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12,346 results for “derivatives”
LAMASUS NUTS-level agricultural data derived from public FADN 1989-2009 (SGM) & 2004-2020 (SO)
<p>This dataset comprises spatial and temporal agricultural data compiled from the Farm Accountancy Data Network (FADN) Public Database available at the FADN-region level and further disaggregated using Corine Land Cover (CLC) information about agricultural area. <br><br><strong>Processing to NUTS level:</strong></p> <p>CLC data layers were used to overlay what is defined as "Agricultural areas" in CLC level 1classification (#2**) with FADN and NUTS regions. The overlay allows to calculate area-weighted shares and further to allocate FADN farm weights to the NUTS level. This allows the application of weights at the NUTS granularity. Please keep in mind, this is only possible under the assumption of heterogeneous farms within the FADN region. <br><br><strong>File description:</strong></p> <p>The dataset consists of eight files, corresponding to four different levels of NUTS coding (NUTS 0-3) according to the 2016 NUTS specification and each of those for the two different sampling periods. <br>FADN data from 2004 onwards, standard results calculated for farms grouped according to EU typology of agricultural holdings based on standard output (SO). <br>FADN data from 1989 to 2009, standard results calculated for farms grouped according to EU typology of agricultural holdings based on standard gross margin (SGM). </p> <p>For each csv file, the following columns are included: <br><br>Identifier:</p> <ol> <li>NUTS Code: The unique identifier for the NUTS (2016) region</li> <li>Year: The year of the data point</li> </ol> <p>Variables:</p> <p> 3. weighting: number of farms represented<br> 4. and after: SE standard result variables, for detailed description, please have a look at the accompanied xlsx: variable_description_zenodo.xlsx</p> <p><strong>Source information:</strong></p> <p>The raw data for the public Farm Accountancy Data Network (FADN) can be accessed through the official platform using the following link: <a href="https://agridata.ec.europa.eu/extensions/FADNPublicDatabase/FADNPublicDatabase.html" target="_new">FADN Public Database</a>.</p> <p>The CLC layers for the weighting of the spatial disaggregation can be accessed via the Copernicus homepage undern the following link: <a href="https://land.copernicus.eu/en/products/corine-land-cover">https://land.copernicus.eu/en/products/corine-land-cover</a></p> <p> </p> <div> <p>This dataset has been created as part of LAMASUS Project under the scope of Deliverable 3.2 titled "Database on EU policies and payments for agriculture, forest, and other LUM related drivers ". The data is directly linked to the work described on pages 50-57, belonging to section 3.6 Public FADN Data. The full text of the deliverable can be accessed via: <a href="https://www.lamasus.eu/wp-content/uploads/LAMASUS_D3.2_policy-and-payment-database.pdf">https://www.lamasus.eu/wp-content/uploads/LAMASUS_D3.2_policy-and-payment-database.pdf.</a></p> </div>
UAV-derived DEM and DOM along the co-seismic surface ruptures produced by the Mw5.7 aftershock during the 22-01-2024, Mw7.0 Wushi earthquake, Xinjiang, China
<p>This unmanned aerial vehicle (UAV) dataset was acquired by a DJI Matrice 300 RTK and a DJI Phantom 4 Pro, on February 3 and 5, 2024, respectively. The Digital Elevation Model (DEM) and Digital Orthophoto Map (DOM) were processed using the Agisoft Metashape Professional software. These data were used to map the co-seismic surface ruptures produced by the 29-01-2024, Mw5.7 aftershock following the 22-01-2024 Mw7.0 Wushi mainshock, Xinjiang, China, and to measure the associated vertical offsets along the surface ruptures.</p>
Neither alpha-synuclein-preformed fibrils derived from patients with GBA1 mutations nor the host murine genotype significantly influence seeding efficacy in the mouse olfactory bulb
<p>Data sets for;</p> <p>Neither alpha-synuclein-preformed fibrils derived from patients with <em>GBA1</em> mutations nor the host murine genotype significantly influence seeding efficacy in the mouse olfactory bulb</p>
CLDF dataset derived from the DoReCo core corpus
<p>Cite the source of the dataset as:</p> <blockquote> <p>Seifart, Frank, Ludger Paschen & Matthew Stave (eds.). 2022. Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). DOI:10.34847/nkl.7cbfq779</p> </blockquote> <p>Please note that when citing this dataset, it is NOT sufficient to refer to DoReCo as a whole, but the full citation for each individual corpus must be provided, including the name(s) of the creator(s) of each corpus.</p> <blockquote>Ozerov, Pavel. 2022. Anal DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/anal1239. DOI:10.34847/nkl.0dbazp8m</blockquote> <blockquote>Riesberg, Sonja. 2022. Yali (Apahapsili) DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/apah1238. DOI:10.34847/nkl.9d91nkq2</blockquote> <blockquote>Cowell, Andrew. 2022. Arapaho DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/arap1274. DOI:10.34847/nkl.36f5r1b6</blockquote> <blockquote>Cobbinah, Alexander Yao. 2022. Baïnounk Gubëeher DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/bain1259. DOI:10.34847/nkl.a332abw8</blockquote> <blockquote>Vanhove, Martine. 2022. Beja DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/beja1238. DOI:10.34847/nkl.edd011t1</blockquote> <blockquote>Seifart, Frank. 2022. Bora DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/bora1263. DOI:10.34847/nkl.6eaf5laq</blockquote> <blockquote>Reiter, Sabine. 2022. Cashinahua DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/cash1254. DOI:10.34847/nkl.a8f9q2f1</blockquote> <blockquote>Däbritz, Chris Lasse and Kudryakova, Nina and Stapert, Eugénie and Arkhipov, Alexandre. 2022. Dolgan DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/dolg1241. DOI:10.34847/nkl.f09eikq3</blockquote> <blockquote>Kazakevich, Olga and Klyachko, Elena. 2022. Evenki DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/even1259. DOI:10.34847/nkl.5e0d27cu</blockquote> <blockquote>Hellwig, Birgit. 2022. Goemai DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/goem1240. DOI:10.34847/nkl.b93664ml</blockquote> <blockquote>Harvey, Andrew. 2022. Gorwaa DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/goro1270. DOI:10.34847/nkl.a4b4ijj2</blockquote> <blockquote>Burenhult, Niclas. 2022. Jahai DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/jeha1242. DOI:10.34847/nkl.6a71xp0p</blockquote> <blockquote>Kim, Soung-U. 2022. Jejuan DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/jeju1234. DOI:10.34847/nkl.06ebrk38</blockquote> <blockquote>Vydrina, Alexandra. 2022. Kakabe DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/kaka1265. DOI:10.34847/nkl.d5aeu9t6</blockquote> <blockquote>Gusev, Valentin and Klooster, Tiina and Wagner-Nagy, Beáta and Arkhipov, Alexandre. 2022. Kamas DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/kama1351. DOI:10.34847/nkl.cdd8177b</blockquote> <blockquote>Hellwig, Birgit and Schneider-Blum, Gertrud and Ismail, Khaleel Bakheet Khaleel. 2022. Tabaq (Karko) DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/kark1256. DOI:10.34847/nkl.eea8144j</blockquote> <blockquote>Döhler, Christian. 2022. Komnzo DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/komn1238. DOI:10.34847/nkl.c5e6dudv</blockquote> <blockquote>Bartels, Hauke and Szczepański, Marcin. 2022. Lower Sorbian DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/lowe1385. DOI:10.34847/nkl.6c6e4e9k</blockquote> <blockquote>Haude, Katharina. 2022. Movima DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/movi1243. DOI:10.34847/nkl.da42xf67</blockquote> <blockquote>Ponsonnet, Maïa. 2022. Dalabon DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/ngal1292. DOI:10.34847/nkl.fae299ug</blockquote> <blockquote>Güldemann, Tom and Ernszt, Martina and Siegmund, Sven and Witzlack-Makarevich, Alena. 2022. Nǁng DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/nngg1234. DOI:10.34847/nkl.f6c37fi0</blockquote> <blockquote>Haig, Geoff and Vollmer, Maria and Thiele, Hanna. 2022. Northern Kurdish (Kurmanji) DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/nort2641. DOI:10.34847/nkl.ca10ez5t</blockquote> <blockquote>Franjieh, Michael. 2022. Fanbyak DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/orko1234. DOI:10.34847/nkl.02084446</blockquote> <blockquote>Ring, Hiram. 2022. Pnar DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/pnar1238. DOI:10.34847/nkl.5ba1062k</blockquote> <blockquote>Krifka, Manfred. 2022. Daakie DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/port1286. DOI:10.34847/nkl.efeav5l9</blockquote> <blockquote>Seifart, Frank. 2022. Resígaro DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/resi1247. DOI:10.34847/nkl.ffb96lo8</blockquote> <blockquote>Witzlack-Makarevich, Alena and Namyalo, Saudah and Kiriggwajjo, Anatol and Molochieva, Zarina. 2022. Ruuli DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/ruul1235. DOI:10.34847/nkl.fde4pp1u</blockquote> <blockquote>Xu, Xianming and Bai, Bibo. 2022. Sadu DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/sadu1234. DOI:10.34847/nkl.3db4u59d</blockquote> <blockquote>Forker, Diana and Schiborr, Nils Norman. 2022. Sanzhi Dargwa DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/sanz1248. DOI:10.34847/nkl.81934177</blockquote> <blockquote>Wegener, Claudia. 2022. Savosavo DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/savo1255. DOI:10.34847/nkl.b74d1b33</blockquote> <blockquote>Thieberger, Nick. 2022. Nafsan (South Efate) DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/sout2856. DOI:10.34847/nkl.ba4f760l</blockquote> <blockquote>Schiborr, Nils Norman. 2022. English (Southern England) DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/sout3282. DOI:10.34847/nkl.9c271u5g</blockquote> <blockquote>Avanzi, Mathieu and Béguelin, Marie-José and Corminboeuf, Gilles and Diémoz, Federica and Johnsen, Laure Anne. 2022. French (Swiss) DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/stan1290. DOI:10.34847/nkl.3520l685</blockquote> <blockquote>Teo, Amos. 2022. Sümi DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/sumi1235. DOI:10.34847/nkl.5ad4t01p</blockquote> <blockquote>Gippert, Jost. 2022. Svan DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/svan1243. DOI:10.34847/nkl.9ba054c3</blockquote> <blockquote>Bogomolova, Natalia and Ganenkov, Dmitry and Schiborr, Nils Norman. 2022. Tabasaran DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/taba1259. DOI:10.34847/nkl.ad7f97xr</blockquote> <blockquote>Mosel, Ulrike. 2022. Teop DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/teop1238. DOI:10.34847/nkl.9322sdf2</blockquote> <blockquote>Wichmann, Søren. 2022. Texistepec Popoluca DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/texi1237. DOI:10.34847/nkl.c50ck58f</blockquote> <blockquote>Rose, Françoise. 2022. Mojeño Trinitario DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/trin1278. DOI:10.34847/nkl.cbc3b4xr</blockquote> <blockquote>Griscom, Richard. 2022. Asimjeeg Datooga DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/tsim1256. DOI:10.34847/nkl.f77c7m72</blockquote> <blockquote>Schnell, Stefan. 2022. Vera'a DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/vera1241. DOI:10.34847/nkl.3e2cu8c4</blockquote> <blockquote>Michaud, Alexis. 2022. Yongning Na DoReCo dataset. In Seifart, Frank, Ludger Paschen and Matthew Stave (eds.). Language Documentation Reference Corpus (DoReCo) 1.2. Berlin & Lyon: Leibniz-Zentrum Allgemeine Sprachwissenschaft & laboratoire Dynamique Du Langage (UMR5596, CNRS & Université Lyon 2). https://doreco.huma-num.fr/languages/yong1270. DOI:10.34847/nkl.abe65p95</blockquote>
CLDF dataset derived Zalizniak et al.'s "Database of Semantic Shifts" from 2024
<p>Cite the source of the dataset as:</p> <blockquote> <p>Zalizniak A. et al (2024). Database of Semantic Shifts. Moscow: Institute of Linguistics, Russian Academy of Sciences. [Accessed on 05.02.2024]</p> </blockquote>
CLDF dataset derived from Greenhill's "TransNewGuinea.org" from 2015
<p>Cite the source of the dataset as:</p> <blockquote> <p>Greenhill, Simon J. (2015): TransNewGuinea.org: An Online Database of New Guinea Languages. PLoS ONE 10.10: e0141563.</p> </blockquote>
CLDF dataset derived from Barlow's "Numerals of the Pacific" from 2025
<p>Cite the source of the dataset as:</p> <blockquote> <p>Barlow, Russell (ed.). 2025. Numerals of the Pacific: A collection of numeral terms in Austronesian and Papuan languages.</p> </blockquote>
CLDF dataset derived from Liu et al.'s "Comparative Wordlist of Newari Varieties" from 2023
<p>Cite the source of the dataset as:</p> <blockquote> <p>Liu, Zhenyang; Jacques, Guillaume; List, Johann-Mattis (2023): Creating a Standardized Comparative Wordlist of Newari Varieties. (26/07/2023), URL: https://calc.hypotheses.org/6269.</p> </blockquote>
CLDF dataset derived from Ugarte et al.'s "NorthPeruLex - A Lexical Dataset of Small Language Families and Isolates from Northern Peru (forthcoming).
<p>Cite the source of the dataset as:</p> <blockquote> <p>Ugarte, Carlos and Blum, Frederic and Ingunza, Adriano and Gonzales, Rosa and Peña, Jaime. Forthcoming. NorthPeruLex - A Lexical Dataset of Small Language Families and Isolates from Northern Peru.</p> </blockquote>
CLDF dataset derived from Lee and Hasegawa's "Evolution of the Ainu Language in Space and Time" from 2013
<p>Cite the source of the dataset as:</p> <blockquote> <p>Lee Sean, Hasegawa Toshikazu (2013). Evolution of the Ainu Language in Space and Time. PLOS ONE 8(4): e62243. https://doi.org/10.1371/journal.pone.0062243</p> </blockquote>
CLDF Dataset derived from Bender et al. 2003 "Proto-Micronesian Reconstructions"
<p>Cite the source of the dataset as:</p> <blockquote> <p>Byron W. Bender, Ward H. Goodenough, Frederick H. Jackson, Jeffrey C. Marck, Kenneth L. Rehg, Ho-min Sohn, Stephen Trussel, and Judith W. Wang. 2003. Proto-Micronesian Reconstructions—1. Oceanic Linguistics Vol. 42(1), 1-110, DOI: 10.2307/3623449</p> <p>Byron W. Bender, Ward H. Goodenough, Frederick H. Jackson, Jeffrey C. Marck, Kenneth L. Rehg, Ho-min Sohn, Stephen Trussel, and Judith W. Wang. 2003. Proto-Micronesian Reconstructions—2. Oceanic Linguistics Vol. 42(2), 271-358, DOI: 10.2307/3623243</p> </blockquote>
BALTRAD_VPTS - Vertical profiles of biological targets derived from European weather radars
<p><em>BALTRAD_VPTS - Vertical profiles of biological targets derived from European weather radars</em> is a vertical profile time series dataset published by the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal movement data derived from 151 European weather radars in 18 countries, with varying coverage from 2012 to 2023. These data were created by processing weather radar data - provided by the Operational Programme for the Exchange of Weather Radar Information (<a href="https://www.eumetnet.eu/activities/observations-programme/current-activities/opera/">OPERA</a>) - with methods optimized for extracting bird targets. The resulting data are vertical profile time series (VPTS), containing the density, speed and direction of biological targets within a weather radar (<code>radar</code>) volume, grouped into altitude bins (<code>height</code>) and measured over time (<code>datetime</code>). The data are also available in the <a href="https://aloftdata.eu/browse/?prefix=baltrad/">Aloft bucket</a>.</p> <div> <div>See Desmet et al. (2025, <a href="https://doi.org/10.1038/s41597-025-04641-5">https://doi.org/10.1038/s41597-025-04641-5</a>) for a more detailed description of this dataset.</div> </div> <h2>Files</h2> <p>VPTS data in this deposit are organized per country (.tgz file), radar (directory), year (directory) and month (.csv.gz file). Fields in the data follow the <a href="https://aloftdata.eu/vpts-csv/">VPTS CSV</a> format and are described in <code>vpts-csv-table-schema.json</code>. An overview of what data are available is provided in <code>coverage.csv</code>. Radar metadata can be found at <a href="https://aloftdata.eu/radars/">https://aloftdata.eu/radars/</a>.</p> <ul> <li><strong>coverage.csv</strong>: coverage of the VPTS data, representing the number of unique hours, heights, source files and records for each radar and date combination.</li> <li><strong>vpts-csv-table-schema.json</strong>: technical description of the fields in the VPTS data.</li> <li><strong>be.tgz</strong>: VPTS data from 2 radars in Belgium.</li> <li><strong>ch.tgz</strong>: VPTS data from 5 radars in Switzerland.</li> <li><strong>cz.tgz</strong>: VPTS data from 2 radars in Czechia.</li> <li><strong>de.tgz</strong>: VPTS data from 20 radars in Germany.</li> <li><strong>dk.tgz</strong>: VPTS data from 5 radars in Denmark.</li> <li><strong>ee.tgz</strong>: VPTS data from 2 radars in Estonia.</li> <li><strong>es.tgz</strong>: VPTS data from 15 radars in Spain.</li> <li><strong>fi.tgz</strong>: VPTS data from 13 radars in Finland.</li> <li><strong>fr.tgz</strong>: VPTS data from 26 radars in France.</li> <li><strong>hr.tgz</strong>: VPTS data from 7 radars in Croatia.</li> <li><strong>il.tgz</strong>: VPTS data from 1 radar in Israel.</li> <li><strong>nl.tgz</strong>: VPTS data from 3 radars in the Netherlands.</li> <li><strong>no.tgz</strong>: VPTS data from 11 radars in Norway.</li> <li><strong>pl.tgz</strong>: VPTS data from 8 radars in Poland.</li> <li><strong>pt.tgz</strong>: VPTS data from 3 radars in Portugal.</li> <li><strong>se.tgz</strong>: VPTS data from 22 radars in Sweden.</li> <li><strong>si.tgz</strong>: VPTS data from 2 radars in Slovenia.</li> <li><strong>sk.tgz</strong>: VPTS data from 4 radars in Slovakia.</li> </ul> <h2>Acknowledgements</h2> <p>This dataset was processed using infrastructure provided by the University of Amsterdam, SURF Cooperative, Ghent University and the Research Institute for Nature and Forest (INBO). It was mainly supported by the <a href="https://globam.science/">GloBAM project</a>, funded through the 2017-18 Belmont Forum and BiodivERsA joint call for research proposals under the BiodivScen ERA-Net COFUND programme.</p>
UVA_VPTS - Vertical profiles of biological targets derived from weather radars in Belgium, Germany and the Netherlands
<p><em>UVA_VPTS - Vertical profiles of biological targets derived from weather radars in Belgium, Germany and the Netherlands</em> is a vertical profile time series dataset published by the <a href="https://www.inbo.be/en">Research Institute for Nature and Forest (INBO)</a>. It contains animal movement data derived from 24 weather radars in Belgium, Germany and the Netherlands, with varying coverage from 2008 to 2023. These data were created by processing weather radar data - provided by the Royal Meteorological Institute of Belgium (<a href="https://www.meteo.be/">RMI</a>), German Meteorological Service (<a href="https://www.dwd.de/">DWD</a>) and Royal Netherlands Meteorological Institute (<a href="https://www.knmi.nl/">KMNI</a>) - with methods optimized for extracting bird targets. The resulting data are vertical profile time series (VPTS), containing the density, speed and direction of biological targets within a weather radar (<code>radar</code>) volume, grouped into altitude bins (<code>height</code>) and measured over time (<code>datetime</code>). The data are also available in the <a href="https://aloftdata.eu/browse/?prefix=uva/">Aloft bucket</a>.</p> <p>See Desmet et al. (2025, <a href="https://doi.org/10.1038/s41597-025-04641-5">https://doi.org/10.1038/s41597-025-04641-5</a>) for a more detailed description of this dataset.</p> <h2>Files</h2> <p>VPTS data in this deposit are organized per country (.tgz file), radar (directory), year (directory) and month (.csv.gz file). Fields in the data follow the <a href="https://aloftdata.eu/vpts-csv/">VPTS CSV</a> format and are described in <code>vpts-csv-table-schema.json</code>. An overview of what data are available is provided in <code>coverage.csv</code>. Radar metadata can be found at <a href="https://aloftdata.eu/radars/">https://aloftdata.eu/radars/</a>.</p> <ul> <li><strong>coverage.csv</strong>: coverage of the VPTS data, representing the number of unique hours, heights, source files and records for each radar and date combination.</li> <li><strong>vpts-csv-table-schema.json</strong>: technical description of the fields in the VPTS data.</li> <li><strong>be.tgz</strong>: VPTS data from 3 radars in Belgium.</li> <li><strong>de.gz</strong>: VPTS data from 18 radars in Germany.</li> <li><strong>nl.gz</strong>: VPTS data from 3 radars in the Netherlands.</li> </ul> <h2>Acknowledgements</h2> <p>This dataset was processed using infrastructure provided by the University of Amsterdam, SURF Cooperative, Ghent University and the Research Institute for Nature and Forest (INBO). It was mainly supported by the <a href="https://globam.science/">GloBAM project</a>, funded through the 2017-18 Belmont Forum and BiodivERsA joint call for research proposals under the BiodivScen ERA-Net COFUND programme.</p>
Dataset for manuscript Tracing Quartz Provenance: A Multi-Disciplinary Investigation of Luminescence Sensitisation Mechanisms of Quartz from Granite Source Rocks and Derived Sediments
<p><span>Quartz optically stimulated luminescence (OSL) sensitivity as well as some electron spin resonance (ESR) and cathodoluminescence (CL) signals have been empirically proposed as provenance indicators. Sensitivity is defined as luminescence emitted in response to a given dose per unit mass. While it is largely believed to be acquired by earth surface processes, recent studies bring evidence that sensitisation processes depend on source geology.</span></p> <p><span>Here we combine OSL and thermoluminescence (TL), ESR and CL analyses to understand the mechanisms of quartz OSL sensitisation. We investigate granites and their derived sediments from catchments draining simple lithologies of known age that display contrasting OSL sensitisation behaviour both in nature and during irradiation and light exposure laboratory experiments. The sample displaying increased OSL sensitisation is characterised by TL emission at intermediate temperatures (150-250 °C), Ti-related signals in CL, and Ti and Ge lithium compensated signals in ESR. <span>The insensitive samples either lack or exhibit very weak such characteristics and contain several times less amount of trace titanium measured by </span></span><span>laser ablation inductively coupled plasma mass spectrometry (</span><span>LA-ICP-MS).</span></p> <p><span>We demonstrate that the OSL sensitisation results as an effect of the existence of certain defects and impurities in the quartz crystal in the parent rock, such as titanium and germanium. However, the degree of sensitisation reached in nature is significantly higher than in the laboratory. <span> </span>As such, the existence of this precursor represents the potential for sensitisation, which can later be amplified by environmental factors during sedimentary history.</span></p>
Data for: Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and multi-scale material modeling
<h2>Description</h2> <p>DATA REPOSITORY FOR</p> <p>Title: Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and <br> multi-scale material modeling<br>By: Eva Jägle, Jithender J. Timothy, Daniel Jansen, Alisa Machner<br>Accepted by: Cement and Concrete Research</p> <p>This dataset presents the data of the paper 'Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and multi-scale material modeling' submitted to and accepted by Cement and Concrete Research. The dataset follows the structure of the paper such that the calculations described therein can be reproduced.</p> <p>Data is available on three types of cement: Two ordinary Portland cements of different grinding fineness (CEM I 42.5 R und CEM I 52.5 R) and one limestone-containing blended cement (CEM II/A-LL 42.5 R). The data refer to the first 24 hours of hydration and temperature conditions of 20°C (for CEM I 42.5 R, CEM I 52.5 R, CEM II/A-LL 42.5 R) and 35°C (for CEM I 52.5 R). All data were retrieved for cement pastes with a water-to-cement ratio of 0.45.</p> <p>The dataset contains raw and processed data from quantitative X-ray diffraction, 5PL cement dissolution fitting, thermodynamic simulation with GEMS, multi-scale material modeling, ultrasonic testing and Vicat penetration tests. The data is mainly available in .xlsx files together with short descriptions in ReadMe.txt files.</p>
Shoulder kinematics derived from radiographic and optical motion analysis
<p>This dataset contains torso/arm, scapula, and humerus kinematics from subjects performing a variety of static poses and dynamic activities. The humerus and scapula were imaged at 100 Hz using a biplane fluoroscopy/dynamic stereoradiography system. Then, 3D models of the humerus and scapula were constructed from each subject’s CT scan. Model-based markerless tracking ascertained the 3D position and orientation of each bone model by semi-automatically aligning digitally reconstructed radiographs against each frame of the radiographic recordings. The kinematics of the torso and arm were measured using skin marker motion capture and co-calibrated spatially and temporally to the radiography system.</p> <p>This repository contains an expanded release of data found in doi:10.5281/zenodo.7542486 and doi:10.5281/zenodo.10972005. The rationale to provide a new repository is that this release, and forthcoming releases, will follow a new format that provides more granular data for past and ongoing studies from our laboratory. These studies may include motion analysis data from healthy controls, pathologic subjects, and those after surgical intervention.</p> <p>v1.1 now contains transforms from Vicon to biplane fluoro coordinate systems.</p> <p> </p>
CLDF dataset derived from Deepadung et al.'s "Lexical Comparison of Palaung Dialects" from 2015
<p>Cite the source of the dataset as:</p> <blockquote> <p>Deepadung, Sujaritlak; Buakaw, Supakit; and Rattanapitak, Ampica (2015): A lexical comparison of the Palaung dialects spoken in China, Myanmar, and Thailand. Mon-Khmer Studies 44. 19-38.</p> </blockquote>
CLDF dataset derived from Hartmann et al.'s "Valency Patterns Leipzig" from 2013
<p>Cite the source of the dataset as:</p> <blockquote> <p>Hartmann, Iren & Haspelmath, Martin & Taylor, Bradley (eds.) 2013. Valency Patterns Leipzig. Leipzig: Max Planck Institute for Evolutionary Anthropology. (Available online at http://valpal.info)</p> </blockquote>
Dataset for Paper "Towards Increased Diversity in STEM Education: Five archetypes Derived through a Data-Driven Approach Examining a Computer Science Student Cohort
<p># Dataset for Paper "Towards Increased Diversity in STEM Education: Five archetypes Derived through a Data-Driven Approach Examining a Computer Science Student Cohort" - Rev #1</p> <p>This is the dataset for the paper titled "Towards Increased Diversity in STEM Education: Five archetypes Derived through a Data-Driven Approach Examining a Computer Science Student Cohort".</p> <p>In case of questions, feel free to contact the authors, *anonymised*, ORCID: https://orcid.org/*anonymised*, current affiliation and email: *anonymised*</p> <p>## Survey 2019 ##<br> The raw survey data for the initial 2019 survey is available in the file *survey2019_anon.csv*. Note that the data is anonymised as free-text comments have been removed. Explanations on the variables and their levels are given in the files *variables_survey2019.csv* and *values_survey2019.csv*.<br> The questionnaire for the 2019 survey is contained in *survey2019_instrument.pdf*.</p> <p>## Survey 2020 ##<br> The raw survey data for the 2020 survey is available in the file *rdata_anon_survey2020.csv*. Additional scripts are supplied to reproduce the exploratory factor analysis. The main entry is the file *EFA.R*, which imports the data. The file contains some comments on the process.<br> The questionnaire for the 2020 survey is contained in *survey2020_instrument.pdf*.</p> <p>## Interviews ##<br> The interview guide used for the five interviews is available in the file *interview_instrument.pdf*.</p>
Bioactive fatty-acid derivated ovalicin from Pseudallescheria boydii
<p>Original data for the mMolecular network with t-SNE representation of crude extracts of <em>P. boydii</em>. SNB-CN71, -CN73, -CN81 and -CN85</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.