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52 results for “esi”
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>
Imalys: ESIS-Software-Tools – Tutorial Data
<p>Imalys is a <a href="https://codebase.helmholtz.cloud/esis/Imalys">software library</a> in the <a href="https://doi.org/10.3390/rs16071139">ESIS project</a>. The Imalys library includes a <a href="https://codebase.helmholtz.cloud/esis/Imalys/tutorial">tutorial</a> that shows how image and vector data can be analyzed and transformed into new products. The examples in the tutorial refer to the sample data in <strong>Imalys_tutorial_data.zip</strong>.</p> <p>The software repository with source code, binaries, documents and the tutorials is available on <a href="https://codebase.helmholtz.cloud/esis/Imalys">GitLab</a> and in an earlier version on <a href="https://github.com/c7sepe2/Imalys_ESIS-Software-Tools">GitHub</a>.</p> <p>In the <a href="https://doi.org/10.3390/rs16071139">ESIS project</a> we are trying to put environmental indicators on a well-defined and reproducible basis. The ESIS software library "Imalys" is supposed to generate the remote sensing products defined for ESIS. Landscape diversity, change and different landuse types can be analyzed in time and space. Landuse borders can be delineated and typical landscape structures can be characterized by a self adjusting process.</p> <p><strong>Acknowledgements:</strong></p> <p>We thank the Helmholtz Association and the Federal Ministry of Education and Research (BMBF) for supporting the DataHub Initiative of the Research Field Earth and Environment. The DataHub enables an overarching and comprehensive research data management, following FAIR principles, for all topics in the Program Changing Earth – Sustaining our Future.</p> <p>Parts of the work was funded by the Federal Ministry for Digital and Transport of Germany (BMDV, grant no. 45KI19D041, Artificial Intelligence and Mobility - AIAMO project).</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>
ESI-CorA: SARS-CoV-2-Abwassersurveillance
<p>Das Vorhaben "Emergency Support Instrument - Nachweis von SARS-CoV-2 im Abwasser" (ESI-CorA) lief von November 2021 bis März 2023. Zwanzig Kläranlagenstandorte wurden in Deutschland ausgewählt, die im Februar 2022 gestaffelt mit der Überwachung von SARS-CoV-2 im Abwasser begonnen haben. Das zentrale Ziel in ESI-CorA war die Vorbereitung und Durchführung der bundesweiten Pilotphase zur Überwachung von SARS-CoV-2 und seiner Varianten im Abwasser. Unter anderem wurde das Verfahren zur Normalisierung der Rohdaten, der angewandten PCR-Analytik und der Berechnung der Trenddynamiken untersucht.<br> Weitere Informationen sind im <a href="https://www.ptka.kit.edu/img/Projektblatt_ESI-CorA.pdf">ESI-CorA Projektblatt</a> des Karlsruher Instituts für Technologie (KIT) zu finden.</p>
Earthquakes analyzed through the Environmental Seismic Intensity (ESI) scale
<p>The spreadsheet includes information on over 150 earthquakes that have been analyzed using the Environmental Seismic Intensity scale.</p>
Exhaled breath condensate samples (mzML files) analysed by uHPLC-ESI-OrbitrapMS
<p>LC-MS raw data files<strong> </strong>were converted to mzML using MSConvert (Version: 3.0.20279, ProteoWizard)</p> <p>- QC_pos_xxx: QC samples analysed in positive polarity (11 files)<br> - ACOS_XXX_pos_01 to 03: Asthmatic patients (3 replicates) randomly injected (15 files)<br> - Ctrl_XXX_pos_01 to 03: Control (15 files)<br> - DPOC_XXX_pos_01 to 03: COPD (15 files)</p> <p> </p> <p><strong>EBC Samples and Clinical Assessment</strong></p> <p>EBC samples were collected from 15 individuals randomly selected (five controls, five with asthma medical diagnosis, and five with COPD medical diagnosis, as assessed by the OLDER Study – Obstructive Lung Diseases in Elders). The Ethics Committee of Nova Medical School approved this study.</p> <p>Asthma was assigned when the patient reported respiratory symptoms, was a nonsmoker, and presented a positive reversibility test (FEV1 > 12% and 200 mL). COPD disease was attributed to those who also reported being current smokers, had a post-bronchodilator FEV1/FVC < 0.70, and had a negative reversibility test.</p> <p><strong>LC–MS Analysis</strong></p> <p>Samples were analyzed in triplicate by LC–MS using an Orbitrap Q Exactive Focus (Thermo Scientific) coupled to an Ultimate 3000 UHPLC (Thermo Scientific). A pooled quality control (QC) sample was used to compensate for any possible time-dependent batch effects. The QC samples were created using a small aliquot from each sample. The QC samples were reinjected at regular intervals to bracket the samples. The separation was performed using a Waters XBridge column C18 (2.1 × 150 mm, 3.5 μm particle size, P/N 186003023). The mobile phase A was water with 0.1% formic acid (v/v), and mobile phase B was acetonitrile with 0.1% formic acid (v/v) (Optima LC–MS Grade, Fisher Scientific). The gradient program was as follows: 1 min at 1% B; 1–13 min from 1 to 99% B, 13–15 min at 99% B, 15–16 min from 99 to 1% B, and 4 min at 1% B. The column temperature was maintained at 30 °C, and a flow rate of 400 μL/min was used.</p> <p>The Q Exactive Focus MS method consisted of several cycles of full MS scan (<em>R</em> = 70000) followed by three ddMS2 scans (<em>R</em> = 17500), with a (N)CE of 30 and in positive mode. External calibration was performed using LTQ ESI Positive Ion Calibration Solution (Thermo Scientific) and the lock mass enabled internal calibration. Data were obtained using the Xcalibur software v.4.0.27.19 (Thermo Scientific). The raw MS files, as recorded by the instrument, are available from the corresponding author upon reasonable request.</p>
high redshift (z~6) quasars (ESI)
<p>This data set contains 34 quasars at high redshift, i.e. 5.77<z<6.54, that were taken with the Echellette Spectrograph and Imager (ESI) at Keck II. The data was taken between the years of 2002 and 2016 and in most cases multiple exposures have been co-added to increase the S/N. The data release is described in Eilers et al. 2018. </p> <p>All spectra are reduced, flux-calibrated, co-added and continuum fitted. Each file contains four columns for wavelength, flux, noise, and the continuum estimate.</p> <p>If the data is being used, please cite Eilers et al. 2018 (https://ui.adsabs.harvard.edu//#abs/2018ApJ...864...53E/).</p>
Synthesis and biological evaluation of a radiolabeled PET probe for visualization of in vivo -fucosidase expression - esi
<p>Supplementary data for paper titled: <strong>Synthesis and biological evaluation of a radiolabeled PET probe for visualization of <em>in vivo</em> </strong><strong>a</strong><strong>-fucosidase expression</strong></p>
Validation of highly sensitive method based on UHPLC-ESI-MS/MS for the quantification of progestogens and androgens in plant material.
<p>We prepared a highly sensitive method for the quantification of progestogens and androgens in plant materials. This method is based on UHPLC-ESI-MS/MS. We show here the data used for the method validation. This includes the determination of linearity, recovery, precision, limits of detection and limits of quantification. </p> <p>The general procedure can be found in the txt or pdf file. </p> <p>The resulting data are collected in the excel file and can be found in the csv files, additonally.</p>
RECETOX Exposome HR-[ESI]-MS library
<p>The RECETOX Mass Spectrum Reference Libraries is a collection of MS spectra collected from authentic compounds. Each Library comprises the spectra in MSP format. The collection is shared under terms & conditions of CC-BY-NC.</p> <p>The RECETOX Exposome Pesticide HR-[ESI]-MS library is a collection of pesticides obtained from Restek Corporation. The RECETOX Exposome Pharmaceutical HR-[ESI]-MS library is a collection of pharmaceuticals selected from Prestwick Chemical Libraries.</p> <p>Compounds were analyzed by liquid chromatography coupled to a Thermo Scientific™ Orbitrap Fusion™ Tribrid™ Mass Spectrometer. Reversed-phase chromatography was performed using a pentabromobenzyl column. Tandem mass spectra were acquired via data-dependent acquisition with higher energy C-trap dissociation and real-time collision energy optimization for positive and negative mode electrospray ionization. The scan range was 85-1275 m/z with MS1 collected at 120K and MS2 at 60K resolving power. </p>
Reactions of cold argon plasma with condensed-phase peptides and proteins for mass spectrometry imaging and structural elucidation - ESI
<p>ESI data for the paper 'Reactions of cold argon plasma with condensed-phase peptides and proteins for mass spectrometry imaging and structural elucidation'.</p>
Antiox properties and mass fingerprint data (DLI-ESI and LTP MS) for elite maize (Zea mays) hybrids in different agroecologies
<p>Study of the nutraceutical value of elite maize (Zea mays) hybrids, using mass fingerprinting by direct liquid injection (DLI) electrospray ionization (ESI) and low-temperature plasma (LTP) ionization.</p>
Implementation of Pedometer for Monitoring Step Count Pre and Post Lumbar ESI for Radicular Pain
ClinicalTrials.gov study NCT02356783. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Study of Epidural Steroid Injection (ESI) Versus Minimally Invasive Lumbar Decompression (Mild®) in Patients With Symptomatic Lumbar Central Canal Stenosis
ClinicalTrials.gov study NCT00995371. IPD Sharing: Not stated. Countries: 1. Publications: 1.
ESI Group Simulations
<p>Heated 3D section: Automated Tape Placement Model</p> <p>and</p> <p>Structure Metal Tape Laying FPM-model</p>
ESI Management of dark fermentation broth via biorefining and photo fermentation
<p>Figure S1 FT-IR spectrums from optimization of the kind of HBD (a) Th; (b) M; (c) LC; (d) E used for effective removal of FF from the model fermentation broth and simultaneous formation of DES.</p> <p>Figure S2 FT-IR spectrums from optimization of the kind of HBD (a) Th; (b) M; (c) LC; (d) E used for effective removal of HMF from the model fermentation broth and simultaneous formation of DES.</p> <p>Figure S3 FT-IR spectrums from optimization of the kind of HBD (a) Th; (b) M; (c) LC; (d) E used for effective removal of Lev from the model fermentation broth and simultaneous formation of DES.</p> <p>Figure S4 FT-IR spectrums from optimization of the kind of HBD (a) Th; (b) M; (c) LC; (d) E used for effective removal of FA from the model fermentation broth and simultaneous formation of DES.</p>
Fig. 3 in Metabolic fingerprinting of Ganoderma spp. using UHPLC-ESI-QTOF-MS and its chemometric analysis
Fig. 3. Structures of five isomeric compounds with the molecular formula C30H42O7 and molecular mass 514.2931 found in the G44 sample.
Fig. 2 in Metabolic fingerprinting of Ganoderma spp. using UHPLC-ESI-QTOF-MS and its chemometric analysis
Fig. 2. General chemical structure of a typical triterpene showing the position of various side chains and list of side chains typically found in Ganoderma.
Fig. 1 in Metabolic fingerprinting of Ganoderma spp. using UHPLC-ESI-QTOF-MS and its chemometric analysis
Fig. 1. (a) Total ion chromatogram of G44 mushroom extract in ESI negative mode (b) Extracted ion chromatogram (EIC) of G44 mushroom extract in ESI negative mode.
ScienceDex guides
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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.