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2,335 results for “Fars”
Far-infrared to millimeter data of protoplanetary disks: dust growth in the Taurus, Ophiuchus, and Chamaeleon I star-forming regions
<p>This repository contains the data set presented in the manuscript "Far-infrared to millimeter data of protoplanetary disks: dust growth in the Taurus, Ophiuchus, and Chamaeleon I star-forming regions" (Ribas et al. 2017), and includes a table with several sample properties (e.g. stellar properties, Herschel photometry, different spectral indices), spectral energy distributions, Spitzer/IRS and Herschel/SPIRE spectra, the median SEDs of Taurus, Ophiuchus and Chamaeleon I, and the Herschel maps used.</p> <p>ERRATUM: three Chamaeleon I sources (Hn 11, T45a, and WY Cha) were mislabeled in the original version of the manuscript, which resulted in their names, stellar parameters, extinction values, infrared slopes, and silicate feature properties being assigned to incorrect coordinates. Because the photometry and spectroscopy presented in the original article is coordinate- based, the provided SEDs and spectra were also missmatched: the data files labeled Hn 11 in the original manuscript correspond to T45a, those labeled T45a correspond to WY Cha, and those labeled WY Cha correspond to Hn 11. Additionally, due to a mislabeling issue in Manoj et al. 2011, the source formerly labeled UX Cha is actually CHSM 8284. Therefore, stellar parameters and photometry labeled UX Cha in our original manuscript correspond to CHSM 8284 The updated version of the repository fixes the issue both in the sample.csv file and in the individual SED and Spitzer/IRS spectra files. The published erratum is available here: <a href="https://iopscience.iop.org/article/10.3847/1538-4357/abb66e">https://iopscience.iop.org/article/10.3847/1538-4357/abb66e</a>.</p>
Spectral Units for Tsiolkovskiy crater (Moon, Far side)
<p>Spectral Units derived from the Moon Mineralogy Mapper (M3) data for the lunar far side Tsiolkovskiy crater.</p>
Reproduction package for paper "How far are we from reproducible research on code smell detection? A systematic literature review"
<p>Checklist and data extracted from publications analyzed for "How far are we from reproducible research on code smell detection? A systematic literature review" paper, together with processing scripts and calculations of Cohen's Kappa.</p> <p>Paper that describes details of the data is available here: https://doi.org/10.1016/j.infsof.2021.106783</p>
MIGR-TWIT Corpus. Migration Tweets of right and far-right politics in Europe
<p><strong>Description</strong></p> <p>The <strong>MIGR-TWIT Corpus</strong> is a multilingual corpus of tweets about the topic of migration in Europe. Within the framework of the collaborative research project OLiNDiNUM (Observatoire LINguistique du DIscours NUMérique, Linguistic Observatory of Online Debate) the MIGR-TWIT Corpus is created with the aim of developing language databases of online debate. Considering the global issue of migration in line with British and French political contexts of last dozen years from 2011 to 2022, the corpus consists of two sub-corpora: </p> <ul> <li> <p><strong>FR-R-MIGR-TWIT-2011-2022 Corpus </strong>for French language data (1 January 2011 - 30 June 2022) and </p> </li> <li> <p><strong>UK-R-MIGR-RA-TWIT-2012-2022 Corpus </strong>for English language data (1 January 2012 - 5 September 2022) <strong> </strong></p> </li> </ul> <p>Using the Twitter API v2 Academic Research, tweets containing at least one occurrence of migration or refugee related words are retrieved automatically from 28 right and far-right political figures and parties. The whole corpus contains 18,233 tweets and 533,198 words. </p> <p><strong>Scientific reference:</strong></p> <p>Pietrandrea, P., Battaglia, E. (2022). “Migrants and the EU”. The diachronic construction of ad hoc categories in French far-right discourse. Journal of Pragmatics 192, 139-157.</p> <p>Blandino, G. (2023). <em>10 years of public debate on immigration: combining topic modeling and corpus linguistics to examine the British (far-)right discourse on Twitter</em>, MA University of Wolverhampton</p> <p>Jeon, S. (2025). Le discours numérique sur l'immigration en France entre 2011 et 2022. Une analyse de corpus (Online Discourse on Immigration in France between 2011 and 2022. A Corpus Analysis), PhD Thesis, Université de Lille, France.</p> <p><strong>Contents</strong></p> <p>The whole corpus contains two CSV Zip files (tabular format) corresponding to each sub-corpus. The complete corpus is presented in two versions, one version with the tweet identifier (<strong><em>data__id</em></strong>) and the text of the tweet (<strong><em>data__text</em></strong>) as a header (folders named <em>FR-R-MIGR-TWIT-2011-2022_textonly</em> and <em>UK-R-MIGR-RA-TWIT-2012-2022_textonly</em>, respectively composed of 12 and 11 Zip files of every single year), and the other version with all tweet fields information included as a header, such as the posting date (<em><strong>data__created__at</strong></em>), the username (<strong><em>author__name</em></strong>), the number of retweets (<em><strong>data__public_metrics__retweet_count</strong></em>), etc., with two folders named <em>FR-R-MIGR-TWIT-2011-2022_meta</em> and <em>UK-R-MIGR-RA-TWIT-2012-2022_meta</em>. Detailed information for each sub-corpus is illustrated below.</p> <p><strong>1. FR-R-MIGR-TWIT-2011-2022 </strong></p> <ul> <li><strong>Created at: </strong>2022-08-08</li> <li> <p><strong>Language: </strong>FR<strong> </strong></p> </li> <li> <p><strong>Coverage: </strong>16 user accounts; 11,761 tweets; 358,491 words</p> </li> <li> <p><strong>Time of data collection: </strong>start=2011-01-01; end=2022-06-30 </p> </li> <li> <p><strong>Keywords: </strong>words derived from a latin root “<em><strong>migr</strong></em>” of <em>migrare</em></p> </li> <li> <p><strong>Corpus composition: </strong></p> </li> </ul> <table> <tbody> <tr> <th> </th> <th>Political figure/party</th> <th>Username</th> <th>Tweets</th> <th>Year concerned</th> </tr> <tr> <th>1</th> <td>Michel Barnier</td> <td>@MichelBarnier</td> <td>31</td> <td>2017-22</td> </tr> <tr> <th>2</th> <td>Valérie Pécresse</td> <td>@vpecresse</td> <td>81</td> <td>2017-22</td> </tr> <tr> <th>3</th> <td>Rassemblement National</td> <td>@RNational_off</td> <td>3,347</td> <td>2017-22</td> </tr> <tr> <th>4</th> <td>Nicolas Dupont-aignan</td> <td>@dupontaignan</td> <td>663</td> <td>2011-22</td> </tr> <tr> <th>5</th> <td>Éric Ciotti</td> <td>@ECiotti</td> <td>1,007</td> <td>2012-22</td> </tr> <tr> <th>6</th> <td>Christian Estrosi</td> <td>@cestrosi</td> <td>137</td> <td>2011-22</td> </tr> <tr> <th>7</th> <td>Marine Le Pen</td> <td>@MLP_officiel</td> <td>1,650</td> <td>2011-22</td> </tr> <tr> <th>8</th> <td>Valérie Boyer</td> <td>@valerieboyer13</td> <td>837</td> <td>2012-22</td> </tr> <tr> <th>9</th> <td>Florian Philippot</td> <td>@f_philippot</td> <td>485</td> <td>2012-22</td> </tr> <tr> <th>10</th> <td>Xavier Bertrand</td> <td>@xavierbertrand</td> <td>70</td> <td>2017-22</td> </tr> <tr> <th>11</th> <td>Marion Maréchal</td> <td>@MarionMarechal</td> <td>479</td> <td>2012-17,19-22</td> </tr> <tr> <th>12</th> <td>Philippe Meunier</td> <td>@Meunier_Ph</td> <td>245</td> <td>2013-22</td> </tr> <tr> <th>13</th> <td>Jordan Bardella</td> <td>@J_Bardella</td> <td>1,095</td> <td>2013-22</td> </tr> <tr> <th>14</th> <td>Nicolas Bay</td> <td>@NicolasBay_</td> <td>1,260</td> <td>2017-22</td> </tr> <tr> <th>15</th> <td>Emmanuel Macron</td> <td>@EmmanuelMacron</td> <td>72</td> <td>2017-22</td> </tr> <tr> <th>16</th> <td>Éric Zemmour</td> <td>@ZemmourEric</td> <td>302</td> <td>2019-22</td> </tr> <tr> <th>17</th> <td>Jean Messiha*</td> <td>Banned from Twitter (since July 2021)</td> <td>-</td> <td>-</td> </tr> </tbody> </table> <ul> <li>Political figures and parties of table above are listed in chronological order according to the dates on which they posted their first tweet.</li> <li> <p><strong>*</strong>Before the launching of Twitter API v2 Academic Research, migr-tweets were collected from the database of Europresse.com including 1,453 tweets of Jean Messiha as part of the reference study (Pietrandrea & Battaglia 2022). However, the Twitter account in question has been permanently banned since July 2021. For our data collection using the Twitter API started in September 2021, we could not access this account. Therefore, we decided not to include his tweets in the FR-R-MIGR-TWIT-2011-2022 for the sake of consistency with the rest of twitter data that are automatically retrieved.</p> </li> <li> <p>The sub-corpus FR-R-MIGR-TWIT-2017-2022 is developed, annotated and analyzed as part of a doctoral thesis in progress (<a href="https://theses.fr/s360032">Jeon, 2025</a>) with the aim of studying the semantic construction of migr-lexicon over the period between 2011 and 2022. </p> </li> </ul> <p><strong> </strong></p> <p><strong>2. UK-R-MIGR-RA-TWIT-2012-2022 </strong></p> <ul> <li> <p><strong>Created at: </strong>2022-09-06</p> </li> <li> <p><strong>Language: </strong>EN</p> </li> <li> <p><strong>Coverage: </strong>12 user accounts; 6,472 tweets; 174,707 words </p> </li> <li> <p><strong>Time of data collection: </strong>start=2012-01-01; end=2022-09-05</p> </li> <li> <p><strong>Keywords: </strong>words derived from a latin root “<strong><em>migr</em></strong>” of <em>migrare </em>in addition to the keywords “<strong><em>refugee</em></strong>(<strong><em>s</em></strong>)” and “<strong><em>asylum</em></strong>”.</p> </li> <li> <p><strong>Corpus composition:</strong></p> </li> </ul> <table> <tbody> <tr> <th> </th> <th>Political figure/party</th> <th>Username</th> <th>Tweets</th> <th>Year concerned</th> </tr> </tbody> <tbody> <tr> <th>1</th> <td>David Cameron</td> <td>@David_Cameron</td> <td>32</td> <td>2012-22</td> </tr> <tr> <th>2</th> <td>Amber Rudd</td> <td>@AmberRuddUK</td> <td>29</td> <td>2012-22</td> </tr> <tr> <th>3</th> <td>Sajid Javid</td> <td>@sajidjavid</td> <td>84</td> <td>2012-22</td> </tr> <tr> <th>4</th> <td>Boris johnson</td> <td>@BorisJohnson</td> <td>80</td> <td>2015-22</td> </tr> <tr> <th>5</th> <td>Priti Patel</td> <td>@pritipatel</td> <td>304</td> <td>2012-22</td> </tr> <tr> <th>6</th> <td>UK Home Office</td> <td>@ukhomeoffice</td> <td>909</td> <td>2012-22</td> </tr> <tr> <th>7</th> <td>Nigel Farage</td> <td>@Nigel_Farage</td> <td>1,010</td> <td>2012-22</td> </tr> <tr> <th>8</th> <td>Richard Tice</td> <td>@TiceRichard</td> <td>180</td> <td>2013-22</td> </tr> <tr> <th>9</th> <td>UKIP</td> <td>@UKIP</td> <td>2,746</td> <td>2012-22</td> </tr> <tr> <th>10</th> <td>Neil Hamilton</td> <td>@NeilUKIP</td> <td>252</td> <td>2013-22</td> </tr> <tr> <th>11</th> <td>Nick Griffin</td> <td>@NickGriffinBU</td> <td>542</td> <td>2012-22</td> </tr> <tr> <th>12</th> <td>Robin Tilbrook</td> <td>@RobinTilbrook</td> <td>304</td> <td>2012-22</td> </tr> </tbody> </table> <p> </p> <ul> <li> <p>2 out of 12 accounts are official accounts belonging to the” UK Home Office” department and the “UKIP” (United Kingdom Independence Party) party. 10 out of 12 accounts are political figures’ accounts.</p> </li> <li> <p>The corpus UK-R-MIGR-RA-TWIT-2012-2022 will be exploited for the following master’s thesis: Blandino, G. (2023). <em>10 years of public debate on immigration: combining topic modeling and corpus linguistics to examine the British (far-)right discourse on Twitter</em>, MA University of Wolverhampton.</p> </li> </ul> <p> </p>
EU FAR Database: EU Funds absorbed by Romanian Municipalities 2016-2021
<p>EU funds reported by each locality (municipality) in their annual budgets execution reports. The data is processed by the authors, based on the information published by the Ministry of Development, Public Works and Administration, Directorate for Local Fiscal and Budgetary Policies.</p>
Fig. 1 in Preface: How far has Neotropical Ichthyology progressed in twenty years?
Fig. 1. Accumulative curve of valid freshwater species described by year from 1977 to 2017 (last 40 years) in the Neotropical region (based on Reis et al., 2003, and Fricke et al., 2018), showing an increased rate of species description in the beginning of the twenty-first century, and the estimate number of total valid species for the region based on Jackknife 1.
Figs 45–52 in Immature stages and biology of the enigmatic oxyporine rove beetles, with new data on Oxyporus larvae from the Russian Far East (Coleoptera: Staphylinidae)
Figs 45–52. Third instar larva of Oxyporus (P.) melanocephalus Kirschenblatt, 1938, head morphology. 45 – head, dorsal view; 46 – head, ventral view; 47 – antenna, dorsal view; 48 – mandible, dorsal view; 49 – maxilla, dorsal view; 50 – labium, dorsal view; 51 – labium, lateral view; 52 – maxilla, ventral view.
Figs 39–44 in Immature stages and biology of the enigmatic oxyporine rove beetles, with new data on Oxyporus larvae from the Russian Far East (Coleoptera: Staphylinidae)
Figs 39–44. Scanning electron micrographs of larva of Oxyporus procerus Kraatz, 1879. 39 – campaniform sensilla and setae of nasale; 40 – posterior epicranial group of sensilla; 41 – antennomeres II and III, apical sensorial complex; 42 – premental group of sensilla; 43 – campaniform sensillum, segment II of maxillary palpus; 44 – thoracic tergite I, lateral view.
Figs 63–66 in Immature stages and biology of the enigmatic oxyporine rove beetles, with new data on Oxyporus larvae from the Russian Far East (Coleoptera: Staphylinidae)
Figs 63–66. Habitat and rearing of Far East Oxyporus species. 63 – aspen-maple forest with lime-trees in a lowland of the Arboretum of the Gornotaezhnaya Station, locality of Oxyporus (P.) melanocephalus. 64 – oak forest on a hill of the Arboretum of the Gornotaezhnaya Station, locality of Oxyporus maxillosus. 65 – rearing box with the sand layer, the leaf litter and a fruit body of Laetiporus sulphureus. 66 – an egg of Oxyporus (Pseudoxyporus) melanocephalus Kirschenblatt, 1938 nested between the gills of Pholiota sp.
Figs 35–36 in Immature stages and biology of the enigmatic oxyporine rove beetles, with new data on Oxyporus larvae from the Russian Far East (Coleoptera: Staphylinidae)
Figs 35–36. Third instar larva of Oxyporus procerus Kraatz, 1879, selected body tergites. 35 – thoracic tergites I–III; 36 – abdominal tergite I.
Figs 19–27 in Immature stages and biology of the enigmatic oxyporine rove beetles, with new data on Oxyporus larvae from the Russian Far East (Coleoptera: Staphylinidae)
Figs 19–27. Scanning electron micrographs of larva of Oxyporus maxillosus Fabricius, 1775. 19 – cervical intersegmental membrane with microsetae M2, M3; 20 – M1 microseta, magnified; 21 – M3 microseta, magnified; 22 – posterior epicranial group of sensilla; 23 – posterior epicranial campaniform sensillum; 24 – ventral sensilla, head capsule; 25, 26 – campaniform sensilla missing between mesonotal setae; 27 – epipharynx with median furrow, hypopharynx with microtrichia.
Figs 15–18 in Immature stages and biology of the enigmatic oxyporine rove beetles, with new data on Oxyporus larvae from the Russian Far East (Coleoptera: Staphylinidae)
Figs 15–18. Third instar larva of Oxyporus maxillosus Fabricius, 1775, selected body tergites. 15 – thoracic tergites I–III; 16 – abdominal tergite I; 17 – apex of abdomen, dorsal view; 18 – mesothoracic leg, posterior view.
RIFIR – A Far Infrared Dataset
<p>This dataset consists of sequences acquired in an urban environment with two cameras (one Far Infrared and two color cameras in stereovision) mounted on the exterior of a vehicle.</p> <p>Training dataset: 14788 frames (containing aprox. 19000 pedestrain bounding boxes in visible spectrum and aprox. 14000 in infrared spectrum) of 138 unique pedestrians</p> <p>Testing dataset: 9373 frames ( containing aprox. 7000 pedestrain bounding boxes in visible spectrum and aprox. 6000 in infrared spectrum) of 33 unique pedestrians</p> <p>You can cite this dataset by:</p> <p>Miron, Alina Dana. "Multi-modal, Multi-Domain Pedestrian Detection and Classification: Proposals and Explorations in Visible over StereoVision, FIR and SWIR." PhD diss., 2014.</p>
Fig. 9 in A new genus of mongoliulid millipedes from the Far East of Russia, with a list of species in the family (Diplopoda, Julida, Mongoliulidae)
Fig. 9. Koiulus interruptus gen. et sp. nov., paratype, ♀, from upper course of river Ko, right vulva. A. Lateral view. B. Anterior view. C. Mesal view. D. Tip, posterior view; arrow points to unknown structure. E. Unknown structure from opercular seta. Abbreviations: BU = bursa; OP = operculum. Scale bars: A–C = 0.1 mm; D = 0.01 mm; E = 0.001 mm.
Fig. 8 in A new genus of mongoliulid millipedes from the Far East of Russia, with a list of species in the family (Diplopoda, Julida, Mongoliulidae)
Fig. 8. Koiulus interruptus gen. et sp. nov., paratype, ♂, from the upper course of the river Ko, posterior gonopods. A. Anterior view. B. Posterior-lateral view. C. Posterior-apical view. Abbreviations: AB = anterior branch; PB = posterior branch. Scale bars = 0.02 mm.
Fig. 7 in A new genus of mongoliulid millipedes from the Far East of Russia, with a list of species in the family (Diplopoda, Julida, Mongoliulidae)
Fig. 7. Koiulus interruptus gen. et sp. nov., paratype, ♂, from the upper course of the river Ko, anterior gonopods. A. Posterior view. B. Modified flagellum. C. Tip of left telopodite. D. Porose structure from telopodite. Abbreviations: CX = coxal process; FL = flagellum; PS = porose structure; TLP = telopodite. Scale bars: A = 0.1 mm; B–C = 0.01 mm; D = 0.001 mm.
Fig. 6 in A new genus of mongoliulid millipedes from the Far East of Russia, with a list of species in the family (Diplopoda, Julida, Mongoliulidae)
Fig. 6. Koiulus interruptus gen. et sp. nov., paratype, ♂, from the environs of the village of Zolotoi, seventh pair of legs. A. Ventral view. B. Tip of the telopodite. Scale bars: A = 0.5 mm; B = 0.1 mm.
Fig. 4 in A new genus of mongoliulid millipedes from the Far East of Russia, with a list of species in the family (Diplopoda, Julida, Mongoliulidae)
Fig. 4. Koiulus interruptus gen. et sp. nov., paratype, ♂, from the upper course of the river Ko. A. Entire body, 39 podous + 3 apodous body rings + telson. B. Anterior end. C. Body rings (5)6–12(13); note absence of ozopores on rings 7 and 11 (arrows). D. Leg-pair 7 and gonopods in situ. 7 = seventh body ring with ventral lobes. Scale bars: A =1 mm; B–C = 0.2 mm; D = 0.1 mm.
Fig. 1 in A new genus of mongoliulid millipedes from the Far East of Russia, with a list of species in the family (Diplopoda, Julida, Mongoliulidae)
Fig. 1. Koiulus interruptus gen. et sp. nov., paratype, juvenile male from upper course of river Ko. Notice interrupted series of defense glands. Scale bar: 1 mm.
Fig. 3 in A new genus of mongoliulid millipedes from the Far East of Russia, with a list of species in the family (Diplopoda, Julida, Mongoliulidae)
Fig. 3. Koiulus interruptus gen. et sp. nov., paratype, ♂, from the upper course of the river Ko. A. Left mandible. B. Molar plate of left mandible. C. Handlike processes on anterior margin of molar plate. D. Gnathochilarium. E. Lamellae linguales and modified promentum of gnathochilarium. Scale bars: A, E = 0.02 mm; B = 0.01 mm; C = 0.001 mm; D = 0.1 mm.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.