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Data associated with the following publication: Developing the Playground Play Value and Usability Audit Tool (PVUA): An Evaluation of Content Validity via an Expert Panel
<p>This data set contains the supporting data associated with the following publication:</p> <p>Morgenthaler, T., Loebach, J., Lynch, H., Pentland, D., Kottorp, A., & Schulze, C. (in press). Developing the Playground Play Value and Usability Audit Tool (PVUA): An Evaluation of Content Validity via an Expert Panel. Children, Youth and Environments. [DOI was not yet available when the data set was published]</p> <p>The data set includes the following files:</p> <ul> <li>read me file [contains all relevant information to understand and reuse this data set] </li> <li>13 additional files [for description, see read me file]</li> </ul> <p>For more information, please contact the lead researcher, Thomas Morgenthaler (tom.morgenthaler@gmail.com or 121101888@umail.ucc.ie)</p> <p> </p>
EUNIS-ESy: Expert system for automatic classification of European vegetation plots to EUNIS habitats
<p><strong>EUNIS-ESy</strong> is an expert system for automatic classification of European vegetation plots to habitat types of the EUNIS Habitat Classification. The EUNIS classification and the principles of the expert system are described by <a href="https://doi.org/10.1111/avsc.12519">Chytrý et al. (2020)</a>. The classification of a set of vegetation plots can be run using the JUICE program (<a href="https://doi.org/10.1111/j.1654-1103.2002.tb02069.x">Tichý 2002</a>; <a href="https://www.sci.muni.cz/botany/juice/">https://www.sci.muni.cz/botany/juice/</a>), TURBOVEG 3 program (Hennekens 2015) and an R script (<a href="https://doi.org/10.1111/avsc.12562">Bruelheide et al. 2021</a>).</p> <p>This dataset contains two parts: (1) the expert system and related files necessary for running it; (2) characterization of EUNIS habitats based on the results of the expert system classification.</p> <p><strong>1. Expert system and related files necessary to run it</strong></p> <p>1.1. <strong>EUNIS-ESy-2025-10-03.txt </strong>– a file containing the script for the classification of vegetation plots by EUNIS-ESy. This version contains tested definitions for the revised EUNIS classification of vegetated Marine (MA), Coastal (N), Wetland (Q), Grassland (R), Shrubland (S), Forest (T), Inland sparsely vegetated (U) and Man-made (V). It also contains tested definitions of Aquatic plant communities (P3) and Springs (P2N). This file is different from the analogous file in the previous versions.</p> <p>1.2. <strong>Nomenclature-translation-from-Turboveg-2-databases.zip </strong>– an archive containing the scripts for automatic translation of taxon concepts and names used in individual European Turboveg 2 databases (<a href="https://doi.org/10.2307/3237010">Hennekens & Schaminée 2001</a>; <a href="https://www.synbiosys.alterra.nl/turboveg/">https://www.synbiosys.alterra.nl/turboveg/</a>) to the nomenclature that can be used as an input for EUNIS-ESy. This file is the same as in the previous versions.</p> <p>1.3. <strong>EUNIS-ESy-User-Guide.pdf </strong>– a brief user guide to the classification of vegetation plots by EUNIS-ESy using the JUICE program. Please read this guide carefully before running the expert system to avoid misclassifications. This file is the same as in the previous versions.</p> <p><strong>2. Characterization of the EUNIS habitats based on the results of the EUNIS-ESy classification</strong></p> <p>2.1. <strong>EUNIS-habitats-2025-10-03.xlsx </strong>– the current list of EUNIS habitats. This file is different from the analogous file in the previous versions.</p> <p>2.2. <strong>EUNIS-EuroVegChecklist-crosswalk-2025-10-03.xlsx</strong> – a crosswalk between the EUNIS habitat classification and phytosociological alliances of EuroVegChecklist (<a href="http://doi.org/10.1111/avsc.12257">Mucina et al. 2016</a>; <a href="https://floraveg.eu/vegetation/">https://floraveg.eu/vegetation/</a>).</p> <p>2.3. <strong>EUNIS-habitats-Characteristic-species-combintation-2025-10-03.xlsx </strong>– a database of habitats' characteristic species combinations in a spreadsheet format. These species combinations are based on the analysis of vegetation plots from the European Vegetation Archive (EVA; <a href="https://doi.org/10.1111/avsc.12191">Chytrý et al. 2016</a>; <a href="http://euroveg.org/eva-database">http://euroveg.org/eva-database</a>) and other databases classified by EUNIS-ESy v2025-10-03. Analytical methods are described in <a href="https://doi.org/10.1111/avsc.12519">Chytrý et al. (2020)</a>. This file is different from the analogous file in the previous versions.</p> <p>2.4. <strong>EUNIS-habitats-Distribution-maps-2025-10-03.xlsx </strong>– a set of distribution maps in the TIFF format based on the analysis of vegetation plots from the European Vegetation Archive (EVA; <a href="https://doi.org/10.1111/avsc.12191">Chytrý et al. 2016</a>; <a href="http://euroveg.org/eva-database">http://euroveg.org/eva-database</a>) and other databases classified by EUNIS-ESy v2025-10-03.</p> <p>2.5. <strong>Data-sources-EUNIS-classification-2025-10-03.pdf </strong>– a list of data sources used to produce the distribution maps and characteristic species combinations.</p> <p>-----------------------------------------------------------------------------------------------------</p> <p><strong>Differences from the previous version (2021-06-01)</strong></p> <p>Aquatic plant communities (P3), spring (P2N), some wetland (Q61-Q63) and some inland sparsely vegetated (U71-U72) habitats were added to the EUNIS-ESy expert system. Plant taxon concepts and nomenclature were extensively revised. Some previously included habitat definitions were slightly refined. New vegetation-plot records added to the EVA database by 8 August 2025 were used to characterize habitat types. Unlike in the previous version, this version does not provide Habitat factsheets because summarized information about each habitat is now available in the FloraVeg.EU database at <a href="https://floraveg.eu/habitat/">https://floraveg.eu/habitat/</a>.</p> <p>-----------------------------------------------------------------------------------------------------</p> <p> </p> <p><strong>Recommended citation of this version of the EUNIS-ESy expert system</strong></p> <p>Chytrý et al. (2020), version 2025-10-03</p> <p>Chytrý M., Tichý L., Hennekens S.M., Knollová I., Janssen J.A.M., Rodwell J.S., Peterka T., Marcenò C., Landucci F., Danihelka J., Hájek M., Dengler J., Novák P., Zukal D., Jiménez-Alfaro B., Mucina L., Abdulhak S., Aćić S., Agrillo E., Attorre F., Bergmeier E., Biurrun I., Boch S., Bölöni J., Bonari G., Braslavskaya T., Bruelheide H., Campos J.A., Čarni A., Casella L., Ćuk M., Ćušterevska R., De Bie E., Delbosc P., Demina O., Didukh Y., Dítě D., Dziuba T., Ewald J., Gavilán R.G., Gégout J.-C., Giusso del Galdo G.P., Golub V., Goncharova N., Goral F., Graf U., Indreica A., Isermann M., Jandt U., Jansen F., Jansen J., Jašková A., Jiroušek M., Kącki Z., Kalníková V., Kavgacı A., Khanina L., Korolyuk A.Yu., Kozhevnikova M., Kuzemko A., Küzmič F., Kuznetsov O.L., Laiviņš M., Lavrinenko I., Lavrinenko O., Lebedeva M., Lososová Z., Lysenko T., Maciejewski L., Mardari C., Marinšek A., Napreenko M.G., Onyshchenko V., Pérez-Haase A., Pielech R., Prokhorov V., Rašomavičius V., Rodríguez Rojo M.P., Rūsiņa S., Schrautzer J., Šibík J., Šilc U., Škvorc Ž., Smagin V.A., Stančić Z., Stanisci A., Tikhonova E., Tonteri T., Uogintas D., Valachovič M., Vassilev K., Vynokurov D., Willner W., Yamalov S., Evans D., Palitzsch Lund M., Spyropoulou R., Tryfon E., Schaminée J.H.J. (2020) EUNIS Habitat Classification: expert system, characteristic species combinations and distribution maps of European habitats. Applied Vegetation Science, 23, 648–675. https://doi.org/10.1111/avsc.12519</p>
CzechVeg-ESy: Expert system for automatic classification of vegetation plots from the Czech Republic
<p><strong>Expertní systém pro automatickou klasifikaci fytocenologických snímků z České republiky </strong><br> [popis a instrukce v češtině jsou uvedeny níže]</p> <p>***************************************************************************************************************</p> <p><strong>CzechVeg-ESy</strong> is an expert system for automatic classification of vegetation plots from the Czech Republic to the vegetation types defined in the monograph <em>Vegetation of the Czech Republic</em> (<a href="https://www.sci.muni.cz/botany/vegsci/vegetace.php?page=monograph&lang=en">Chytrý 2007-2013</a>). It is delivered in two main versions: The <strong>main version 1 (v1) </strong>is the original version used for the vegetation classification in that monograph, described in detail in its English Summary (<a href="https://www.sci.muni.cz/botany/chytry/Vegetation-Czech-Rep-Summary.pdf">Chytrý 2007</a>). Its subversions (indicated by dates) contains corrections of minor errors and species nomenclature. This version only classifies vegetation to the phytosociological associations. The <strong>main version 2 (v2)</strong> uses the same classification system as accepted in the <em>Vegetation of the Czech Republic</em>, but includes more advanced functions to provide more accurate classification. Moreover, it is hierarchical, performing classification not only to associations but also to alliances and classes (for the plots not classified at lower levels).</p> <p>Each version is delivered in two variants. The <strong>basic variant </strong>(CzechVeg-ESy-basic-v1.txt) assigns vegetation plots to associations following their formal definitions created using the Cocktail method (<a href="https://doi.org/10.2307/3236796">Bruelheide 2000</a>) modified by <a href="https://www.sci.muni.cz/botany/chytry/Koci_etal2003_JVS.pdf">Kočí et al (2003)</a>. These definitions are based on the presence of sociological species groups and the dominance of selected species. The expert system evaluates individual vegetation plots and assigns them to the associations. A classification process is considerably faster when the basic variant is used, as opposed to the full variant. The <strong>full variant</strong> (file CzechVeg-ESy-full-v1.txt) performs the same functions as the basic variant, but in addition, it can also assign the plots not classified by formal definitions based on their numerical similarity to the plots that fulfil the requirements of the formal definitions. Most of such plots can be considered as untypical from the phytosociological point of view, i.e. with poor correspondence to any of the defined vegetation types, in most cases because of the lack of ecologically specialized species. The method of similarity-based assignment is the Frequency-Positive Fidelity Index (FPFI) described in <a href="https://www.sci.muni.cz/botany/chytry/Koci_etal2003_JVS.pdf">Kočí et al. (2003)</a> and <a href="https://doi.org/10.1007/s11258-004-5798-8">Tichý (2005)</a>.</p> <p><strong>Instructions for using CzechVeg-ESy version 1</strong></p> <ol> <li>Vegetation plots have to be stored in the TURBOVEG 2 program (<a href="https://www.synbiosys.alterra.nl/turboveg/">https://www.synbiosys.alterra.nl/turboveg/</a>) with the species list Czechia-Slovakia-2015 (contained in the file TurbovegSlBackup_Czechia_slovakia_2015.zip), which is largely compatible with the older species lists called Czechia-Slovakia-2012 and Central-Europe.</li> <li>Export plots from TURBOVEG 2 to a CC! file (Export / Other formats / JUICE input files) and import this file to the JUICE program (<a href="https://www.sci.muni.cz/botany/juice/">https://www.sci.muni.cz/botany/juice/</a>) using the species list in the file Checklist-Danihelka-et-al-2012-ver-2019-07-06.txt, which converts the plant nomenclature to correspond with the Checklist of vascular plants of the Czech Republic (<a href="http://www.preslia.cz/P123Danihelka.pdf">Danihelka et al. 2012</a>).</li> <li>Select Analysis / Expert system in the JUICE program.</li> <li>Upload the expert system file (either CzechVeg-ESy-basic-v1.txt or CzechVeg-ESy-full-v1.txt) by pressing Load ES File button.</li> <li>Modify species nomenclature by pressing the Modify Species Names button.</li> <li>If there are juvenile species in the herb layer in the plots to be analysed, delete them by pressing Delete Juveniles button.</li> <li>In some cases, narrow species concepts were changed to broader concepts, resulting in repetitions of the same names in the table. These must be merged using the Merge Same Spec. Names button. This will also merge records of the same species in different vegetation layers because the expert system assumes that each species name is contained only once in the same plot.</li> <li>When using the full version of the expert system, a threshold similarity value for similarity-based assignment must be specified in the field in the bottom right part of the form. The higher the value, the fewer plots will be assigned, while only those plots will be assigned that have a high similarity to the association. If the threshold value is set to 0, all the plots will be assigned, but some of them may be very dissimilar to the associations which they are assigned to.</li> <li>Run expert system using the Classify Relevé button (a plot marked by a previous mouse click will be classified) or Classify [colour] Relevés button (all the plots of the selected colour will be assigned).</li> <li>If a single plot is classified, species groups present in this plot and the assignment of the plot to an association will be shown. If the plot is not classified, no association will be listed. Occasionally, a plot can be assigned to more than one association. If the full version of the expert system is used, the table will contain a list of associations ranked by decreasing similarity to the plot.</li> <li>If multiple plots are classified, association codes will appear in the header of those plots that were assigned based on the formal definitions. The legends for the codes can be found in a printed version of the <em>Vegetation of the Czech Republic</em>, in its online version (<a href="https://pladias.cz/en/vegetation/">https://pladias.cz/en/vegetation/</a>) or in the expert system txt file. Plots not assigned to any association will be marked with ? and those assigned to more than one association will be marked with +. If the full version of the expert system is used, the output will contain the most similar associations for those plots which have remained unassigned to associations or were assigned to more than one association.</li> <li>Using the formal definition, the expert system normally assigns some plots of a single vegetation stand to a certain association while other plots of the same stand remain unassigned. This means that the stand consists of patches with species composition typical of the given association and patches with a less typical species composition. If different plots from a single relatively homogeneous stand are assigned to different associations, it is appropriate to interpret the stand as transitional between these associations.</li> </ol> <p>This electronic publication of CzechVeg-ESy was supported by the Czech Science Foundation (grant no. 17-15168S).</p> <p>***************************************************************************************************************</p> <p><strong>CzechVeg-ESy</strong> je expertní systém pro automatickou klasifikaci fytocenologických snímků z České republiky do vegetačních typů definovaných v monografii <em>Vegetace České republiky </em>(<a href="https://www.sci.muni.cz/botany/vegsci/vegetace.php?lang=en&page=monograph">Chytrý 2007-2013</a>). Existují dvě hlavní verze tohoto expertního systému: <strong>Hlavní verze 1 (v1) </strong>je originální verze použitá pro klasifikaci v této národní vegetační monografii, která je podrobně popsaná v její metodické kapitole. Její dílčí verze (označené datem) obsahují opravy drobných chyb a nomenklatury druhů. Tato verze klasifikuje fytocenologické snímky pouze do fytocenologických asociací. <strong>Hlavní verze 2 (v2)</strong> používá stejný klasifikační systém, jaký byl použit ve Vegetaci České republiky, ale používá pokročilejší funkce umožňující přesnější klasifikaci. Tato verze také provádí hierarchickou klasifikaci nejen do asociací, ale také do svazů a tříd (pro snímky nezařazené do nižších jednotek).</p> <p>Každá verze má dvě varianty. <strong>Základní varianta </strong>(soubor CzechVeg-ESy-basic-v1.txt) přiřazuje fytocenologické snímky do asociací na základě jejich formálních definic vytvořených metodou Cocktail (<a href="https://doi.org/10.2307/3236796">Bruelheide 2000</a>) v úpravě podle práce <a href="https://www.sci.muni.cz/botany/chytry/Koci_etal2003_JVS.pdf">Kočí et al (2003)</a>. Tyto definice jsou založeny na prezenci sociologických skupin druhů a dominanci vybraných druhů. Expertní systém vyhodnocuje každý jednotlivý fytocenologický snímek v datovém souboru a řadí jej do asociace. Klasifikace pomocí základní varianty je výrazně rychlejší než klasifikace pomocí plné varianty. <strong>Plná varianta </strong>(soubor CzechVeg-ESy-full-v1.txt) zajišťuje stejné funkce jako základní varianta, ale navíc klasifikuje i snímky neklasifikované formálními definicemi, a to na základě jejich numerické podobnosti ke skupinám snímků, které vyhovují podmínkám formálních definic asociací. Většinu takových snímků lze považovat z fytocenologického hlediska za netypické porosty, tj. takové, které plně neodpovídají definovaným vegetačním typům, zpravidla kvůli absenci ekologicky specializovaných druhů. Klasifikace na základě podobnosti se počítá pomocí indexu FPFI (Frequency-Positive Fidelity Index), který definovali <a href="https://www.sci.muni.cz/botany/chytry/Koci_etal2003_JVS.pdf">Kočí et al. (2003)</a> a <a href="https://doi.org/10.1007/s11258-004-5798-8">Tichý (2005)</a>.</p> <ol> <li>Fytocenologické snímky určené k analýze musí být uloženy v databázi v programu TURBOVEG 2 (<a href="https://www.synbiosys.alterra.nl/turboveg/">https://www.synbiosys.alterra.nl/turboveg/</a>) s druhovým seznamem Czechia-Slovakia-2015 (soubor TurbovegSlBackup_Czechia_slovakia_2015.zip), který je kompatibilní s druhovými seznamy Czechia-Slovakia-2012 a Central-Europe.</li> <li>Snímky se exportují z programu TURBOVEG 2 do souboru CC! (Export / Other formats / JUICE input files) a tento soubor se importuje do programu JUICE (<a href="https://www.sci.muni.cz/botany/juice/">https://www.sci.muni.cz/botany/juice/</a>) s použitím druhového seznamu v souboru Checklist-Danihelka-et-al-2012-ver-2019-07-06.txt, čímž se nomenklatura konvertuje do nomenklatury odpovídající Seznamu cévnatých rostlin květeny České republiky (<a href="http://www.preslia.cz/P123Danihelka.pdf">Danihelka et al. 2012</a>).</li> <li>V programu JUICE se zvolí menu Analysis / Expert system.</li> <li>Tlačítkem Load ES File se nahraje do paměti soubor s příslušným expertním systémem (buď CzechVeg-ESy-basic-v1.txt, nebo CzechVeg-ESy-full-v1.txt).</li> <li>Tlačítkem Modify Species Names se upraví nomenklatura druhů tak, aby odpovídala nomenklatuře používané expertním systémem.</li> <li>Obsahují-li snímky určené k analýze juvenilní dřeviny v bylinném patru, je potřeba je vymazat tlačítkem Delete Juveniles.</li> <li>Při převodu nomenklatury se v některých případech převedlo užší pojetí druhů na širší, čímž vznikly v tabulce druhové údaje vedené pod stejnými jmény. Ty je potřeba sloučit tlačítkem Merge Same Spec. Names. Přitom se sloučí i údaje stejného druhu v různých patrech, protože expertní systém předpokládá jen jeden výskyt stejného druhového jména v jednom snímku.</li> <li>Pokud je používána plná (Full) verze expertního systému, je potřeba v okénku vpravo dole nastavit prahovou hodnotu podobnosti pro přiřazování snímků k asociacím pomocí podobnosti. Čím vyšší hodnota, tím méně snímků se přiřadí, ale přiřadí se ty, které se dané asociaci více podobají. Při hodnotě 0 se přiřadí všechny snímky, ale některé budou dané asociaci velmi nepodobné.</li> <li>Spustí se běh expertního systému, a to buď tlačítkem Classify Relevé (bude se klasifikovat jeden snímek, na který se předtím kliklo myší) nebo Classify [colour] Relevés (budou se klasifikovat všechny snímky vybrané barvy).</li> <li>Při klasifikaci jednoho snímku se zobrazí v tabulce druhové skupiny, jejich zastoupení v daném snímku a asociace, do které byl snímek přiřazen pomocí formální definice. Pokud přiřazen nebyl, nezobrazí se žádná asociace. Snímek může být přiřazen i do více než jedné asociace. Při použití plné verze expertního systému se do tabulky vypíší asociace v pořadí klesající podobnosti ke snímku, a to u těch snímků, které nebyly přiřazeny do žádné asociace nebo byly přiřazeny do více než jedné asociace.</li> <li>Při klasifikaci více snímků se do záhlaví tabulky vepíší kódy asociací u těch snímků, které se přiřadily na základě formálních definic. Převod kódů na jména asociací lze dohledat v tištěné verzi <em>Vegetace České republiky</em>, v její online verzi (<a href="https://pladias.cz/en/vegetation/">https://pladias.cz/vegetation/</a>) nebo v textovém souboru expertního systému. U snímků, které se nepřiřadily k žádné asociaci, se v záhlaví zobrazí znak ?. U snímků přiřazených do více než jedné asociace se zobrazí znak +. Při použití plné verze expertního systému se do tabulky vypíší nejpodobnější asociace u těch snímků, které nebyly přiřazeny do žádné asociace nebo byly přiřazeny do více než jedné asociace.</li> <li>Expertní systém běžně přiřazuje pomocí formálních definic některé snímky v porostu nebo lokálně rozlišovaném rostlinném společenstvu do určité asociace a jiné do žádné asociace, což znamená, že se porost skládá z míst s druhovým složením typickým pro danou asociaci a míst s méně typickým druhovým složením. Pokud expertní systém přiřadí různé snímky z jednoho relativně homogenního porostu k různým asociacím, je vhodné porost interpretovat jako přechodný mezi těmito asociacemi.</li> </ol> <p>Tato elektronická publikace expertního systému CzechVeg-ESy byla podpořena Grantovou agenturou České republiky (grant 17-15168S).</p>
A map selection of wigeon stopover sites (core areas) based on wetland expert knowledge
<p>Stopover areas (core areas only) along the migration route of wigeons tracked with GPS transmitters were selected when they exhibited forests on more than 50% of their total surface or had less than 50% cover by water and/or wetland on the ESA’s global land cover map. We created a sample of 5,630 regions of interest (3,403 for training and 2,227 for validation), delineated with polygons assigned to land classes listed in the Table 1. We used archives of Google Earth, ESRI, and BING satellites for the photointerpretation of the land classes as described in Table 1. The classification was performed with a Sentinel-2 MultiSpectral Instrument, Level-2A image collection in Google Earth Engine (GEE) through the R-package Rgee to create a batch process applying the GEE Random forest classifier to each selected core home range. The cloudless (maximum 3%) images were selected within the period from 01/06/2021 to 30/09/2021. The optimal number of trees was estimated at 100 for an out of bag error of 14%. The overall accuracy on the validation sample was 82 %. </p>
Expert judgements for evaluating deduplication of OpenAIRE Research Graph
<p>Expert judgments used to evaluate the deduplication algorithm used for constructing the OpenAIRE Research Graph.</p> <p>Each expert assigned each group with one of the following predetermined classes (that also indicate whether contained entities are equivalent or not):</p> <ul> <li>AMBIGUOUS: At least one DOIs is invalid (no metadata are available) (N/A)</li> <li>DELETED-DUPLICATES: DOIs once pointing to the same research object, currently deleted. (TRUE)</li> <li>MULTI-PUBLISHED: Article published in more than one locations (full or abstract) (TRUE)</li> <li>VERSIONS: Multiple versions of the same research object (e.g. pre-prints, post-prints etc). (TRUE)</li> <li>ERRONEOUS: Unrelated set of objects. (FALSE)</li> <li>PAPER-EXTENSIONS: Extended version of a conference paper in a journal. (FALSE)</li> <li>PART-OF-A-GROUP: Multiple parts of the same research object (e.g. multi-part publication, photos of the same collection etc). (FALSE)</li> <li>SUPPLEMENTARY: A publication and its supplementary material (including errata). (FALSE)</li> </ul> <p>The following are provided:</p> <ul> <li>OpenAIRE identifier</li> <li>Judgement</li> <li>Count of numbers in group</li> <li>DOIs in group</li> </ul>
IPBES Data Management Tutorials - Session 2.2: Why a data management policy for IPBES and how it concerns experts
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data and knowledge management policy. They cover topics ranging from data and knowledge management policy, reports, active research data, tools, and examples.</p> <p>The <em>IPBES data management policy </em>chapter provides an introduction of the IPBES data and knowledge management policy. It discusses why IPBES has a data and knowledge management policy and who is responsible for what in the implementation and further development of this policy.</p> <p>This session, <em>Why a data management policy for IPBES and how it concerns experts, </em>provides background on what IPBES wants to achieve with its data and knowledge management policy and how it impacts experts within IPBES. The session now has a supplement which covers the development of the policy from version 1.0 to 2.0. </p> <p>Please note that the IPBES data and knowledge management policy is the second version of the IPBES data management policy.</p>
OpenAIRE and FAIR Data Expert Group survey about Horizon 2020 template for Data Management Plans
<p>This dataset is published in 2017 by the OpenAIRE project and the FAIR Data Expert Group.</p> <p>It contains two survey data files, two pdf-files summarising the results in a report and an infographic, and a Readme.txt file.</p> <p>The OpenAIRE project supports the open science ambitions of the European Commission. The project and in particular the Research Data Management team provide support, training and information on the Open Research Data Pilot. In this context, a survey was carried out to collect feedback on the Horizon 2020 template for Data Management Plans (DMPs). The team collaborated with the FAIR data expert group, which is providing recommendations to the European Commission on turning FAIR data into reality. One of the specific tasks of the Expert Group is contributing to an evaluation of the Horizon 2020 approach to DMPs, including future revisions of the template and the development of additional sector/ discipline-specific guidance. The aim of the survey was to collect experiences of researchers and DMP reviewers with the DMP template and guidelines on FAIR data management in Horizon 2020. The survey assesses the usefulness of the guidelines and any aspects that are confusing and unclear to determine what improvements can be made.</p> <p>Feedback was sought from both researchers and research support staff. The survey was initially scheduled to run from 22 May to 21 June 2017. Several organisations were asked to help announce the survey, including OpenAIRE’s National Open Access Desks, the FAIR data expert group, FOSTER, LIBER, and the RDA Interest Group on Active DMPs. When the first survey responses showed only a small share of researchers, more stakeholders were contacted to specifically target this community. The European Research Area was approached, whose project officers circulated the survey call among award holders of EC projects. Early-career researchers were also informed through the YEAR network and EURODOC. This resulted in an extension of the survey to 21 July 2017.</p> <p>At the close of the survey on 21 July 2017, a total number of 289 responses were reached. 50% of the respondents indicated that they were researchers, and 60% that they were (also) research support staff. OpenAIRE and the FAIR data expert group are very pleased with this balanced outcome and would like to thank all colleagues and organisations who promoted the survey, as well as everyone who took part in it.</p> <p> </p>
ELWar Expert Survey Trend File
<p>This repository contains the combined data from the 2018, 2020, and 2022 ELWar expert surveys on parties’ political ideology, policy positions, and behavior in six countries in South-East Europe: Bosnia and Herzegovina, Croatia, Kosovo, North Macedonia, Montenegro, and Serbia. The survey is part of the project “Electoral Legacies of War: Political Competition in Postwar Southeast Europe" (ELWar).</p>
Biodiversity expert responses to survey questions
Despite substantial progress in understanding global biodiversity loss, major taxonomic and geographic knowledge gaps remain. Decisionmakers often rely on expert judgement to fill knowledge gaps, but are rarely able to engage with sufficiently large and diverse groups of experts. To improve understanding of the perspectives of thousands of biodiversity experts worldwide, we conducted a survey and asked experts to focus on the taxa and freshwater, terrestrial, or marine ecosystem they know best. We identified biodiversity experts as corresponding authors of papers published on the topic of biodiversity in scientific journals indexed in the Web of Science over the decade from January 2010 to December 2019. Focusing on the taxa and ecosystems they know best, these experts estimated past and future global biodiversity loss, which was defined in the survey as the percentage of species that are globally threatened or extinct. Experts also ranked the direct and indirect drivers of global biodiversity loss and estimated its impacts on ecosystem functioning and nature's contributions to people. We received 3,331 responses from biodiversity experts who live in 113 countries and who research biodiversity in nearly all (187) countries, including all major habitats in freshwater, terrestrial, and marine ecosystems. The data provided here are the 3,331 responses (rows) to survey questions (columns).
Challenges to freedom of speech and journalists in Ukraine in times of war – Non-representative online expert survey of Ukrainian journalists (January 2023)
The expert survey of journalists was conducted from 18 to 27 January 2023 using a self-completion questionnaire in Google Forms. The survey was conducted by the Ilko Kucheriv Democratic Initiatives Foundation on the request of the Human Rights Centre ZMINA with the support of Freedom House Ukraine. A total of 132 people participated in the survey. The respondents were selected using the method of voluntary selection and snowballing to the point of saturation. The sample represents only the opinion of the respondents, but it also allows us to talk about certain trends and common assessments of certain phenomena and processes in the journalistic field. The survey includes questions about freedom of speech and self-censorship in the media environment during the Russian-Ukrainian war. The data collection contains original survey data. The Excel file (.xlsx) is the original file with the respondents' answers in Ukrainian, provided by the Ilko Kucheriv Democratic Initiatives Foundation. The documentation includes the questions and answer options of the original questionnaire in Ukrainian and English. Additionally, the data collection contains the "Summary" file, which is an analytical report prepared by the Ilko Kucheriv Democratic Initiatives Foundation and the Human Rights Centre ZMINA. The report uses data from an expert survey of journalists in 2019 and 2023, and the results of focus groups in 2022.
Nursing Assignment Help | Best Writing Service By Experts
<p>Students enrolled in nursing degree and diploma programs at Australian universities find writing nursing assignments challenging and seek <a href="https://www.globalassignmenthelp.com.au/nursing-assignment-help">Nursing assignment writing services</a> in Australia.</p>
PhasAGE Expert Seminar- Dissecting the contribution of motor-cargo adaptors to microtubule-based transport in neurons
<p>The PhasAGE Expert Seminars consist of a series of talks with speakers from PhasAGE partner’s institutions to promote a successful transfer of knowledge about PhasAGE topics – biomolecular phase separation, aging and age-related diseases.</p>
Identifying Source Code File Experts
<p>This replication package contains the data which allows to reproduce and extend the results presented in the paper study.</p>
PhasAGE Expert Seminar- Intrinsic Protein Disorder and Conditional Folding in AlphaFoldDB
<p>The PhasAGE <strong>Expert Seminars</strong> consist of a series of talks with speakers from PhasAGE partner’s institutions to promote a successful transfer of knowledge about PhasAGE topics – biomolecular phase separation, aging and age-related diseases.</p>
EU Demographic Change Experts
<p>Network data on demographic experts in the European, coded from work team participation and event attendance. The data has been anonymized.</p>
Feedback from experts on participatory governance
<p>Feedback from of nine experts, conducted in the context of task 2.5 of the PANTHEON project, to assess the current practices on participatory governance and the techniques and experiences in how to involving civilian in risk management. The respondents are with public organizations, associations, first responders and researchers involved in risk management and research projects dealing with emergency response. (Contact: Simon.Bittner@johanniter.at)</p> <div></div>
PhasAGE Expert Seminar- Inhibition of age-associated genomic instability: emerging strategy to delay cellular senescence and aging
<p>The PhasAGE <strong>Expert Seminars </strong>consist of a series of talks with speakers from PhasAGE partner’s institutions to promote a successful transfer of knowledge about PhasAGE topics – biomolecular phase separation, aging and age-related diseases.</p>
Global knowledge and use of soil biodiversity: Results of an expert survey
<p>A global survey on soil biodiversity (see file Global Biodiversity Survey.pdf provided as an attachment) was conducted over a three-week period in March 2022 by the Global Soil Partnership (GSP) of the Food and Agriculture Organization (FAO) of the United Nations, as part of the activities of the International Network on Soil Biodiversity (NETSOB). The survey intended to obtain information on the current status of knowledge and use of soil organisms worldwide, i.e., to identify who is doing what, where, and how, as well as the main gaps, pitfalls, and opportunities across existing national initiatives and research.</p> <p>The survey included 122 questions that characterized the work undertaken by experts regarding microbes, fauna, and their activity in soils, community & functional assessments, inventories, mapping and monitoring activities, ecosystem services, applications, and threats to soil biodiversity, education, and communication activities, as well as public policies related to soil biodiversity. The online survey was created using the software Survey Monkey v. 11 and was sent out to over 70 thousand e-mail addresses with a link to complete the survey. </p> <p>Over 2,600 responses were received, representing >1,350 institutions from 135 countries, mainly from experts active in research and academia. The number of respondents was not equal for all questions, as the survey guided the respondents to different parts, depending on their replies.</p> <p>The 122 questions and the replies of the respondents are presented as separate tabs in the attached Excel file (Results survey for Zenodo.xlsx). The respondents and their identities, as well as their e-mails and any personal websites were removed in the current file to maintain anonymity. Institutional websites were maintained as long as they did not identify the respondent(s) directly. </p> <p>A detailed written description of the survey results was prepared as a manuscript for a special issue of the journal Soil Organisms, volume 97 (Brown et al., 2025). The survey was prepared by a team of scientists from the Brazilian Corporation for Agricultural Research (Embrapa) and collaborating institutions, with assistance from the board of the International Network on Soil Biodiversity (NETSOB), and with funding provided by the FAO. The work was further supported by the Fundação de Apoio a Pesquisa e Desenvolvimento Agropecuário Edmundo Gastal (FAPEG), Brazil, a grant of CNPq (Processo No. 312824/2022-0) to GGB, and of the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant program (# 05901–2019) to ZL, who was also supported by Western University.</p>
Pre-Publication Dataset: Is Remote Sensing a Better Measure of Internet Censorship than Expert Analysis? Analyzing Tradeoffs for International Donors and Advocacy Organizations
<p>These are the underlying data and do file to support the analysis in the forthcoming paper "Is Remote Sensing a Better Measure of Internet Censorship than Expert Analysis? Analyzing Tradeoffs for International Donors and Advocacy Organizations" that has been submitted to the <em>Data & Policy </em>Journal. This is an expanded and updated version of the Data For Policy conference paper "Comparing Measures of Internet Censorship: Analyzing the Tradeoffs between Expert Analysis and Remote Measurement" (10.5281/zenodo.3967398).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.