Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
179
datasets available to search
ShareScore release 0.9.0
Dataset results
179 results for “Classification systems”
WorldSeasons: a seasonal classification system interpolating biomes within the year for improved temporal aggregation
<p>We present a seasonal classification system to improve the temporal framing of comparative scientific analysis. Research often uses yearly aggregates to understand inherently seasonal phenomena like harvests, monsoons, and droughts. This obscures important trends across time and differences through space by including redundant data. Our classification system allows for a more targeted approach. We split global land into four principal climate zones: desert, arctic and high montane, tropical, and temperate. A cluster analysis with zone-specific variables and weighting splits each month of the year into discrete seasons based on the monthly climate. We expect the data will be able to answer global comparative analysis questions like: are global winters less icy than before? Are wildfires more frequent now in the dry season? How severe are monsoon season flooding events? This is a natural extension of the historical concept of biomes, made possible by recent advances in climate data availability and artificial intelligence.</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>
System transferability of a Raman-based oesophageal tissue classification
<p>This dataset contains 560819 Raman spectra (Uncorrected_MedianFiltered_SizeMatched_SMART_x) taken from 61 oesophageal samples (sampleID) representing 51 patients (patientID). The data was acquired across three independent sites (centre) using the same make of spectrometer - Renishaw RA816 Biological Analyser (Renishaw plc, Wotton-under-edge, UK). Ostensibly the same sample was collected by all three sites (three adjacent FFPE tissue slices were obtained and regions of interest were identified by a histopathologist on one of the centres). The samples belong to one of 5 pathology classes: NSQ (0), IM(1), LGD(2), HGD(3) and AC(4). </p> <p>Included in this version is the protocol used to collect this data, particularly focused on the aquisition of Raman spectra.</p>
Data of the article Analysis of the self-archiving policies of journals in the highest rank category of the Finnish journal classification system within computer science, physics and electronic engineering
<p>The publication forum level three journals representing the three fields of science of computer science, computer science and electrical engineering were identified by utilizing the MinEdu field search filter while searching for the top-ranked journals from the publication channel search (https://www.tsv.fi/julkaisufoorumi/haku.php?lang=en), which is based on Field of Science, Statistics Finland classification (https://www.stat.fi/meta/luokitukset/tieteenala/001-2010/index_en.html). The data were extracted during august 2017 consists of total of 127 individual journals. It is worth noting that circa 30 journals were classified into more than one fields of sciences under scrutiny. First, the journals were divided into representing gold and hybrid model journals. Second, green open access policies of the identified hybrid journals were analyzed using Laakso’s (2014) publisher policy coding framework. Also publishers of the individual journals were identified and subsequently added to the data.</p> <p>NOTE! The data includes the shortest embargo to either institutional or subject repositories. For example, Elsevier had no embargo to opening accepted manuscripts from arXiv subject repository and thus no embargoes to Elsevier's journals are included within this datasheet.</p> <p>Data is in CSV. format</p> <p> </p> <p> </p>
GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery
<p>A novel global 30-m land-cover product with a fine classification system for the year 2015 (GLC_FCS30-2015). The product was produced by combining time-series of Landsat imagery and high-quality training data from the GSPECLib (Global Spatial Temporal Spectra Library) on the Google Earth Engine computing platform. First, the global training data from the GSPECLib were developed by applying a series of rigorous filters to the MCD43A4 NBAR and CCI_LC land-cover products. Secondly, a local adaptive random forest model was built for each 5°×5° geographical tile by using the multi-temporal Landsat spectral and textures features of the corresponding training data, and the GLC_FCS30-2015 land-cover product containing 30 land-cover types was generated for each tile.</p>
Automated Classification of Conversation Valence and Arousal using Autonomic Nervous System Responses
<p>This repository contains the supplementary file for our study "Automated Classification of Conversation Valence and Arousal using Autonomic Nervous System Responses". The MS Excel file contains all physiological features (individual features and synchrony features) for all valid dyads and all intervals together with self-report ratings of the conversation (Self-Assessment Manikin) and personality trait data (CES-D, BFNES, QCAE). Synchrony features were calculated using code from a previous Zenodo submission (https://zenodo.org/record/7140829).</p>
BgMA-ESy: Expert system for automatic classification of vegetation plots of subalpine tall-herb vegetation (class Mulgedio-Aconitetea) from Bulgaria
<p>*****</p> <p>BgMA-ESy is an expert system that classifies vegetation plots of the class <em>Mulgedio-Aconitetea</em> (<a href="https://doi.org/10.1111/avsc.12257">Mucina et al. 2016</a>) occurring in Bulgaria. The expert system 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>).</p> <p>The aggregation of vascular plants included within the BgMA-ESy is adopted from EUNIS-ESy (<a href="https://doi.org/10.1111/avsc.12519">Chytrý et al. 2020</a>; <a href="https://doi.org/10.5281/zenodo.4812736">https://doi.org/10.5281/zenodo.4812736</a>), and in a few cases, it is adjusted.</p> <p>*****</p> <p><strong>Specifications</strong></p> <p>The analyzed data (vegetation plots) cannot: </p> <ul> <li>include scrub vegetation (cover of tall shrub species > 8%; e.g., <em>Pinus mugo</em>, <em>Salix </em>spp.).</li> <li>contain tree species with cover > 1% (e.g., <em>Fagus sylvatica</em>, <em>Picea abies</em>).</li> <li>contain <em>Pteridium aquilinum </em>as a dominant species.</li> </ul> <p>The expert system was trained on vegetation plots with 5–100 m<sup>2</sup> area that occur above 1000 m a. s. l.</p> <p>* Exceptions from EUNIS-ESy aggregation:</p> <p>Heracleum sphondylium agg. does not include H. sphondylium subsp. verticillatum.</p> <p> </p> <p>*****</p> <p>When using this work, please cite:</p> <p>Szokala D., Kočí M. & Vassilev K. (2024): Subalpine tall-herb vegetation in Bulgaria: diversity and ecology. – Plant Biosystems 158: 490–510. <a href="https://doi.org/10.1080/11263504.2024.2327865">https://doi.org/10.1080/11263504.2024.2327865</a>.</p> <p>*****</p>
Mediterranean land system classification on 2005 and 2015
<p>This shapefile represent the classification of the existing Mediterranean land systems on 2005 and 2015</p>
Broad-Coverage German Sentiment Classification Model and Dataset for Dialog Systems
<p><a href="http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.202.pdf"><strong>Training a Broad-Coverage German Sentiment Classification Model for Dialog Systems</strong></a></p> <p>This paper describes the training of a general-purpose German sentiment classification model. Sentiment classification is an important aspect of general text analytics. Furthermore, it plays a vital role in dialogue systems and voice interfaces that depend on the ability of the system to pick up and understand emotional signals from user utterances. The presented study outlines how we have collected a new German sentiment corpus and then combined this corpus with existing resources to train a broad-coverage German sentiment model. The resulting data set contains 5.4 million labelled samples. We have used the data to train both, a simple convolutional and a transformer-based classification model and compared the results achieved on various training configurations. The model and the data set will be published along with this paper.</p> <p>You can find the code for training testing the models, that was published along with the paper in this <a href="https://github.com/oliverguhr/german-sentiment">repository</a>.</p> <p>The <a href="https://github.com/oliverguhr/german-sentiment-lib"><em>germansentiment</em></a> Python package contains a easy to use interface for the model that was published with this paper.</p> <p> </p> <p> </p>
GLC_FCS30-2020:Global Land Cover with Fine Classification System at 30m in 2020
<p>The new GLC_FCS30-2020 products were produced based on Global 30-m land-cover product with fine classification system in 2015 (GLC_FCS30-2015) and combined with the 2019-2020 time series Landsat surface reflectance data, Sentinel-1 SAR data, DEM terrain elevation data, global thematic auxiliary dataset and prior knowledge dataset. </p>
The System for Classification of Low-Pressure Systems (SyCLoPS) Dataset (Based on ERA5)
<p>This is the ERA5 System for Classification of Low-Pressure Systems (SyCLoPS) dataset<strong> version 6</strong>. Details of SyCLoPS algorithms are described in the paper titled <strong>The System for Classification of Low-Pressure Systems (SyCLoPS): An All-in-One Objective Framework for Large-scale Data sets </strong>published on <em>J. Geophys. Res. Atm.</em>:<strong> [<a href="https://doi.org/10.1029/2024JD041287">https://doi.org/10.1029/2024JD041287]</a></strong></p> <p><strong>Important: The most up-to-date SyCLoPS codes are now kept on GitHub: [<a href="https://github.com/yepkids/SyCLoPS">https://github.com/yepkids/SyCLoPS</a>] <br></strong>*Updates on GitHub: SyCLoPS can now run entirely in Python. See the GitHub README page for details.*</p> <p>SyCLoPS user manual: [<a href="https://climate.ucdavis.edu/syclops.php">https://climate.ucdavis.edu/syclops.php</a>]</p> <p><strong>Known issues</strong></p> <ol> <li>The master TE branch now lacks the ability to deal with large missing values in datasets (e.g. 1e20), this will result in unreasonable values in the classification process for some datasets. NaNs as missing values are safe to proceed with. We are working on this issue and users can expect a newer TE version with fixes in the near future. For now, users can install this fork of TempestExtremes via CMAKE, which can be found here: [<a href="https://github.com/yepkids/tempestextremes"><strong>https://github.com/yepkids/tempestextremes</strong></a>], to work around this problem. This fork provides a temporary solution that adds missing value support for operators used by SyCLoPS and has been tested. Note that this is not a stable release, and please report any problems with this fork to Yushan Han (yshhan@ucdavis.edu). You can also choose to convert all missing values in your input files to NaNs.</li> </ol> <p>TempestExtremes software (master branch): [<a href="https://github.com/ClimateGlobalChange/tempestextremes">https://github.com/ClimateGlobalChange/tempestextremes</a>] </p> <p><strong>Major updates and bug fixes in this version:</strong></p> <ol> <li><strong>The classified and input LPS dataset is now extended from 1979-2022 to 1970-2024 </strong>(See Chapter 3 of the manual on how to load and use the output classified catalog).</li> <li>Note: This release will have a slight difference in the number of nodes found and some tracks compared to previous releases for the overlapped period. This is partly due to the extension of the tracks at the beginning of 1979 and the end of 2022, and also to the use of the newly developed "--mergeequal" argument for ERA5 MSLP nodes (this is to avoid some rare cases where two nodes with exactly the same MSLP values are close to each other but are not merged; see the manual section 2.2 for more details). </li> </ol> <p><strong>The following files can be obtained from version 4:</strong></p> <ol> <li>The labeled size blobs of each year: "<strong>size_blobs_1979_2022.tar.gz</strong>"</li> <li>The labeled precipitation blobs of each year: "<strong>preci_blobs_1979_2022.tar.gz</strong>"</li> </ol> <p>Please contact Yushan Han (yshhan@ucdavis.edu) if you have questions about the SyCLoPS framework. Please contact Paul Ullrich (paullrich@ucdavis.edu) if you have any questions about the TE software.</p> <p>See below for a table of atmospheric variables required for SyCLoPS and a flowchart of the classification process. See the SyCLoPS manual for more details.</p>
Monthly data on caesarean practice using the Ten Group Classification System (2020-2025)
<p>This dataset present summary statistics (numbers of vaginal deliveries and number of caesarean sections according to the Ten groups) that were collected daily from January 2020 to June 2025 in the 32 hospitals participating in the Quali-Dec project. The data were extracted from the hospitals’ birth registers and medical records by a trained health care provider and controlled quarterly by the country data manager. Monthly summary statistics were collected and managed by using REDCap electronic data capture tools hosted at the Karolinska Institute and controlled at a monthly frequency by the principal data manager for pending data and duplicated records. The dataset is composed with 1984 monthly records that have been uploaded to Redcap: 400 from Argentina, 392 from Burkina Faso, 404 from Thailand and 401 from Vietnam. The data collected covered 728,586 deliveries: 392,194 vaginal births and 336,392 caesarean sections.</p>
Figure 10. Simple setae. A in Revising the definition of the crustacean seta and setal classification systems based on examinations of the mouthpart setae of seven species of decapods
Figure 10. Simple setae. A, typical simple setae from the mandibular palp of Panulirus argus. No outgrowths are seen. B, terminal pore (arrow) from simple seta. C, simple setae situated on the basis of maxilla 2 of Carcinus maenas. Abbreviation: Si, simple setae.
Figure 5. Pappose setae. A in Revising the definition of the crustacean seta and setal classification systems based on examinations of the mouthpart setae of seven species of decapods
Figure 5. Pappose setae. A, overview of two typical pappose setae from Cherax quadricarinatus. Note random arrangement of setules. B, tips of pappose setae from Stenopus hispidus. Setules get smaller closer to the tip (arrow). C, serration on the setules (arrows) from pappose seta. D, pappose setae on the exopod of maxilliped 1 of Carcinus maenas. E, pappose setae on the mandibular palp of Ca. maenas. F, pappose setae on the coxa of maxilliped 1 of Pagurus bernhardus. Abbreviation: Pa, pappose setae.
Figure 4 in Revising the definition of the crustacean seta and setal classification systems based on examinations of the mouthpart setae of seven species of decapods
Figure 4. Substructures of setae. A, infracuticular articulation with the general cuticle. Arrow indicates deep socket. B, supracuticular articulation (arrows) with the general cuticle. C, annulus seen as a ring in the cuticle (arrow). D, two rows of denticles arranged distally on a seta. E, large setule displaying articulation (arrow) with setal shaft. F, small setule with weak articulation (arrows). G, stitched picture showing gradual change from setule (arrow) to denticle (arrowhead) on the same seta. H, subterminal pore (arrow) from seta with denticles. I, terminal pore (arrow) from seta with denticles.
Figure 7. Serrulate setae. A, typical serrulate setae from maxilliped 1 in Revising the definition of the crustacean seta and setal classification systems based on examinations of the mouthpart setae of seven species of decapods
Figure 7. Serrulate setae. A, typical serrulate setae from maxilliped 1 of Pagurus bernhardus. Setules are small and only present on the distal half of the seta. B, middle part of serrulate seta with setules in three rows. C, setules from serrulate seta arranged randomly along the shaft. Note strong serration. D, small setules with weak articulations (arrows). E, scalelike setules from serrulate seta of Palaemon adspersus. Note serration on distal rim (arrows). F, terminal pore (arrow) from serrulate seta. G, serrulate setae on the coxa of maxilla 1 of Penaeus monodon. Abbreviation: Su, serrulate setae.
Figure 9. Papposerrate setae. A, typical papposerrate seta from maxilliped 1 in Revising the definition of the crustacean seta and setal classification systems based on examinations of the mouthpart setae of seven species of decapods
Figure 9. Papposerrate setae. A, typical papposerrate seta from maxilliped 1 of Cherax quadricarinatus, with long, randomly arranged setules on proximal part and denticles in two rows on distal part. B, transition region between long setules and denticles. Abbreviations: D, denticles; LS, long setules; SS, short setules.
Figure 8. Serrate setae. A in Revising the definition of the crustacean seta and setal classification systems based on examinations of the mouthpart setae of seven species of decapods
Figure 8. Serrate setae. A, typical serrate setae from the endopod of maxilla 1 of Cherax quadricarinatus. Denticles in two strict rows on the distal half. B, serrate seta with setules (arrow). Arrowhead indicates denticles. C, tip of serrate seta with terminal pore (arrow). No denticles, only scale-like setules near the tip (arrowhead). D, partial (arrows) and complete fusion of denticles on serrate seta from Penaeus monodon. E, serrate setae on the dactylus of maxilliped 3 of Palaemon adspersus. F, serrate setae on the dactylus of maxilliped 2 of Pe. monodon. Abbreviation: Se, serrate setae.
Figure 2 in Revising the definition of the crustacean seta and setal classification systems based on examinations of the mouthpart setae of seven species of decapods
Figure 2. Types of projections found on the general cuticle. A, type I projection, a seta, is an elongate circular projection, which is articulated with the general cuticle (arrow). It is the most common type of projection. B, type II projection, a seta, from maxilla 1 of Pagurus bernhardus with a more or less direct transition into the general cuticle. In the other species articulated setae are situated in the same place (compare with Fig. 11A). They may have small outgrowths (arrows). C, type III projections, denticles, from maxilliped 1 of Panulirus argus. Arrows indicate direct transition into general cuticle without an articulation. D, type IV projections, setules, from the paragnath of Stenopus hispidus. Arrows indicate serration and arrowheads indicate articulation with the general cuticle. Note the flattened shape at the base.
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.