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163 results for “gazetteers”
Historical Place Gazetteer and Historical Mine Register, Tyrol 15th, 16th century
<p>The dataset is split in two separated files in CSV format (UTF 8):</p> <p>1) Historical place gazetteer: It contains historical tyrolean names of places (core region: districts Kufstein, Schwaz; Austria) within the mining documents Hs. 37 and Hs. 1587 and their modern equivalents enriched with unique Identifiers. Furthermore, Places were localised and georeferenced as far as current scientific data and knowledge permit. A final column shows corresponding IDs between Hs. 37 and Hs. 1587.</p> <p>2) Historical mine register: The file contains historical Tyrolean names of mines and pits (core region: district Kufsten, Schwaz) within the mining documents Hs. 37 and Hs. 1587 as well as a modern standardisation of the names. The mines also received unique Identifiers.</p> <p>The registers were generated by the research team of the project “Text Mining Medieval Mining Texts” (2019-2022). Two historical mining sources were processed: “Verleihbuch der Rattenberger Bergrichter” ( Hs. 37, 1460-1463) and “Schwazer Berglehenbuch” (Hs. 1587, approx. 1515) stored by the Tyrolean Regional Archive, Innsbruck (Austria). The central research objective of T.M.M.M.T. is the extraction and representation of the legal relationships between people, claims and mines over space and time. Furthermore it deals with the semantically opening and visualisation of the montanistic network of two tyrolean mining regions.</p> <p>Citeable Transcripts are online available:<br> Hs. 37 DOI: 10.5281/zenodo.6274562<br> Hs. 1587 DOI: 10.5281/zenodo.6274928</p> <p>The research project (2019-2022) was carried out at the University of Innsbruck and funded by go!digital next generation programme of the Austrian Academy of Sciences.</p>
Kabadayi_Boykov_Sefer_Gerrits_Ottoman_NFS_Gazetteer_23112022_16296_populated_places_version_1
<p>This gazetteer is a joint product of two research projects, UrbanOccupationsOETR (European Research Council - Starting Grant, Industrialisation and Urban Growth from the mid-nineteenth century Ottoman Empire to Contemporary Turkey in a Comparative Perspective, 1850-2000, <a href="https://urbanoccupations.ku.edu.tr/">https://urbanoccupations.ku.edu.tr</a>, Grant agreement ID: 679097) and POPGEO_BG (Marie Skłodowska-Curie Individual Fellowship, Population Geography of Bulgaria, 1500- 1920: A Historical Spatial Analysis, <a href="https://popgeo.ku.edu.tr/">https://popgeo.ku.edu.tr</a>, Grant agreement ID: 867474) both funded by the European Commission.</p> <p>M. Erdem Kabadayı and Grigor Boykov have been the principal investigators of UrbanOccupationsOETR and POPGEO_BG, respectively.</p> <p>This unprecedented large-scale Ottoman gazetteer is based on mid-nineteenth century population registers (NFS.d. with their archival fond) available at the Presidency of the Republic of Türkiye, Directorate of State Archives. Its current first version enlists 16.296 populated places (12.285 and 4.011 geolocated within UrbanOccupationsOETR and POPGEO_BG, respectively) extracted from 764 population registers dating between 1830 and 1849. For details of the geolocation procedure, especially for UrbanOccupationsOETR but also valid to a large extent POPGEO_BG, see:</p> <p>Ma, Jilian, Akın Sefer, and M. Erdem Kabadayı. “Geolocating Ottoman Settlements: The Use of Historical Maps for Digital Humanities.” In <em>Proceedings of the 8th International Symposium of the International Cartography Commission on the History of Cartography “Mapping the Ottoman Realm: Travelers, Cartographers and Archaeologists” 21–23 April 2020, Istanbul, Turkey (Rescheduled for December 2021, Florence, Italy)</em>, edited by Imre Josef Demhardt, Vol. 3. Göttingen: Copernicus Publications, 2021. <a href="https://doi.org/10.5194/ica-proc-3-10-2021">https://doi.org/10.5194/ica-proc-3-10-2021</a>.</p> <p>The gazetteer is available in the format of a spreadsheet with 15 columns. It provides the following information for all of the 16.296 enlisted populated places:</p> <ul> <li>longitude</li> <li>latitude</li> <li>register_date_in_Hicri (calendar)</li> <li>doc_type (<em>mufassal </em>or <em>icmal</em> population registers)</li> <li>NFS.d. register_number (as in the archival fond)</li> <li>project (acronym)</li> <li>toponym_modern (current toponym in various languages depending on the location of the populated place today). For 797 populated places, toponym_modern is entered as ‘vanished.’ These populated places are all geolocated with coordinates by using georeferenced historical maps. However, no current settlements could be identified in their locations.</li> <li>toponym Ottoman in NFS.d. (typed as accurately as possible in Arabic script copying from the Ottoman population registers)</li> <li>toponym transcribed (original handwritten Ottoman to Latin script, as a principle, following the Redhouse lexicon) from NFS.d. </li> <li><em>kaza</em> (the Ottoman administrative unit on the sub-district level as stated) in NFS.d.</li> <li><em>kaza_1848_1264</em> and <em>liva_1848_1264</em> correspondences of enlisted <em>kaza</em> information in NFS.d. registers according to the official Ottoman yearbook dating to 1848 (1264 in Hicri): <ul> <li><em>Salname-i Devlet-i Aliye-i Osmaniye</em>. 2nd edition. Istanbul: Darü’t-Tıbaatü’l-Amire, 1264.</li> </ul> </li> <li>(Unique) populated place id (number)</li> </ul> <p>As well as two additional administrative levels, <em>divan</em> and <em>nahiye</em> for the limited number of populated places.</p> <p>POPGEO_BG was an individual fellowship; therefore, Grigor Boykov has contributed to the construction of this section of the gazetteer single-handedly, from its conceptualization to the data entry and control.</p> <p>UrbanOccupationsOETR, on the other hand, sustained a large research team between 01.10.2016 and 30.09.2022 at Koç University and several team members contributed to the gazetteer in varying capacities:</p> <p>M. Erdem Kabadayı conceptualized and designed the research with support from Grigor Boykov. M. Erdem Kabadayı, Akın Sefer, Grigor Boykov, and Piet Gerrits supervised the project. Piet Gerrits designed and maintained graphical user interfaces for geolocation and data entry. Akın Sefer as a team leader, located, evaluated, and selected the population registers, supervised and controlled geolocation and data entry, and conducted data curation, coding, control, and cleaning. Efe Erünal, Aysel Yıldız, and Semih Çelik also conducted source selection. Efe Erünal was also active in geolocation and data entry control.</p> <p>Following UrbanOccupationsOETR team members in alphabetical order geolocated populated places and entered data into our geospatial databases: Akın Sefer, Aysel Yıldız, Barış Yıldırım, Deniz Ali Uyan, Efe Erünal, Fulya Özturan, Jilian Ma, Mertkan Karaca, Nikola Rakovski, Semih Çelik, Şehnaz İyibaş.</p> <p>External researchers Furkan Elmas and Alper Kara conducted Ottoman transcription corrections and new entries under the supervision of Akın Sefer.</p> <p>Piet Gerrits maintained the digital research infrastructure of the UrbanOccupationsOETR and merged the POPGEO_BG dataset to construct the gazetteer. Furthermore, he also prepared the GeoPackage and data visualization interface available at: <a href="https://urbanoccupations.ku.edu.tr/gazetteer/">https://urbanoccupations.ku.edu.tr/gazetteer/</a>.</p> <p>This gazetteer is a work in progress, and we would appreciate critical feedback to improve it in its following versions. Please get in touch with <a href="mailto:mkabadayi@ku.edu.tr">mkabadayi@ku.edu.tr</a> / <a href="mailto:mekabadayi@gmail.com">mekabadayi@gmail.com</a> / <a href="mailto:grigor.boykov@univie.ac.at">grigor.boykov@univie.ac.at</a> / <a href="mailto:griboykov@yahoo.com">griboykov@yahoo.com</a> for your correction suggestions and inquiries.</p> <p>If you would like to use the gazetteer in further publication, please use the credentials specified below:</p> <div> <div>Kabadayı, M. Erdem, Akın Sefer, Grigor Boykov, and Piet Gerrits. 2022. “Making of a Mid-Nineteenth Century Ottoman Gazetteer and Mapping and Examining Late Ottoman Population Geography.” <em>Journal of the Ottoman & Turkish Studies Association</em> 9 (2): 179–204. <a href="https://doi.org/10.2979/jottturstuass.9.2.25">https://doi.org/10.2979/jottturstuass.9.2.25</a>.</div> </div>
Gazetteer_Generalkarte von Mitteleuropa
<p>The gazetteer contains 21,524 macro toponyms, manually map mined from the Generalkarte von Mitteleuropa (1:200,000), a result of the 3rd Military Mapping Survey of Austria-Hungary.</p><p>The gazetteer is formatted as a CSV spreadsheet with seven columns. For each toponym, it provides the following information:</p><ul><li>longitude</li><li>latitude</li><li>Name</li></ul><p>Indicates the toponym as it appears on the map</p><ul><li>Description</li></ul><p>Indicates the settlement's modern name using the original spelling and alphabet of its respective country.</p><ul><li>AltName</li></ul><p>Indicates settlements indicated on the map with more than one name</p><ul><li>Type</li></ul><p>Indicates the category of the toponym assigned by Boykov: arsenal; barracks; bath; bridge; chapel; church; customs office; factory; fishery; fishing weir; fortress; han; hospital; institute; karaul; lighthouse; lithography; mandra; melting house; metochion; military depot; military station; military supply spot; mill; mine; mine_quicksilver; mine_ salt; monastery; ore deposits; paddy field; palace; pasture; port; quarantine; residence; road tunnel; ruins; ruins. fortress; saltworks; school; settlement; settlement_chiftlik; settlement_huts; sheepfold; stud; summer pasture; tabiya; teke; tower; train station; villa; water spring; watermill; winery; winter pasture</p><ul><li>Country</li></ul><p>Indicates the modern state in which the toponym falls: Bulgaria (BG); Greece (GRE); Kosovo (KOS); N. Macedonia (MK); Romania (RO); Serbia (SER); Turkey (TR)</p><p> </p><p>The gazetteer is a work in progress and your feedback is highly appreciated. Please do not hesitate to contact at: <a href="mailto:grigor.boykov@univie.ac.at">grigor.boykov@univie.ac.at</a> and <a href="mailto:griboykov@gmail.com">griboykov@gmail.com</a></p><p> </p><p>If you would like to use the gazetteer in further publication, please use the credentials specified below:</p><p>Grigor Boykov. (2023). Boykov_Gazetteer_Generalkarte von Mitteleuropa (v.1). Zenodo. https://doi.org/10.5281/zenodo.8409507</p><p>Acknowledgments: Boykov received partial funding from the Bulgarian Science Fund and the National Research Program Cultural Heritage, National Memory and Society Development of the Ministry of Education and Science in Bulgaria for developing the gazetteer. Data extraction was carried out by Hristo Hristozov of the University of Plovdiv and Alexander Zhabov of the University of Sofia. </p>
The SAFE Gazetteer
<b>Description: </b><p>These files contain the gazetteer used to record distinct, repeated sampling sites across the SAFE Project, including locations in the experimental landscape, the converted landscape surrounding SAFE and forest reserves and protected areas. <br><br>The data files include a geojson file containing the sampling locations and their associated metadata and a CSV file containing recognized aliases for official gazetteer locations. The data in the csv alias file is deliberately not included in this metadata spreadsheet as the data is primarily used in this CSV format by data checking and other processes. The GIS geometries in the geojson file (using WGS84 coordinates) are taken from a wide range of original GIS files collected from researchers at SAFE: the source files are indicated in the geometry metadata and most will also be available in the SAFE Zenodo community. The files also include the R code used to collate the geometry data from the various source files and create the geojson and aliases files.<br><br>This update adds new LOMBOK river transects (RRSJI1-6, RR8, RR10, RR19, RR20) </p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/1"><b>SAFE CORE DATA</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3906082">here</a></p><p><b>Files: </b>This dataset consists of 4 files: SAFE_Gazetteer_metadata_v3.xlsx, gazetteer.geojson, location_aliases.csv, gazetteer_sf.R</p><p><b>SAFE_Gazetteer_metadata_v3.xlsx</b></p><p>This file only contains metadata for the files below</p><p><b>gazetteer.geojson</b></p><p>Description: A geojson file containing officially recognised SAFE Project sampling locations, along with basic metadata.</p><p>This file contains 1 data tables:</p><ol><li><p></p><p><b>SAFE Gazetteer</b> (described in worksheet gazetteer.geojson)</p><p>Description: This worksheet describes the feature data associated with the geometries in the Gazeetteer.geojson file.</p><p>Number of fields: 15</p><p>Number of data rows: Unavailable (table metadata description only).</p><p>Fields: </p><ul><li><b>location</b>: The primary ID code used for this location (Field type: id)</li><li><b>type</b>: Broad categories describing the different sampling types and campaigns that have been included in the gazetteer (Field type: categorical)</li><li><b>plot_size</b>: Intended geometry area for area based sampling locations. (Field type: categorical)</li><li><b>display_order</b>: Integer value used to control plotting order in displaying gazetteer data (Field type: numeric)</li><li><b>parent</b>: ID of parent location, for locations nested within larger sampling areas (Field type: categorical)</li><li><b>region</b>: Broad sampling regions within the SAFE Project (Field type: categorical)</li><li><b>fractal_order</b>: Fractal nesting level for core SAFE sampling sites (Field type: numeric)</li><li><b>transect_order</b>: Position along sampling point transect within blocks (Field type: numeric)</li><li><b>centroid_x</b>: Longitudinal centroid of feature geometry, not defined for linear geometries (Field type: numeric)</li><li><b>centroid_y</b>: Latitudinal centroid of feature geometry, not defined for linear geometries (Field type: numeric)</li><li><b>source</b>: Name of of original GIS source file containing geometry data (Field type: categorical)</li><li><b>bbox_xmin</b>: Geographic bound of geometry (Field type: numeric)</li><li><b>bbox_ymin</b>: Geographic bound of geometry (Field type: numeric)</li><li><b>bbox_xmax</b>: Geographic bound of geometry (Field type: numeric)</li><li><b>bbox_ymax</b>: Geographic bound of geometry (Field type: numeric)</li></ul><p></p></li></ol><p><b>location_aliases.csv</b></p><p>Description: A CSV file containing pairs of official location names and accepted aliases, with an additional column showing where a location in an published dataset has been accepted as a gazeteer location</p><p>This file contains 1 data tables:</p><ol><li><p></p><p><b>SAFE Gazetteer location aliases</b> (described in worksheet location_aliases.csv)</p><p>Description: This file contains pairs of known aliases for locations and the matching official location ID. Aliases can be global or can be tied to a specific dataset ID.</p><p>Number of fields: 3</p><p>Number of data rows: Unavailable (table metadata description only).</p><p>Fields: </p><ul><li><b>zenodo_record_id</b>: location_aliases.csv (Field type: id)</li><li><b>location</b>: An official SAFE gazetteer location name (Field type: id)</li><li><b>alias</b>: A commonly used alternative alias for the location (Field type: id)</li></ul><p></p></li></ol><p><b>gazetteer_sf.R</b></p><p>Description: The R file used to generate the two gazetteer files from the source files. The source files are not included in this record as they are published under separate records.</p><p><b>Date range: </b>2010-10-01 to 2020-05-19</p><p><b>Latitudinal extent: </b>4.4121 to 4.9618</p><p><b>Longitudinal extent: </b>116.9471 to 117.9959</p>
DISTEMIST gazetteer
<p><strong>Please, cite use:</strong></p> <p>Miranda-Escalada, A., Gascó, L., Lima-López, S., Farré-Maduell, E., Estrada, D., Nentidis, A., Krithara, A., Katsimpras, G., Paliouras, G., & Krallinger, M. (2022). Overview of DisTEMIST at BioASQ: Automatic detection and normalization of diseases from clinical texts: results, methods, evaluation and multilingual resources. <em>Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings</em></p> <pre><code>@article{miranda2022overview, title={Overview of DisTEMIST at BioASQ: Automatic detection and normalization of diseases from clinical texts: results, methods, evaluation and multilingual resources}, author={Miranda-Escalada, Antonio and Gascó, Luis and Lima-López, Salvador and Farré-Maduell, Eulàlia and Estrada, Darryl and Nentidis, Anastasios and Krithara, Anastasia and Katsimpras, Georgios and Paliouras, Georgios and Krallinger, Martin}, booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings}, year={2022} }</code></pre> <p> </p> <p> </p> <p>The DISTEMIST gazetteer contains main terms and synonyms from the relevant branches of Snomed-CT for the grounding of disease mentions. There are mentions belonging to Snomed-CT hierarchies such as disorder, finding, or morphological abnormality.</p> <p>The DISTEMIST Gazetteer is also used as an <strong>additional resource</strong> in the <strong>SocialDisNER task.</strong> </p> <p><strong>Format</strong></p> <p>Tab-separated file with the following columns:</p> <ul> <li>code: Snomed-CT ID</li> <li>language</li> <li>term: string mention</li> <li>semantic_tag: Snomed-CT hierarchy</li> <li>mainterm: binary field, indicating whether the term is a main term, or not</li> </ul> <p> </p> <p><strong>DISTEMIST resources:</strong></p> <ul> <li><a href="https://temu.bsc.es/distemist/"><strong>Web</strong></a></li> <li><a href="https://doi.org/10.5281/zenodo.6408476"><strong>DISTEMIST Gold Standard</strong></a></li> <li><a href="https://doi.org/10.5281/zenodo.6458078"><strong>DISTEMIST annotation guidelines</strong></a></li> </ul> <p><strong>SocialDisNER resouress:</strong></p> <ul> <li><a href="https://temu.bsc.es/socialdisner/"><strong>Web</strong></a></li> <li><a href="https://doi.org/10.5281/zenodo.6641702"><strong>SocialDisNER corpora</strong></a></li> </ul> <p>For further information, please visit https://temu.bsc.es/livingner/ or email us at encargo-pln-life@bsc.es</p>
Mulberry Disasters in Chinese Local Gazetteers
<p>This dataset contains 404 mulberry disasters found in a digital collection of 4,000 Chinese local gazetteers (published in Erudition's Zhongguo Fangzhi Ku, or the Database of Chinese Local Gazetteers) that was curated during 2018 and 2019 by scholars at the Max Planck Institute for the History of Science to support their joint research paper entitled: “What Is Local Knowledge: Digital Humanities and Yuan Dynasty Disasters in Imperial China’s Local Gazetteers.” The paper appeared in the <em>Journal of Chinese History</em> in its 2020 spring issue. The authors use this dataset to ask what results the emerging methodology of analyzing data drawn from historical sources can produce for historical research, given that such data-driven analysis is inevitably quantitative and that many historians believe this would contradict the core value of studies in the humanities.</p> <p>This dataset is accompanied by a data paper to be published by Brill's Digital Concordances Platform. In the data paper, the authors give information about the full curation cycle of this dataset, including the research goal that motivated the curation, the sources that were used, and the curation/cleansing/preparation process. The authors also explain this dataset in detail, including the kind of information it contains and its overall temporal and geospatial distributions. In conclusion, they discuss the potential usages of the dataset. With this paper, we also hope to offer an exemplary data curation workflow for collecting data from historical sources that includes not only data curation (whether manual or semiautomatic), but also the practical steps of data cleansing and normalization that reflect the various historical considerations in the process.</p>
GazetteersScotland-KG: A Knowlege Graph for representing the Gazetteers of Scotland (1803-1901)
<p>This Knowlege Graph represents the information of the "Gazeteers of Scotland<strong>"</strong> (years: 1803 - 1901) collection in RDF (ttl format). This collection comprises twenty volumes of the most popular descriptive gazetteers of Scotland in the 19th century. Principal places in Scotland, including towns, counties, castles, glens, antiquities and parishes, are listed alphabetically. Each entry includes detailed historical and geographical information about each place. The raw dataset is provided by the NLS in this <a href="https://data.nls.uk/data/digitised-collections/gazetteers-of-scotland/">link</a>. As other NLS data collections, they are originally provided using two XMLs schemas: METS for descriptive, structural, technical and administrative metadata (Title, Author, Publisher, etc); and ALTO for encoding the OCR text of a page.</p> <p>In this work, we have extracted the information from METS and ALTO XMLS using <a href="https://github.com/francesNLP/defoe">defoe</a> tool and developed a <a href="https://github.com/francesNLP/defoe/blob/master/defoe/nls/queries/write_metadata_pages_yml.py">new information extraction defoe query</a> , and created a new Knowlege Graph called GazetteersScotland-KG. The GazetteersScotland-KG uses the <a href="https://francesnlp.github.io/NLS-ontology/doc/index-en.html">NLS Ontology </a>to represent the information extracted. Furthermore, during the information extraction phase, we have employed several techniques to mitigate two common OCR errors: long-S and the line-break hyphenation.</p> <p>The GazetteersScotland-KG contains 354,998 RDF triples. It has information from 12 series and 20 volumes: Each serie can have several Volumes. Each serie has an Editorm Publisher, mmsid, Shelf-Locator, publication year, etc. A Volume has several Pages, with text in them. The data model of the GazetteersScotland-KG can be found <a href="https://francesnlp.github.io/NLS-ontology/doc/dataModel.png">here</a>.</p> <pre> </pre>
Historical Mine Register & Place Gazetteer: Schwaz / Rottenburg / Rattenberg, Tyrol; 17th Century
<p>The data of the historical registers is based on the historical document “Bergbeschau Schwaz/Rottenburg/Rattenberg” of the 17<sup>th</sup> century (approx. 1666). The document is currently stored by the Salinenarchiv Bad Ischl) using the Identifier XXD6.The document contains 6 coloured maps of the mining areas in the mining district of Schwaz (Falkenstein, Ringenwechsel, Mehren/Merach, Reichental, Palleiten) as well as the mining district of Rattenberg (Groß-, Kleinkogel, Geyer). Furthermore, it contains detailed mine descriptions and localisations of the mines in the maps.</p> <p>Historical Mine Register: Sorting is done by mining area, main pit / mine and associated mine sections. The historical writings of the mines and mine sections are summarized in separate columns (alternate writing). Mines written in capital letters symbolize latin characters within the historical document. All mines received an Identifier and URIs which lead to a Knowledge Graph (graph_Link). In a separate column the mines also received a standard spelling (mine_name_standard). The mentioned mines were compared to two geological maps created by Herwig Pirkl (1961). Mines that could be also be georeferenced by comparison also received a geo_link to OpenStreetMap as well as HIK (‘Historische Kartenwerke Tirol’). Finally, the historical mines of the “Bergbeschau” are also linked to the Knowledge Graph TMMMT (DOI: 10.5281/zenodo.6276586) of the UIBK Project „Text Mining Medieval Mining Texts“, whenever possible.</p> <p>The historical place gazetteer is in alphabetical order by place_standard. The historical writings of the place names are summarized in a separate column (alternate writing). Places written in capital letters symbolize latin characters within the historical document. All places received an Identifier and URIs which lead to a Knowledge Graph (graph_Link). In a separate column the places received their current standard spelling (place_standard). Places that could be also be georeferenced also received coordinates as well as a geo_link to OpenStreetMap. Finally, the historical places of the “Bergbeschau” are also linked to the Knowledge Graph TMMMT (DOI: 10.5281/zenodo.6276586) of the UIBK Project „Text Mining Medieval Mining Texts“, whenever possible.</p>
N-gram dataset of Chinese local gazetteers (中國地方誌)
<p>This dataset contains the N-grams (1-3) collected from 11083 Chinese local gazetteers (中國地方誌).</p> <p>The dataset comprises of the following resources:</p> <ul> <li><strong>local_gazetteer_1.7z</strong> Unigram dataset in tab separated format (one file per book, each row contains the N-gram and its count)</li> <li><strong>local_gazetteer</strong><strong>_2.7z</strong> Bigram dataset in tab separated format (one file per book, each row contains the N-gram and its count)</li> <li><strong>local_gazetteer</strong><strong>_3.7z</strong> Trigram dataset in tab separated format (one file per book, each row contains the N-gram and its count)</li> <li><strong>local_gazetteer</strong><strong>_metadata.xlsx</strong> Metadata of each book</li> </ul> <p> </p> <p>Dieses Datenset enthält die in 11083 chinesischen Lokalmonographien (中國地方誌) enthaltenen N-Gramme (1-3). </p> <p>Das Datenset besteht aus den folgenden Dateien:</p> <ul> <li><strong>local_gazetteer</strong><strong>_1.7z</strong><em> </em>Monogramm-Datenset im .txt Dateiformat mit Tabstopp als Trennzeichen (jede Datei enthält ein Buch, jede Zeile ein N-Gramm mit der Anzahl der Vorkommnisse im Text)</li> <li><strong>local_gazetteer</strong><strong>_2.7z</strong><em> </em>Bigramm-Datenset im .txt Dateiformat mit Tabstopp als Trennzeichen (jede Datei enthält ein Buch, jede Zeile ein N-Gramm mit der Anzahl der Vorkommnisse im Text)</li> <li><strong>local_gazetteer</strong><strong>_3.7z</strong><em> </em>Trigramm-Datenset im .txt Dateiformat mit Tabstopp als Trennzeichen (jede Datei enthält ein Buch, jede Zeile ein N-Gramm mit der Anzahl der Vorkommnisse im Text)</li> <li><strong>local_gazetteer</strong><em><strong>_</strong></em><strong>metata.xlsx</strong> Metadaten der enthaltenen Bücher</li> </ul> <p> </p> <p>11083 中國地方誌n元語法統計資料 (N-gram Dataset)</p> <p>以下是檔案簡說:</p> <ul> <li><strong>local_gazetteer</strong><strong>_1.7z</strong><em> </em>中國地方誌一元分詞(Unigram)的統計資料 (每本書一個檔案, 以tab作欄區分, 每一行紀錄該N-gram在書中出現的次數)</li> <li><strong>local_gazetteer</strong><strong>_2.7z</strong><em> </em>中國地方誌二元分詞(Bigram)的統計資料 (每本書一個檔案, 以tab作欄區分, 每一行紀錄該N-gram在書中出現的次數)</li> <li><strong>local_gazetteer</strong><strong>_3.7z</strong><em> </em>中國地方誌三元分詞(Trigram)的統計資料 (每本書一個檔案, 以tab作欄區分, 每一行紀錄該N-gram在書中出現的次數)</li> <li><strong>local_gazetteer</strong><em><strong>_</strong></em><strong>metadata.xlsx</strong> 紀錄每本書的基本Metadata</li> </ul>
Gazetteer_Russian-3verst_map
<p>The gazetteer contains 6,446 macro toponyms manually map mined for the Russian military topographic map at scale of 3 verst (1:126,000).</p><p>The gazetteer is available in the format of a CSV spreadsheet with 6 columns. It provides the following information for all of the toponyms:</p><ul><li>longitude</li><li>latitude</li><li>Name</li></ul><p>Indicates the toponym as it appears on the map</p><ul><li>Description</li></ul><p>Indicates the modern name of the settlement in the original spelling and alphabet of the belongingcountry</p><ul><li>AltName</li></ul><p>Indicates settlements indicated on the map with more than one name</p><ul><li>Type</li></ul><p>Indicates the category of the toponym assigned by Boykov: bath; bridge; chapel; fortress; fulling-mill; han; monastery; mosque; ore deposits; ruins; settlement; settlement_chiftlik; settlement_huts; sheepfold; spring; stud; teke; tower; villa; watermill; winery; winter pasture.</p><p>The gazetteer is a work in progress and your feedback is highly appreciated. Please do not hesitate to contact at <a href="mailto:grigor.boykov@univie.ac.at">grigor.boykov@univie.ac.at</a> and <a href="mailto:griboykov@gmail.com">griboykov@gmail.com</a></p><p> </p><p>If you would like to use the gazetteer in further publication, please use the credentials specified below:</p><p>Grigor Boykov. (2023). Boykov_Gazetteer_Russian-3verst_map. Zenodo. https://doi.org/10.5281/zenodo.8411078</p><p>Acknowledgments: Boykov received partial funding from the Bulgarian Science Fund and the National Research Program Cultural Heritage, National Memory and Society Development of the Ministry of Education and Science in Bulgaria for developing the gazetteer. Data extraction was carried out by Hristo Hristozov of the University of Plovdiv and Alexander Zhabov of the University of Sofia. </p>
AI4PROFHEALTH - Automatic Silver Gazetteer for Named Entity Recognition and Normalization
<p>This dataset comprises a professions gazetteer generated with automatically extracted terminology from the Mesinesp2 corpus, a manually annotated corpus in which domain experts have labeled a set of scientific literature, clinical trials, and patent abstracts, as well as clinical case reports. </p> <p>A silver gazetteer for mention classification and normalization is created combining the predictions of automatic Named Entity Recognition models and normalization using Entity Linking to three controlled vocabularies SNOMED CT, NCBI and ESCO. The sources are 265,025 different documents, where 249,538 correspond to <a href="https://zenodo.org/records/4707104">MESINESP2 Corpora</a> and 15,487 to clinical cases from open clinical journals. From them, 5,682,000 mentions are extracted and 4,909,966 (86.42%) are normalized to any of the ontologies: SNOMED CT (4,909,966) for diseases, symptoms, drugs, locations, occupations, procedures and species; ESCO (215,140) for occupations; and NCBI (1,469,256) for species.</p> <p>The repository contains a .tsv file with the following columns:</p> <ul> <li><strong><code>filenameid</code></strong>: A unique identifier combining the file name and mention span within the text. This ensures each extracted mention is uniquely traceable. Example: biblio-1000005#239#256 refers to a mention spanning characters 239–256 in the file with the name biblio-1000005.</li> <li> <p><strong><code>span</code></strong>: The specific text span (mention) extracted from the document, representing a term or phrase identified in the dataset. Example: centro oncológico.</p> </li> <li> <p><strong><code>source</code></strong>: The origin of the document, indicating the corpus from which the mention was extracted. Possible values: mesinesp2, clinical_cases.</p> </li> <li> <p><strong><code>filename</code></strong>: The name of the file from which the mention was extracted. Example: biblio-1000005.</p> </li> <li> <p><strong><code>mention_class</code></strong>: Categories or semantic tags assigned to the mention, describing its type or context in the text. Example: ['ENFERMEDAD', 'SINTOMA'].</p> </li> <li> <p><strong><code>codes_esco</code></strong>: The normalized ontology codes from the European Skills, Competences, Qualifications, and Occupations (ESCO) vocabulary for the identified mention (if applicable). This field may be empty if no ESCO mapping exists. Example: 30629002.</p> </li> <li> <p><strong><code>terms_esco</code></strong>: The human-readable terms from the ESCO ontology corresponding to the <code>codes_esco</code>. Example: ['responsable de recursos', 'director de recursos', 'directora de recursos'].</p> </li> <li> <p><strong><code>codes_ncbi</code></strong>: The normalized ontology codes from the NCBI Taxonomy vocabulary for species (if applicable). This field may be empty if no NCBI mapping exists.</p> </li> <li> <p><strong><code>terms_ncbi</code></strong>: The human-readable terms from the NCBI Taxonomy vocabulary corresponding to the <code>codes_ncbi</code>. Example: ['Lacandoniaceae', 'Pandanaceae R.Br., 1810', 'Pandanaceae', 'Familia'].</p> </li> <li> <p><strong><code>codes_sct</code></strong>: The normalized ontology codes from SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) vocabulary for diseases, symptoms, drugs, locations, occupations, procedures, and species (if applicable). Example: 22232009.</p> </li> <li> <p><strong><code>terms_sct</code></strong>: The human-readable terms from the SNOMED CT ontology corresponding to the <code>codes_sct</code>. Example: ['adjudicador de regulaciones del seguro nacional'].</p> </li> <li> <p><strong><code>sct_sem_tag</code></strong>: The semantic category tag assigned by SNOMED CT to describe the general classification of the mention. Example: environment.</p> </li> </ul> <p> </p> <p><strong>Suggestion</strong>: If you load the dataset using python, it is recommended to read the columns containing lists as follows</p> <div> <blockquote> <div>import ast</div> <div>df["mention_class"] = df["mention_class"].apply(lambda x: ast.literal_eval(x) if isinstance(x, str) else x)</div> </blockquote> </div> <p> </p> <p><strong>License</strong></p> <p>This dataset is licensed under <strong>Creative Commons Attribution 4.0 International (CC BY 4.0)</strong>. This means you are free to:</p> <ul> <li>Share: Copy and redistribute the material in any medium or format.</li> <li>Adapt: Remix, transform, and build upon the material for any purpose, even commercially.</li> </ul> <p><strong>Attribution Requirement</strong>: Please credit the dataset creators appropriately, provide a link to the license, and indicate if changes were made.</p> <p><strong>Contact</strong></p> <p>If you have any questions or suggestions, please contact us at:</p> <p>Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p> <p><strong>Additional resources and corpora</strong></p> <p>If you are interested, you might want to check out these corpora and resources:</p> <ul> <li><a href="https://zenodo.org/records/5602914">MESINESP-2</a> (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)</li> <li><a href="10.5281/zenodo.5070540" target="_blank" rel="noopener">MEDDOPROF corpus </a></li> <li> <p><a href="https://zenodo.org/record/4722741">Codes Reference List</a> (for MEDDOPROF-NORM)</p> </li> <li> <p><a href="https://zenodo.org/record/4720833">Annotation Guidelines</a></p> </li> <li> <p><a href="https://doi.org/10.5281/zenodo.4524658">Occupations Gazetteer</a></p> </li> </ul>
Gazetteer and map of place names in Eustorgio y Clorilene (1629)
<p>Output data for the project "Beyond the canon: the Byzantine genre in the Spanish baroque literature".</p> <p>The dataset contains a gazetteer of places (CSV) and a self-contained HTML file with a map of the places (frequencies and proximity) of the novel <em>Eustorgio y Clorilene</em> (1629).</p> <p>The dataset has been produced during a research visit at the Université de Genève funded by the Swiss National Science Foundation (SNSF).</p>
Gazetteer_HTO: A Knowledge Graph for representing the Gazetteers of Scotland (1803-1901) following Heritage Textual Ontology
<p>This Knowlege Graph represents the information of the "Gazeteers of Scotland<strong>"</strong> (years: 1803 - 1901) collection in RDF (ttl format). This collection comprises twenty volumes of the most popular descriptive gazetteers of Scotland in the 19th century. Principal places in Scotland, including towns, counties, castles, glens, antiquities and parishes, are listed alphabetically. Each entry includes detailed historical and geographical information about each place. The raw dataset is provided by the NLS in this <a href="https://data.nls.uk/data/digitised-collections/gazetteers-of-scotland/">link</a>. As other NLS data collections, they are originally provided using two XMLs schemas: METS for descriptive, structural, technical and administrative metadata (Title, Author, Publisher, etc); and ALTO for encoding the OCR text of a page.</p> <p>In this work, we have extracted the information from METS and ALTO XMLS using <a href="https://github.com/francesNLP/defoe">defoe</a> tool. The KG uses the <a href="https://w3id.org/hto">HTO</a> to represent the information extracted. Furthermore, during the information extraction phase, we have employed several techniques to mitigate two common OCR errors: long-S and the line-break hyphenation.</p>
AI4PROFHEALTH - Automatic Occupations Gazetteer and Occupations Co-occurrence with Clinical Concepts
<div> <div>This dataset comprises an occupations gazetteer generated with automatically extracted terminology from the Mesinesp2 corpus, a manually annotated corpus in which domain experts have labeled a set of scientific literature, clinical trials, and patent abstracts, as well as clinical case reports. In addition, this dataset also includes the co-occurrences among occupations, and of professions with other clinical concepts that have been extracted automatically, including diseases, procedures, symptoms, species, drugs, and neoplasia morphologies. </div> <br> <div>The repository contains a .zip file for all the results obtained from Mesinesp2 and another for the clinical cases, both containing a .tsv file for the professions gazetteer, one for the professions internal co-occurrence, and one for the professions co-occurrence with other semantic classes:</div> </div> <ul> <li><strong>mesinesp2_profession_gazetteer_and_cooccurrence.zip (Mesinesp2)</strong> <ul> <li>mesinesp2_professions_gazetteer.tsv</li> <li>mesinesp2_professions_cooccurrences.tsv</li> <li>mesinesp2_professions_cooccurrences_with_other_classes.tsv</li> </ul> </li> </ul> <ul> <li><strong>clinicalcases_profession_gazetteer_and_cooccurrence.zip (clinical cases)</strong> <ul> <li>clinicalcases_professions_gazetteer.tsv</li> <li>clinicalcases_professions_cooccurrences.tsv</li> <li>clinicalcases_professions_cooccurrences_with_other_classes.tsv</li> </ul> </li> </ul> <p>The gazetteer is divided into two columns, one with the name of the extracted terms and the other one with their total count.</p> <p>The professions co-occurrences .tsv file is divided into three columns. The first two columns contain the professions that co-occur, and the third column, named "count," indicates the number of co-occurrences calculated at the document level for each pair of detected entities. The professions co-occurrences with other classes .tsv file is structured in a similar manner, where the first column contains professions, the second one the clinical concepts that they co-occur with, the third one the counts of co-occurrences, and the fourth one the class to which the clinical concept belongs.</p> <p><strong>License</strong></p> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> <p><strong>Contact</strong></p> <p>If you have any questions or suggestions, please contact us at:</p> <p>- Sergi Marsol Torrent (<sergi [dot] marsol [at] bsc [dot] es>)<br>- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p> <p><strong>Additional resources and corpora</strong></p> <p>If you are interested, you might want to check out these corpora and resources:</p> <ul> <li><a href="https://zenodo.org/records/5602914">MESINESP-2</a> (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)</li> <li><a href="10.5281/zenodo.5070540" target="_blank" rel="noopener">MEDDOPROF corpus </a></li> <li><a href="https://zenodo.org/record/4720833">Annotation Guidelines</a></li> </ul> <p><strong>Acknowledgements</strong></p> <p>This resource been funded by the Spanish National Proyectos I+D+i 2020 AI4ProfHealth project PID2020-119266RA-I00 (<strong>PID2020-119266RA-I0/AEI/10.13039/501100011033).</strong></p>
Beta maṣāḥǝft: Gazetteer
<p>These is a dump of the <a href="https://www.betamasaheft.uni-hamburg.de/">Beta maṣāḥǝft:</a> Gazetteer of places in Ethiopia provided to the <a href="https://pelagios.org/">Pelagios Network </a>from the <a href="https://www.betamasaheft.uni-hamburg.de/">Beta maṣāḥǝft: Manuscripts of Ethiopia and Eritrea (Schriftkultur des christlichen Äthiopiens und Eritreas: eine multimediale Forschungsumgebung</a> project. </p> <p>Annotations in the <a href="https://doi.org/10.5281/zenodo.3533134">Beta maṣāḥǝft - Pelagios Network Annotations</a> point to resources which are described here.</p> <p>The annotations follow the old <a href="https://github.com/pelagios/pelagios-cookbook/wiki/Joining-Pelagios">Pelagios Gazetteer I</a><a href="https://github.com/pelagios/pelagios-cookbook/wiki/Pelagios-Gazetteer-Interconnection-Format">nterconnection format</a> now surpassed by <a href="https://github.com/LinkedPasts/linked-places">Linked Places</a> format and are validated with <a href="http://peripleo.no5.at/validator">http://peripleo.no5.at/validator</a></p> <p>The entries in the Gazetteer are produced by all <a href="https://www.betamasaheft.uni-hamburg.de/">Beta maṣāḥǝft:</a><a href="https://betamasaheft.eu/team.html"> contributors</a>, although the responsibility for the correctness of the present dataset is of Pietro Liuzzo.</p>
FIGURE 1 in Two centuries of ornithological exploration of the Rio Doce Basin, southeastern Brazil. Part II-A catalogue of its birds and a gazetteer
FIGURE 1. Map of the study area, depicting the Rio Doce Hydrographic Basin and the Rio Barra Seca Hydrographic Basin, southeastern Brazil. Main reserves are depicted in green, rivers in blue, and main cities (above 50,000 inhabitants) in black. The red star depicts the location of the tailing dam that collapsed in Bento Rodrigues in 2015.
FIGURE 3 in Two centuries of ornithological exploration of the Rio Doce Basin, southeastern Brazil. Part II-A catalogue of its birds and a gazetteer
FIGURE 3. Sampling effort in the Rio Doce Basin, southeastern Brazil, measured as the number of bird records per each 0.25° x 0.25° grid cell, irrespective of the sampling method used. Note that several cells have never been sampled for birds, while a few cells have been heavily sampled.
FIGURE 2 in Two centuries of ornithological exploration of the Rio Doce Basin, southeastern Brazil. Part II-A catalogue of its birds and a gazetteer
FIGURE 2. Percentage of bird records for the Rio Doce Basin, southeastern Brazil, that we considered to be erroneous per decade (data from appendixes C and D). Numbers above bars indicate the absolute number of erroneous records per decade. When the publication did not specify the year when the record was made, we attributed it to the decade when the fieldwork was supposed to have been done or, when not available, to the decade of the publication. Records for the 19th century were pooled together.
Medical students Gazette database 2020
<p>Excel database of the University of Manchester Medical Students Gazette 1898-2009. Including various titles: <em>MRI Students Gazette</em>; <em>Manchester Medical Students Gazette; Manchester University Medical Students Gazette; Manchester Medical Gazette </em>and<em> Mediscope</em></p> <p>The database includes volume details, the title or topic of the articles, author, subject and brief information The database includes photographs, cartoons and illustrations.</p> <p>The <em>Gazettes</em> cover a wide range of medical topics, articles about medical students and medical education.</p> <p>To use: click onto any blank space and press 'Control F', 'find all', enter a search word or phrase, e,g, 'Stopford' or cardiology'.</p>
FIGURE 13. Louteridium donnell-smithii. Plate 7 from Botanical Gazette, vol. 14 in Louteridium (Acanthaceae: Acanthoideae: Ruellieae: Trichantherinae): Taxonomy, Phylogeny, Reproductive Biology, and Conservation
FIGURE 13. Louteridium donnell-smithii. Plate 7 from Botanical Gazette, vol. 14 (1889), drawn by C.E. Faxon. From the legend on page 29 of Donnell-Smith's article, "Undescribed plants from Guatemala. V:" "Fig. 1. Cyme and leaf. Fig. 2. Flower with calyx removed and corolla laid open. Fig. 3. Vertical section of ovary. Fig. 4. Ovule. Fig. 5. Capsule divided transversely. Fig. 6. Dehiscent capsule. Fig. 7. One valve showing placenta and retinacula. Fig. 8. Seed divided transversely. Fig. 9 Embryo. Fig. 10. Diagram of flower."
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