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dc.contributor.author Rüdiger, Jan Oliver
dc.date.accessioned 2024-11-23T13:04:32Z
dc.date.available 2024-11-23T13:04:32Z
dc.date.issued 2024-11-21
dc.identifier.uri http://hdl.handle.net/11372/LRT-5794
dc.description *** german version see below *** The ‘Ancillary Monitor Corpus: Common Crawl - german web’ was designed with the aim of enabling a broad-based linguistic analysis of the German-language (visible) internet over time - with the aim of achieving comparability with the DeReKo (‘German Reference Corpus’ of the Leibniz Institute for the German Language - DeReKo volume 57 billion tokens - status: DeReKo Release 2024-I). The corpus is separated by year (here year 2022) and versioned (here version 1). Version 1 comprises (all years 2013-2024) 97.45 billion tokens. The corpus is based on the data dumps from CommonCrawl (https://commoncrawl.org/). CommonCrawl is a non-profit organisation that provides copies of the visible Internet free of charge for research purposes. The CommonCrawl WET raw data was first filtered by TLD (top-level domain). Only pages ending in the following TLDs were taken into account: ‘.at; .bayern; .berlin; .ch; .cologne; .de; .gmbh; .hamburg; .koeln; .nrw; .ruhr; .saarland; .swiss; .tirol; .wien; .zuerich’. These are the exclusive German-language TLDs according to ICANN (https://data.iana.org/TLD/tlds-alpha-by-domain.txt) as of 1 June 2024 - TLDs with a purely corporate reference (e.g. ‘.edeka; .bmw; .ford’) were excluded. The language of the individual documents (URLs) was then estimated with the help of NTextCat (https://github.com/ivanakcheurov/ntextcat) (via the CORE14 profile of NTextCat) - only those documents/URLs for which German was the most likely language were processed further (e.g. to exclude foreign-language material such as individual subpages). The third step involved filtering for manual selectors and filtering for 1:1 duplicates (within one year). The filtering and subsequent processing was carried out using CorpusExplorer (http://hdl.handle.net/11234/1-2634) and our own (supplementary) scripts, and the TreeTagger (http://hdl.handle.net/11372/LRT-323) was used for automatic annotation. The corpus was processed on the HELIX HPC cluster. The author would like to take this opportunity to thank the state of Baden-Württemberg and the German Research Foundation (DFG) for the possibility to use the bwHPC/HELIX HPC cluster - funding code HPC cluster: INST 35/1597-1 FUGG. Data content: - Tokens and record boundaries - Automatic lemma and POS annotation (using TreeTagger) - Metadata: - GUID - Unique identifier of the document - YEAR - Year of capture (please use this information for data slices) - Url - Full URL - Tld - Top-Level Domain - Domain - Domain without TLD (but with sub-domains if applicable) - DomainFull - Complete domain (incl. TLD) - DomainFull - Complete domain (incl. TLD) - Datum - (System Information): Date of the CorpusExplorer (date of capture by CommonCrawl - not date of creation/modification of the document). - Hash - (System Information): SHA1 hash of the CommonCrawl - Pfad - (System Information): Path of the cluster (raw data) - is supplied by the system. Please note that the files are saved as *.cec6.gz. These are binary files of the CorpusExplorer (see above). These files ensure efficient archiving. You can use both CorpusExplorer and the ‘CEC6-Converter’ (available for Linux, MacOS and Windows - see: https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-5705) to convert the data. The data can be exported in the following formats: - CATMA v6 - CoNLL - CSV - CSV (only meta-data) - DTA TCF-XML - DWDS TEI-XML - HTML - IDS I5-XML - IDS KorAP XML - IMS Open Corpus Workbench - JSON - OPUS Corpus Collection XCES - Plaintext - SaltXML - SlashA XML - SketchEngine VERT - SPEEDy/CODEX (JSON) - TLV-XML - TreeTagger - TXM - WebLicht - XML Please note that an export increases the storage space requirement extensively. The ‘CorpusExplorerConsole’ (https://github.com/notesjor/CorpusExplorer.Terminal.Console - available for Linux, MacOS and Windows) also offers a simple solution for editing and analysing. If you have any questions, please contact the author. Legal information The data was downloaded on 01.11.2024. The use, processing and distribution is subject to §60d UrhG (german copyright law), which authorises the use for non-commercial purposes in research and teaching. LINDAT/CLARIN is responsible for long-term archiving in accordance with §69d para. 5 and ensures that only authorised persons can access the data. The data has been checked to the best of our knowledge and belief (on a random basis) - should you nevertheless find legal violations (e.g. right to be forgotten, personal rights, etc.), please write an e-mail to the author (amc_report@jan-oliver-ruediger.de) with the following information: 1) why this content is undesirable (please outline only briefly) and 2) how the content can be identified - e.g. file name, URL or domain, etc. The author will endeavour to identify the content. The author will endeavour to remove the content and re-upload the data (modified) within two weeks (new version). If you have any further questions, please contact CLARIN. *** english version see above *** Das ‚Ancillary Monitor Corpus: Common Crawl - german web‘ wurde mit dem Ziel konzipiert - eine breit angelegte und zeitlich begleitende linguistische Analyse des deutschsprachigen (sichtbaren) Internets zu ermöglichen - wobei eine Vergleichbarkeit mit dem DeReKo (‚Deutsches Referenz Korpus‘ des Leibniz-Instituts für Deutsche Sprache - DeReKo Umfang 57 Mrd. Token - Stand: DeReKo Release 2024-I) angestrebt wird. Das Korpus ist nach Jahren getrennt (hier Jahr 2022) und versioniert (hier Version 1). Die Version 1 umfasst (alle Jahre 2013-2024) 97,45 Mrd. Token. Das Korpus basiert auf den Daten-Dumps von CommonCrawl (https://commoncrawl.org/). CommonCrawl ist eine Non-Profit-Organisation, die Kopien des sichtbaren Internets kostenlos für die Forschung zur Verfügung stellt. Die CommonCrawl WET Rohdaten wurden zunächst nach TLD (Top-Level Domain) gefiltert. Es wurden nur Seiten berücksichtigt, die auf folgende TLDs enden: „.at; .bayern; .berlin; .ch; .cologne; .de; .gmbh; .hamburg; .koeln; .nrw; .ruhr; .saarland; .swiss; .tirol; .wien; .zuerich“. Dies sind die exklusiven deutschsprachigen TLDs laut ICANN (https://data.iana.org/TLD/tlds-alpha-by-domain.txt) zum Stand 01.06.2024 - ausgeschlossen wurden TLDs mit reinem Firmenbezug (z.B. „.edeka; .bmw; .ford“). Für die einzelnen Dokumente (URLs) wurde dann mit Hilfe von NTextCat (https://github.com/ivanakcheurov/ntextcat) die Sprache geschätzt (über das CORE14-Profil von NTextCat) - es wurden nur solche Dokumente/URLs weiterverarbeitet, bei denen Deutsch die wahrscheinlichste Sprache war (z.B. um möglichst auszuschließen, dass fremdsprachiges Material wie einzelne Unterseitenbereiche enthalten sind). Als dritter Schritt erfolgte eine Filterung nach manuellen Selektoren und eine Filterung nach 1:1-Dubletten (innerhalb eines Jahres). Die Filterung und anschließende Aufbereitung erfolgte mit dem CorpusExplorer (http://hdl.handle.net/11234/1-2634) und eigenen (ergänzenden) Skripten, wobei für die automatische Annotation der TreeTagger (http://hdl.handle.net/11372/LRT-323) verwendet wurde. Die Aufbereitung des Korpus erfolgte auf dem HELIX-HPC-Cluster. Der Autor dankt an dieser Stelle dem Land Baden-Württemberg und der Deutschen Forschungsgemeinschaft (DFG) für die Möglichkeit das bwHPC/HELIX HPC-Cluster nutzen zu können – Förderkennzeichen HPC-Cluster: INST 35/1597-1 FUGG. Dateninhalt: - Token und Satzgrenzen - Automatische Lemma- und POS-Annotation (mittels TreeTagger) - Metadaten: - GUID - Eindeutiger Identifikator des Dokuments - YEAR - Jahr der Erfassung (bitte verwenden Sie diese Angabe für Datenschnitte) - Url - Vollständige URL - Tld – Top-Level Domain - Domain – Domain ohne TLD (aber ggf. mit Sub-Domains) - DomainFull – Vollständige Domain (inkl. TLD) - DomainFull - Komplette Domain (inkl. TLD) - Datum - (System Information): Datum des CorpusExplorers (Tag der Erfassung durch CommonCrawl - nicht Tag der Erstellung/Änderung des Dokuments). - Hash - (System Information): SHA1-Hash des CommonCrawl - Pfad - (System Information): Pfad des Clusters (Rohdaten) - wird systembedingt geliefert. Bitte beachten Sie, dass die Dateien als *.cec6.gz gespeichert sind. Dies sind Binärdateien des CorpusExplorers (siehe oben). Diese Dateien gewährleisten eine effiziente Archivierung. Sie können sowohl den CorpusExplorer als auch den ‚CEC6-Converter‘ (verfügbar für Linux, MacOS und Windows - siehe: https://lindat.mff.cuni.cz/repository/xmlui/handle/11372/LRT-5705) zur Konvertierung der Daten verwenden. Die Daten können in folgende Formate exportiert werden: - CATMA v6 - CoNLL - CSV - CSV (only meta-data) - DTA TCF-XML - DWDS TEI-XML - HTML - IDS I5-XML - IDS KorAP XML - IMS Open Corpus Workbench - JSON - OPUS Corpus Collection XCES - Plaintext - SaltXML - SlashA XML - SketchEngine VERT - SPEEDy/CODEX (JSON) - TLV-XML - TreeTagger - TXM - WebLicht - XML Bitte beachten Sie, dass ein Export den Speicherplatzbedarf erheblich erhöht. Eine einfache Lösung zur Bearbeitung und Analyse bietet auch die „CorpusExplorerConsole“ (https://github.com/notesjor/CorpusExplorer.Terminal.Console - verfügbar für Linux, MacOS und Windows). Bei Fragen wenden Sie sich bitte an den Autor. Rechtliche Hinweise Die Daten wurden am 01.11.2024 heruntergeladen. Die Nutzung, Verarbeitung und Verbreitung unterliegt §60d UrhG, der die Nutzung für nicht kommerzielle Zwecke in Forschung und Lehre erlaubt. LINDAT/CLARIN übernimmt die Langzeitarchivierung nach §69d Abs. 5 und stellt sicher, dass nur berechtigte Personen auf die Daten zugreifen können. Die Daten wurden nach bestem Wissen und Gewissen (stichprobenartig) überprüft - sollten Sie dennoch Rechtsverletzungen (z.B. Recht auf Vergessenwerden, Persönlichkeitsrechte etc.) finden, schreiben Sie bitte eine E-Mail an den Autor (amc_report@jan-oliver-ruediger.de) mit folgenden Informationen: 1) warum dieser Inhalt unerwünscht ist (bitte nur kurz skizzieren) und 2) wie der Inhalt identifiziert werden kann - z.B. Dateiname, URL oder Domain etc. Der Autor wird sich bemühen, den Inhalt zu entfernen und die Daten innerhalb von zwei Wochen (verändert) wieder hochzuladen (neue Version). Bei weiteren Fragen wenden Sie sich bitte an CLARIN.
dc.language.iso deu
dc.publisher Rüdiger, Jan Oliver
dc.rights Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/4.0/
dc.subject Web corpus
dc.subject web corpora
dc.subject corpus
dc.subject text corpora
dc.subject Ancillary Corpus
dc.subject Ancillary Monitor Corpus
dc.subject Monitor Corpus
dc.subject CorpusExplorer
dc.title Ancillary Monitor Corpus: Common Crawl - german web (YEAR 2022 – VERSION 1)
dc.type corpus
metashare.ResourceInfo#ContentInfo.mediaType text
dc.rights.label PUB
has.files yes
branding LRT + Open Submissions
contact.person Jan Oliver Rüdiger amc_report@jan-oliver-ruediger.de Leibniz-Institut für Deutsche Sprache
size.info 6108527 texts
size.info 6842836057 tokens
files.size 35586157942
files.count 172


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196.8 MB
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2022_0064.cec6.gz
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201.16 MB
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2022_0066.cec6.gz
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200.09 MB
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2022_0067.cec6.gz
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198.77 MB
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2022_0063.cec6.gz
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2022_0069.cec6.gz
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2022_0071.cec6.gz
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2022_0070.cec6.gz
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2022_0062.cec6.gz
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2022_0073.cec6.gz
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2022_0076.cec6.gz
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2022_0079.cec6.gz
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196.15 MB
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2022_0080.cec6.gz
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200.13 MB
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2022_0083.cec6.gz
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2022_0081.cec6.gz
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199.82 MB
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2022_0082.cec6.gz
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198.42 MB
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2022_0086.cec6.gz
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198.22 MB
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2022_0087.cec6.gz
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2022_0089.cec6.gz
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193.43 MB
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198.53 MB
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201.02 MB
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194.86 MB
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2022_0097.cec6.gz
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195.36 MB
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2022_0099.cec6.gz
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2022_0105.cec6.gz
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2022_0103.cec6.gz
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2022_0102.cec6.gz
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2022_0106.cec6.gz
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2022_0104.cec6.gz
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2022_0107.cec6.gz
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2022_0108.cec6.gz
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198.65 MB
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2022_0113.cec6.gz
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2022_0114.cec6.gz
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2022_0112.cec6.gz
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2022_0115.cec6.gz
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2022_0116.cec6.gz
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2022_0117.cec6.gz
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196.46 MB
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2022_0119.cec6.gz
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191.66 MB
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2022_0118.cec6.gz
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2022_0120.cec6.gz
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2022_0121.cec6.gz
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195.15 MB
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2022_0122.cec6.gz
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205.14 MB
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2022_0123.cec6.gz
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202.26 MB
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2022_0125.cec6.gz
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2022_0127.cec6.gz
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2022_0126.cec6.gz
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2022_0124.cec6.gz
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2022_0128.cec6.gz
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2022_0130.cec6.gz
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2022_0129.cec6.gz
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2022_0134.cec6.gz
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2022_0135.cec6.gz
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2022_0144.cec6.gz
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2022_0145.cec6.gz
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2022_0140.cec6.gz
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2022_0142.cec6.gz
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2022_0141.cec6.gz
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2022_0143.cec6.gz
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2022_0139.cec6.gz
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198.75 MB
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2022_0149.cec6.gz
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2022_0152.cec6.gz
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2022_0164.cec6.gz
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194 MB
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2022_0168.cec6.gz
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2022_0169.cec6.gz
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2022_0166.cec6.gz
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2022_0165.cec6.gz
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2022_0093.cec6.gz
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2022_0088.cec6.gz
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2022_0153.cec6.gz
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2022_0094.cec6.gz
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2022_0095.cec6.gz
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2022_0136.cec6.gz
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2022_0138.cec6.gz
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2022_0161.cec6.gz
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2022_0162.cec6.gz
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200.27 MB
Format
application/x-gzip
Description
Unknown
MD5
a598d7c87a37242dd5ad114882ef98d7
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