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opencc-by-4.0Jan 2020View details →

CzechVeg-ESy: Expert system for automatic classification of vegetation plots from the Czech Republic

Expertní systém pro automatickou klasifikaci fytocenologických snímků z České republiky  [popis a instrukce v če&scaron;tině jsou uvedeny níže] *************************************************************************************************************** CzechVeg-ESy is an expert system for automatic classification of vegetation plots from the Czech Republic to the vegetation types defined in the monograph Vegetation of the Czech Republic (<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 main version 1 (v1) 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 main version 2 (v2) uses the same classification system as accepted in the Vegetation of the Czech Republic, 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). Each version is delivered in two variants. The basic variant (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 full variant (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>. Instructions for using CzechVeg-ESy version 1 <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 Vegetation of the Czech Republic, 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> This electronic publication of CzechVeg-ESy was supported by the Czech Science Foundation (grant no. 17-15168S). *************************************************************************************************************** CzechVeg-ESy je expertní systém pro automatickou klasifikaci fytocenologických snímků z České republiky do vegetačních typů definovaných v monografii Vegetace České republiky (<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: Hlavní verze 1 (v1) 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. Hlavní verze 2 (v2) používá stejný klasifikační systém, jaký byl použit ve Vegetaci České republiky, ale používá pokročilej&scaron;í funkce umožňující přesněj&scaron;í klasifikaci. Tato verze také provádí hierarchickou klasifikaci nejen do asociací, ale také do svazů a tříd (pro snímky nezařazené do niž&scaron;ích jednotek). Každá verze má dvě varianty. Základní varianta (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 rychlej&scaron;í než klasifikace pomocí plné varianty. Plná varianta (soubor CzechVeg-ESy-full-v1.txt) zaji&scaron;ťuje 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. Vět&scaron;inu 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>. <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 příslu&scaron;ným 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 už&scaron;í pojetí druhů na &scaron;ir&scaron;í, 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 vy&scaron;&scaron;í hodnota, tím méně snímků se přiřadí, ale přiřadí se ty, které se dané asociaci více podobají. Při hodnotě 0 se přiřadí v&scaron;echny 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 my&scaron;í) nebo Classify [colour] Relevés (budou se klasifikovat v&scaron;echny 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 vypí&scaron;í 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 vepí&scaron;í kódy asociací u těch snímků, které se přiřadily na základě formálních definic. Převod kódů na jména asociací lze dohledat v ti&scaron;těné verzi Vegetace České republiky, 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 vypí&scaron;í nejpodobněj&scaron;í 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 rozli&scaron;ovaném 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> Tato elektronická publikace expertního systému CzechVeg-ESy byla podpořena Grantovou agenturou České republiky (grant 17-15168S).

opencc-by-4.0Oct 2017View details →

CATCH-EyoU: Processes in Youth's Construction of Active EU Citizenship: Wave 1 Questionnaires: Czech Republic

This dataset was generated within the research project Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions (CATCH-EyoU) funded by European Union, Horizon 2020 Programme - Grant Agreement No 649538. Work Package 7 of this project aims to test the processes in youth’s construction of active EU citizenship on various social and psychological levels. The main file contains quantitative data from the first wave of the longitudinal survey on adolescents and young adults (age 15-26). Data collection was carried out in the Czech Republic (regions Prague, South Moravian, Moravian-Silesian, Pardubicky, Vysocina) from October to December 2016. The supplementary files contain national translations of the questionnaire for the younger (15-19) and the older (20-26) subgroups.

opencc-by-4.0Oct 2024View details →

Photos from WIDER UPTAKE case study site in the Czech Republic (Camera 1)

Hourly photos from camera 1 at the WIDER UPTAKE H2020 project's case study site in the Czech Republic.

opencc-by-4.0Oct 2024View details →

Photos from WIDER UPTAKE case study site in the Czech Republic (Camera 3)

Hourly photos from camera 3 at the WIDER UPTAKE H2020 project's case study site in the Czech Republic.

opencc-by-4.0Oct 2024View details →

Photos from WIDER UPTAKE case study site in the Czech Republic (Camera 2)

Hourly photos from camera 2 at the WIDER UPTAKE H2020 project's case study site in the Czech Republic.

opencc-by-4.0Aug 2023View details →

[Luminescence Dataset] The loess sequence of Dolní Věstonice, Czech Republic: A new OSL‐based chronology of the last climatic cycle

Dolní Věstonice - Luminescence Dataset <table> <tbody> <tr> <td> Applicable Licence </td> <td> CC-BY-NC </td> </tr> <tr> <td> Data curators: </td> <td> Markus Fuchs, Sebastian Kreutzer </td> </tr> <tr> <td> Reference original work </td> <td> Fuchs, M., Kreutzer, S., Rousseau, D. D., Antoine, P., Hatté, C., Lagroix, F., Moine, O., Gauthier, C., Svoboda, J., and Lisá, L.: The loess sequence of Dolní Věstonice, Czech Republic: A new OSL‐based chronology of the last climatic cycle, Boreas, 42, 664–677, https://doi.org/10.1111/j.1502-3885.2012.00299.x, 2013. </td> </tr> <tr> <td> How to refer/cite this dataset </td> <td> Please the information auto-generated by Zendo </td> </tr> </tbody> </table> Scope This dataset contains the original luminescence data used to create the chronostratigraphy of the Dolní Věstonice loess profile. By publishing this dataset, we aim to support studies on the general characteristics of luminescence behaviours of natural minerals and support the FAIR guidelines for data sharing. This data is primary unmodified measurement data with all the possible errors and typos! Please note: The dataset was compiled with the greatest care. However, the dataset comes without any guarantee. Human errors are always possible. If you feel that, after reading the original study and this document, the metadata describing the dataset is insufficient, please contact the data curators so that this document can be updated accordingly. Contrary, the primary cannot be updated/modified! Dataset structure The dataset consists of sequence files (<code>SEQ</code>) and measurement data (<code>BIN</code>). To each <code>.bin</code> file, there should be one corresponding sequence file. Measurement data are tagged with sample names in the <code>.bin</code> file, e.g., unless, in case of mistakes, samples can be distinguished in the file.  <ul> <li> <code>...BIN/</code> <ul> <li> <code>BIN/A-VALUE/:</code>measurement data with a-value measurements; the alpha irradiation was partly done on an external source </li> <li> <code>BIN/MAIN/</code> measurement data with data used to estimate the equivalent dose of each sample </li> <li> <code>BIN/PREHEAT_PLATEAU</code> files with combined preheat and dose recovery test results </li> <li> <code>BIN/MISC</code> various additional measurements as indicated by the name </li> </ul> </li> <li> <code>...SEQ/</code> <ul> <li> <code>SEQ/A-VALUE/</code> </li> <li> <code>SEQ/MAIN</code> </li> <li> <code>SEQ/PREHEAT_PLATEAU</code> </li> <li> <code>SEQ/MISC</code> </li> </ul> </li> <li> <code>...DE_CSV_EXPORT</code> The extracted equivalent dose values from the measurements with uncertainties in s.  </li> </ul> Please note that samples were partly measured on different machines. All files are selections; in the course of the study, we carried out various additional measurements, in particular, preliminary tests; those data are not included in the dataset to keep the dataset comprehensible.  Used abbreviations Abbreviations as used in the measurement and sequence file names. For technical details, we refer to the original study and the reference therein. <table> <tbody> <tr> <td> ABBREVIATION </td> <td> TERM </td> <td> DESCRIPTION </td> </tr> <tr> <td> <code>CG</code> </td> <td> coarse grain </td> <td> refers to the used grain size fraction, here: 90-200 µm </td> </tr> <tr> <td> <code>DRT</code> </td> <td> dose-recovery test </td> <td> measurement data with dose recovery test results; in this study, such measurements were carried out in combination with different preheat settings </td> </tr> <tr> <td> <code>FG</code> </td> <td> fine grain </td> <td> refers to the used grain size fraction, here: 4-11 µm </td> </tr> <tr> <td> <code>IRSLT</code> </td> <td> Infrared stimulated test </td> <td> test for feldspar contamination </td> </tr> <tr> <td> <code>LM</code> </td> <td> linear modulation </td> <td> linearly modulated optically stimulated luminescence measurements </td> </tr> <tr> <td> <code>MAIN</code> </td> <td> main measurement </td> <td> usually, the measurement used to estimate the equivalent dose </td> </tr> <tr> <td> <code>MG</code> </td> <td> medium grain </td> <td> refers to the used grain size fraction, here: 38-63 µm </td> </tr> <tr> <td> <code>mz</code> </td> <td> Moritz </td> <td> Name of the used Risø OSL/TL DA-15 reader </td> </tr> <tr> <td> <code>mx</code> </td> <td> Max </td> <td> Name of the used Risø OSL/TL DA-15 reader </td> </tr> <tr> <td> <code>NEU</code> </td> <td> neu </td> <td> a German word translating to 'new' </td> </tr> <tr> <td> <code>oDRT</code> </td> <td> oDRT </td> <td> a typo for just <code>DRT</code> </td> </tr> <tr> <td> <code>PreaHeat</code> </td> <td> preheat test </td> <td> test against different preheat temperatures </td> </tr> <tr> <td> <code>Q</code> </td> <td> quartz </td> <td> the measured mineral composition, often in combination with <code>CG</code> , <code>MG</code> , or F<code>G</code> . Example: <code>FGQ</code> : fine grain quartz </td> </tr> </tbody> </table>

opencc-by-4.0Sep 2021View details →

Towards new demography proxies and regional chronologies: Radiocarbon dates from archaeological contexts located in the Czech Republic covering the period between 10,000 BC and AD 1250 (dataset)

The dataset was created within the project “Land use, social transformations and woodland in Central European Prehistory. Modelling approaches to human-environment interactions” funded by the Czech Science Foundation (19-20970Y). This dataset represents the largest and the most comprehensive collection of archaeological radiocarbon dates from the Czech Republic to date. The dataset offers 1579 samples from 347 archaeological sites dating from Early Mesolithic (10 000 BC) to Medieval Period (AD 1250). Published in a simple spreadsheet format, the database offers researchers a quick tool for further analyses. It is important to highlight that dates we collected originated only from archaeological contexts, which means that we have excluded some radiocarbon dates produced through palaeoecological research without a direct relationship to past human activities, such as pollen records or samples from fossilized trees in river beds. The dataset is intended to be used for demographic modelling of population numbers during periods without written records, i.e. prehistory.

opencc-zeroAug 2024View details →

National Checklists 2017: Czech Republic Species List

Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See https://github.com/diatomsRcool/checklists for detailsA list of species from the Czech Republic collected using effechecka and geonames polygons

opencc-zeroAug 2024View details →

National Checklists 2019: Czech Republic Species List

Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See https://github.com/diatomsRcool/checklists for details.A list of species from the Czech Republic collected using effechecka and geonames polygons

opencc-by-4.0Dec 2023View details →

Dataset for "Demographic yearbooks as a source of weather-related fatalities: the Czech Republic, 1919–2022"

This deposit contains three .xlsx files. The file &bdquo;01_fatalities_1919-2022“ contains annual numbers of fatalities (males, females and sum) for individual categories of external death causes attributed to weather and natural extremes, excerpted from demographic yearbooks for the Czech Republic for the period 1919–2022. The file &bdquo;02_age_categories_1931-2022“ contains eight sheets with annual numbers of weather-related fatalities in the Czech Republic in the period 1931–2022 for eight age categories and for males and females separately. Sheets represent individual categories of death causes – Cold, Heat, Lightning, Natural hazards, Fall on ice or snow, Air pressure. Heat and Natural hazards are divided into two sheets – one with summarized numbers and one with numbers for individual sub-categories. The file &bdquo;03_clima_factors“ contains mean temperature of January–February (Brázdil et al., 2012, extended) and mean annual number of days with a thunderstorm in the Czech Republic for the period 1919–2022 and mean temperature of winter season (DJF) in the Czech Republic for the period 1986/1987–2021/2022 (Brázdil et al., 2012, extended).

openodc-byDec 2018View details →

Traffic network routing index for the Czech Republic

Graph representation of road network of the Czech Republic This dataset contains graph of the entire Czech road network. The data are derived from the Open Street Map project and stored in a HDF5 file. The file contains graph topology and metadata for edges and vertices.  Spatial index is also included for the purpose of point snapping and other spatial queries. The spatial index is stored in a SQLite file with SpatiaLite extension. Creation of this dataset has been supported by the <a href="http://antarex-project.eu">Antarex project</a>. Routing Index in HDF5 File: <a href="/api/files/f1fb6db8-a2e2-425b-b15e-8e401eb22d44/CZE-1528295206-proc-20180724134457.hdf?versionId=3294ae0b-c3b2-4155-ad36-311d2bb06ed9">CZE-1528295206-proc-20180724134457.hdf</a> The index is divided into parts according to geographical boundaries defined by country borders. In this case it contains only single part - CZE. This information along with the creation time is stored as an attribute in the root group of the file. The graph topology is stored in the following way. Nodes have assigned a row index in the edges dataset which points to an outbound edge plus a number of the subsequent edges which are also output to this node. The edge metadata are stored in the EdgeData dataset to avoid redundancy. NodeMap dataset provides a convenient way to query nodes based on their uniqe identifiers. File structure: <pre><code class="language-javascript">HDF5 "CZE-1528295206-proc-20180724134457.hdf" { GROUP "/" { GROUP "Index" { ATTRIBUTE "CreationTime" { DATATYPE H5T_STD_I64LE DATASPACE SCALAR DATA { (0): 1535451896 } } ATTRIBUTE "PartsCount" { DATATYPE H5T_STD_I32LE DATASPACE SCALAR DATA { (0): 1 } } ATTRIBUTE "PartsInfo" { DATATYPE H5T_COMPOUND { H5T_STRING { STRSIZE 4; STRPAD H5T_STR_NULLPAD; CSET H5T_CSET_ASCII; CTYPE H5T_C_S1; } "id"; H5T_STD_I64LE "nodeCount"; H5T_STD_I64LE "edgeCount"; } DATASPACE SIMPLE { ( 1 ) / ( 1 ) } DATA { (0): { "CZE\000", 904085, 2223222 } } } GROUP "CZE" { ATTRIBUTE "PartInfo" { DATATYPE H5T_STRING { STRSIZE 4; STRPAD H5T_STR_NULLPAD; CSET H5T_CSET_ASCII; CTYPE H5T_C_S1; } DATASPACE SCALAR DATA { (0): "CZE\000" } } DATASET "Edges" { DATATYPE H5T_COMPOUND { H5T_STD_I32LE "edgeId"; H5T_STD_I32LE "nodeIndex"; H5T_STD_I32LE "computed_speed"; H5T_STD_I32LE "length"; H5T_STD_I32LE "edgeDataIndex"; } DATASPACE SIMPLE { ( 2223222, 1 ) / ( H5S_UNLIMITED, H5S_UNLIMITED ) } } DATASET "Nodes" { DATATYPE H5T_COMPOUND { H5T_STD_I32LE "id"; H5T_STD_I32LE "latitudeInt"; H5T_STD_I32LE "longtitudeInt"; H5T_STD_U8LE "edgeOutCount"; H5T_STD_I32LE "edgeOutIndex"; H5T_STD_U8LE "edgeInCount"; } DATASPACE SIMPLE { ( 904085, 1 ) / ( H5S_UNLIMITED, H5S_UNLIMITED ) } } } DATASET "EdgeData" { DATATYPE H5T_COMPOUND { H5T_STD_I32LE "id"; H5T_STD_U8LE "speed"; H5T_STD_U8LE "funcClass"; H5T_STD_U8LE "lanes"; H5T_STD_U8LE "vehicleAccess"; H5T_STD_U16LE "specificInfo"; H5T_STD_U16LE "maxWeight"; H5T_STD_U16LE "maxHeight"; H5T_STD_U8LE "maxAxleLoad"; H5T_STD_U8LE "maxWidth"; H5T_STD_U8LE "maxLength"; H5T_STD_I8LE "incline"; } DATASPACE SIMPLE { ( 2838, 1 ) / ( H5S_UNLIMITED, H5S_UNLIMITED ) } } DATASET "NodeMap" { DATATYPE H5T_COMPOUND { H5T_STD_I32LE "nodeId"; H5T_STRING { STRSIZE 4; STRPAD H5T_STR_NULLPAD; CSET H5T_CSET_ASCII; CTYPE H5T_C_S1; } "partId"; H5T_STD_I32LE "nodeIndex"; } DATASPACE SIMPLE { ( 904085, 1 ) / ( H5S_UNLIMITED, H5S_UNLIMITED ) } } } } }</code></pre> Spatial index File: <a href="https://zenodo.org/api/files/f1fb6db8-a2e2-425b-b15e-8e401eb22d44/CZE-1528295206-proc-20180829135251.sqlite?versionId=56bb1e60-ff7c-407f-8041-3c628ef78db0">CZE-1528295206-proc-20180829135251.sqlite</a>   SQLite database contains two primary tables. Table nodes and table segments, that resluts of selection and projection from the tables of the same name in primary db. Both tables are suitable for searching nearest lines task and searching closest node of nearest line task.  <pre><code class="language-sql">CREATE TABLE nodes ( gid INTEGER, part_gid INTEGER, node_type INTEGER, geom POINT ); CREATE TABLE segments ( gid INTEGER, node_gid_from INTEGER, node_gid_to INTEGER, frc TEXT, transition_time DOUBLE, computed_speed REAL, geom_length DOUBLE, geom LINESTRING ); </code></pre> There are three more tables. Table segments_rt is a copy of table segments, that exclude loops in segments, hence it supports network routing task in SpatiaLite. Next table is rt_network (static graph generated from table segments_rt suitable for routing). The last one is table virtual_rt_network, that is an interface for routing query.

opencc-by-4.0Mar 2023View details →

Data from: Vegetation change in acidic dry grasslands in Moravia (Czech Republic) over three decades: slow decrease in habitat quality after grazing cessation

This dataset contains the original data used in the article: Harásek M., Klinkovská K. & Chytrý M. (2023) Vegetation change in acidic dry grasslands in Moravia (Czech Republic) over three decades: slow decrease in habitat quality after grazing cessation. Applied Vegetation Science, 26, e12726. https://doi.org/10.1111/avsc.12726 The data contain plant species composition data from resurveyed vegetation plots in southwestern and central Moravia (Czech Republic). The plots were first surveyed by Milan Chytrý in 1986–1991 (“old plots”) and resurveyed by Martin Harásek, under the supervision of Milan Chytrý, in 2018–2019 (“new plots”). Of the old plots, 86 were sampled between 26 June and 16 September and 8 in May. Their size ranged from 5 to 49 m2 (mean 33 m2). These plots were subjectively selected at different sites to document maximum variation in species composition and environmental conditions of the grasslands and heathlands studied. In each plot, all vascular plant species were recorded, and their covers were estimated using the nine-grade Braun-Blanquet scale (van der Maarel 1979). Plot locations were recorded in the form of text descriptions. Geographic coordinates of approximate location were added for each plot prior to the resurvey by the original surveyor using georeferenced aerial photographs and various information recorded in the field during the first survey, including slope, aspect and elevation. Location uncertainty (mean = 139 m) was indicated as the possible distance of the actual location from the given coordinates. The resurvey was conducted between 4 June and 16 August. Care was taken to select the most likely location of the original plot based on the coordinates of the approximate location, the original site description, and the occurrence of the species recorded during the first survey. New plots always had the same plot size as in the original sampling. Each old plot was resurveyed using 1–3 new plots depending on the uncertainty of the location of the old plot. A total of 94 old plots were resurveyed at 47 sites with 153 new plots. Of these, 71 old plots at 32 sites were in current protected areas, while 23 old plots at 15 sites were outside protected areas. All new plots were located using GPS with a location uncertainty of approximately 5 m. For each old plot resurveyed with more than one new plot, the most similar new plot (based on Bray-Curtis dissimilarity in species composition) was selected, resulting in a dataset of 94 old and 94 new plots (“best-fit dataset”). To test the robustness of the results, we created another dataset (“validation dataset”) that included the least similar of the corresponding new plots for each old plot. This dataset also included the 94 old and 94 new plots. If the old plot was resurveyed using a single new plot, that new plot was included in both the best-fit and validation datasets. The header data structure follows that of the ReSurveyEurope Database (<a href="http://euroveg.org/eva-database-re-survey-europe">http://euroveg.org/eva-database-re-survey-europe</a>). In addition, fields are added to indicate whether the new plot was used in the best-fit dataset (Best_fit) or the validation dataset (Validation). The information about location within or outside the protected area is given in the field Protection. The data on species composition and environmental variables are provided in two formats: <ul> <li>Turboveg 2 database (see <a href="https://www.synbiosys.alterra.nl/turboveg/">https://www.synbiosys.alterra.nl/turboveg/</a>) – file TurbovegDbBackup_SW_moravia_acidgrass.zip. For using this dataset in Turboveg, the database dictionary (TurbovegDdBackup_Default dictionary.zip) and the species list (TurbovegSlBackup_Czechia_slovakia_2015.zip) must be installed.</li> <li>Three TXT files with columns separated by tabs: <ul> <li>SW_moravia_acidgrass_species.txt contains the percentage covers of plant species in the plots, which are mid-values for cover-abundance categories of the Braun-Blanquet scale. Plant nomenclature was harmonised according to Danihelka et al. (2012).</li> <li>SW_moravia_acidgrass _head.txt contains information on the number of species in each plot (number_species), the number, proportion and relative cover of threatened species (IUCN categories CR, EN, VU, NT, columns CR_NT_number, CR_NT_perc_number and CR_NT_perc_cover), alien species (alien_number, alien_perc_number, alien_perc_cover), species characteristic of dry grasslands (TH_number, TH_perc_number, TH_perc_cover), sand and rock-outcrop grasslands (TF_number, TF_perc_number, TF_perc_cover), mesotrophic grasslands (TD_number, TD_perc_number, TD_perc_cover) and herbaceous ruderal vegetation (XA_XC_number, XA_XC _perc_number, XA_XC _perc_cover) and unweighted means of Ellenberg-type indicator values for light (light), temperature (temperature), moisture (moisture), soil reaction (reaction) nutrients (nutrients) and salinity (salinity) used to test changes in these variables through time.</li> <li>SW_moravia_life_forms.txt contains information about the assignment of individual species to the life form, which was used to analyse changes in frequency and cover of the life forms.</li> </ul> </li> </ul> These data are also stored in the Czech National Phytosociological Database (Chytrý & Rafajová  2003; <a href="https://botzool.cz/vegsci/phytosociologicalDb">https://botzool.cz/vegsci/phytosociologicalDb</a>) and the ReSurveyEurope database (Knollová et al. 2023; <a href="http://euroveg.org/eva-database-re-survey-europe">http://euroveg.org/eva-database-re-survey-europe</a>).

opencc-by-4.0Jul 2018View details →

Figs 6–8 in Two new species of Heleomyzidae (Diptera) from Czech Republic and Crimea

Figs 6–8. Heleomyza kovali sp. nov., male genitalia. 6 – paratype (Crimea), last two abdominal segments and genitalia sublaterally; 7 – holotype (Czech Republic, Jizerské hory PLA), genitalia laterally; 8 – paratype (Crimea), aedeagal complex and hypandrium, dorsal view. For abbreviations see page 268.

opencc-by-4.0Jul 2018View details →

Figs 9–14 in Two new species of Heleomyzidae (Diptera) from Czech Republic and Crimea

Figs 9–14. Comparison of males of Eccoptomera nevrlyi sp. nov. and E. ornata Loew, 1862. 9–11, 13–14 – Eccoptomera nevrlyi: 9 – head and thorax, dorsal view; 10 – left wing, lateral view; 11 – hind leg, inner lateral view; 13 – head, anterior view; 14 – head and antennae, lateral view. 12 – E. ornata, basal part of hind leg of E. ornata, inner lateral view.

opencc-by-3.0Mar 2017View details →

Fig. 2. Sampling localities. A in Glossostyles perspicua gen. et sp. nov. and other fungivorous Cecidomyiidae (Diptera) new to the Czech and Slovak Republics

Fig. 2. Sampling localities. A. Velká Kotlina Glacial Cirque (Czech Republic) with a Malaise trap used in 2006. Frequent avalanches are the main cause of the unique subalpine biodiversity of this locality (e.g., more than 350 species of vascular plants have been recorded from there) B. Hrončecký grúň Reserve in Poľana Mts (Slovak Republic) with a Malaise trap used in 2005. This is a virgin forest mainly composed of fir and beech intermixed with ash, spruce and sycamore maple and with an enormous and unique diversity of flies (see Roháček & Ševčík 2009). Photos by J. Ševčík

opencc-by-3.0Mar 2017View details →

Fig. 3 in Glossostyles perspicua gen. et sp. nov. and other fungivorous Cecidomyiidae (Diptera) new to the Czech and Slovak Republics

Fig. 3. Morphology of Glossostyles perspicua Jaschhof & Sikora gen. et sp. nov. A. Wing, setae omitted (♀ from Tyresta). B. Gonostylus, lateral (specimen from Tyresta). C. Female genitalia, lateral view (specimen from Tyresta). D. Male genitalia, ventral (holotype). E. Male fourth flagellomere, lateral view (holotype). F. Female fourth flagellomere, lateral (specimen from Tyresta). Scale bars: A = 1 mm; B–F = 0.05 mm.

opencc-by-3.0Mar 2017View details →

Fig. 1. Sampling localities. A in Glossostyles perspicua gen. et sp. nov. and other fungivorous Cecidomyiidae (Diptera) new to the Czech and Slovak Republics

Fig. 1. Sampling localities. A. Rejvíz peat-bog (Czech Republic) with a Malaise trap used in 2004. A well preserved postglacial peat-bog with Pinus rotundata growth. B. Rejvíz peat-bog with the Malaise trap used in 2005. Photos by J. Ševčík.

opencc-by-4.0May 2016View details →

Figure 2 in New Cenozoic dragonflies from the Most Basin and Středohoří Complex volcanic area (Czech Republic, Germany)

Figure 2. Aeshna zlatkokvaceki sp. nov. (Aeshnidae) (A) Photograph of holotype specimen SMMG CsT 1091 (Senckenberg Naturhistorische Sammlungen Dresden coll., Germany), imprint only; (B) line drawing of fore wing. Scale bars represent 5 mm.

opencc-by-4.0Sep 2020View details →

Silene seeds from the laboratories of the Institute of Biophysics (Academy of Sciences) of Brno (Czech Republic)

Seeds of Silene for the analysis of morphology (Martín Gómez et al.) obtained from the laboratories of the Academy of Sciences of Brno (Czech Republic). Photos contains 40 seeds of: Silene acutifolia; S. colpophylla; S. conica; S. diclinis; S. dioica; S. gallica; S. italica; S. latifolia; S. noctiflora; S. nutans;  S. otites; S. pendula; S. saxifraga; S. schafta; S. tatarica;  S. viscosa; S. vulgaris; S. wolgensis; S. zawadzkii