAV file contains the questions and answer options of the original questionnaire in Ukrainian. The original questionnaire and an English translation have also been included in this data collection as separate PDF files. In addition, the dataset includes a file of "selected findings", which documents some of the key findings of the survey in the form of analytical summaries and descriptive statistics. The report was prepared by the civil society organisation OPORA.

opencc-by-4.0Jun 2024View details →

Data for UV Plasmon-Enhanced Chiroptical Spectroscopy of Membrane-Binding Proteins, June 2024

Extinction spectra of arrays of aluminum nanoparticles with diameters between 40 - 100 nm. Circular dichroism spectra of Tol-BINAP films on Al nanoparticle arrays before and after annealing of the films. Electromagnetic simulations of phase, electric (Eenh) field and magnetic (Henh) field enhancements as well as optical chirality density (Cenh) enhancement around flat aluminum hexagonal pyramid at specified wavelength. The simulations were performed with FDTD using Ansys Lumerical.

opencc-by-4.0Jun 2019View details →

Radiocarbon Palaeolithic Europe Database v25 June 2019 extract

The database, version 25 (first version was available in2002), contains now 13093 site forms, (most of them with their geographical coordinates), comprising 15500 radiometric data:  Conv. 14C  and AMS 14C (12500 items), TL (1023  items), OSL (627  items), ESR, Th/U and AAR (2015 items)  from the European (Russian Siberia included) Lower, Middle and Upper Palaeolithic. All 14C dates are conventional dates BP. 263 new sites are incorporated and 447 sites have a corrected or an updated content. The latest version is available at https://ees.kuleuven.be/en/geography/projects/14c-palaeolithic/.

opencc-by-4.0Aug 2019View details →

Child mortality dataset (from the UN Inter-agency Group for Child Mortality Estimation database). June 2019

This dataset compromises all country data included in the UN Inter-agency Group for Child Mortality Estimation (IGME) database (<a href="https://childmortality.org/data">https://childmortality.org/data</a>, downloaded June 2019). It includes: Reference area: name of the country Indicator: child mortality indicator (neonatal mortality, infant mortality, under-5 mortality and mortality rate age 5 to 14) Sex: sex of the child (male, female and total) Series name: name of survey/census/VR [note: UN IGME estimates, i.e. not source data, are identified as "UN IGME estimate" in this field] Series year: year of survey/census/VR series Observation value: value of indicator from survey/census/VR Observation status: indicates whether the data point is included  or excluded for estimation [status of "normal" indicates UN IGME estimate, i.e. not source data] Series Category: category of survey/census/VR, and can be: <ul> <li>DHS [Demographic and Health Survey]</li> <li>MIS [Malaria Indicator Survey]</li> <li>AIS [AIDS Indicator Survey]</li> <li>Interim DHS</li> <li>Special DHS</li> <li>NDHS [National DHS]</li> <li>WFS [World Fertility Survey]</li> <li>MICS [Multiple Indicator Cluster Survey]</li> <li>NMICS [National MICS]</li> <li>RHS [Reproductive Health Survey]</li> <li>PAP [Pan Arab Project for Child or Pan Arab Project for Family Health or Gulf Famly Health Survey]</li> <li>LSMS [Living Standard Measurement Survey]</li> <li>Panel [Dual record, multiround/follow-up survey and longitudinal/panel survey]</li> <li>Census</li> <li>VR [Vital Registration]</li> <li>SVR [Sample Vital Registration]</li> <li>Others [e.g. Life Tables]</li> </ul> Series type: the type of calculation method used to derive the indicator value (direct, indirect, household deaths, life table and vital records) Standard error: sampling standard error of the observation value Series method: data collection method, and can be: <ul> <li>Survey/census with Full Birth Histories</li> <li>Survey/census with Summary Birth Histories</li> <li>Survey/census with Household death</li> <li>Vital Registration</li> <li>Other</li> </ul> Lower and upper bound: the lower and upper bounds of 90% uncertainty interval of UN IGME estimates (for estimates only, i.e., not source data). The dataset is used in the following paper: Ezbakhe, F. and Pérez-Foguet, A. (2019) Levels and trends in child mortality: a compositional approach. Demographic Research (Under Review)

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-4.0Mar 2019View details →

RDF Linked Data representation of GC-MS data from the 'Rose Genome' article published in Nature genetics, June, 2018

This dataset corresponds to the RDF Linked Data representation of the measurements of 61 known metabolites (all annotated with resolvable CHEBI identifiers and InChi strings), measured by gas chromatography mass-spectrometry (GC-MS) in 6 different Rose cultivars (all annotated with resolvable NCBITaxonomy Identifiers) and 3 organism parts (all annotated with resolvable Plant Ontology identifiers). The quantitation types are annotated with resolvable <a href="https://github.com/ISA-tools/stato">STATO</a> terms. Most of the semantics resources belong to the <a href="http://obofoundry.org">OBO foundry</a>. The transformation to RDF was performed on a Frictionless Tabular Data Package (<a href="https://frictionlessdata.io/specs/tabular-data-package/">https://frictionlessdata.io/specs/tabular-data-package/)</a>, holding the data extracted from a supplementary material table, available from <a href="https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip">https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0110-3/MediaObjects/41588_2018_110_MOESM3_ESM.zip</a>  and published alongside the Nature Genetics manuscript identified by the following doi: <a href="https://doi.org/10.1038/s41588-018-0110-3">https://doi.org/10.1038/s41588-018-0110-3</a>, published in June 2018. This supplementary material table was deposited to Zenodo and is identified by the following doi: <a href="https://doi.org/10.5281/zenodo.2598799">https://doi.org/10.5281/zenodo.2598799</a> This dataset is used to demonstrate how to make data Findable, Accessible, Discoverable and Interoperable (FAIR) and how Frictionless Tabular Data Package representations can be easily mobilised for reanalysis and data science. It is associated to the following project: <a href="https://github.com/proccaserra/rose2018ng-notebook">https://github.com/proccaserra/rose2018ng-notebook</a> with all the necessary information, executable code and tutorials in the form of Jupyter notebooks.

opencc-zeroAug 2024View details →

EOL Dynamic Hierarchy Trunk (trunk): EOL Dynamic Hierarchy Trunk 12 June 2017

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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ZooKeys 88, 1–972. https://doi.org/10.3897/zookeys.88.807 Cannon, Johanna Taylor, Bruno Cossermelli Vellutini, Julian Smith, Fredrik Ronquist, Ulf Jondelius, and Andreas Hejnol. 2016. Xenacoelomorpha Is the Sister Group to Nephrozoa. Nature 530(7588):89–93. https://doi.org/10.1038/nature16520. Davis, R.B., Baldauf, S.L., Mayhew, P.J., 2010. The origins of species richness in the Hymenoptera: insights from a family-level supertree. BMC Evolutionary Biology 10, 109. https://doi.org/10.1186/1471-2148-10-109 Dunlop, J. A., Penney, D. & Jekel, D. 2015. A summary list of fossil spiders and their relatives. In World Spider Catalog. Natural History Museum Bern, online at http://wsc.nmbe.ch Dunn, C.W., Giribet, G., Edgecombe, G.D., Hejnol, A., 2014. Animal Phylogeny and Its Evolutionary Implications. Annu. Rev. Ecol. Evol. Syst. 45, 371–395. https://doi.org/10.1146/annurev-ecolsys-120213-091627 Foottit, R. G., Adler, P. H., eds. 2017. Insect Biodiversity: Science and Society, Volume 1 & 2. 2nd Edition. Wiley-Blackwell. Fritz, U., Havaš, P., 2013. Order Testudines: 2013 update. In : Zhang, Z.-Q. (Ed.) Animal Biodiversity: An Outline of Higher-level Classification and Survey of Taxonomic Richness (Addenda 2013). Zootaxa 3703, 12–14. https://doi.org/10.11646/zootaxa.3703.1.4 Giribet, Gonzalo. 2016. New Animal Phylogeny: Future Challenges for Animal Phylogeny in the Age of Phylogenomics. Organisms Diversity & Evolution 16 (2):419–26. https://doi.org/10.1007/s13127-015-0236-4. Giribet, Gonzalo, and Gregory D. Edgecombe. 2020. The Invertebrate Tree of Life. Princeton, United States: Princeton University Press, 2020. Guy, L., Ettema, T.J.G., 2011. The archaeal __TACK__ superphylum and the origin of eukaryotes. 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. Entomol. 59, 487–512. https://doi.org/10.1146/annurev-ento-011613-162046 James, S.W., Davidson, S.K., 2012. Molecular phylogeny of earthworms (Annelida:Crassiclitellata) based on 28S, 18S and 16S gene sequences. 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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)