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45,411 results for “collection”
Berlin State Library (2024). Metadata of the Digitized Collections of the Berlin State Library (SBB)
<p>The motivation for creating this dataset was to enable research on the basis of metadata which are available in a cultural heritage institution on a large scale. Libraries such as the Staatsbibliothek zu Berlin – Berlin State Library (SBB) typically provide three kinds of data: Images (scans of books, illustrations contained in the scanned material, or else), texts (OCR'd from digitized books or manuscripts), and metadata. However, metadata form an underresearched resource, which is lamentable: These metadata are of a high quality since they have been established by trained librarians, archivists, or other cultural heritage practitioners. The publication of a set of metadata of more than 200.000 works aims therefore at providing an underresearched high-quality type of data. The basic interest of the funder in this data publication is the stimulation of innovation.</p> <p>The dataset consists of a single table containing the metadata of all 219.419 works which were available in the Digitized Collections of the Berlin State Library (SBB) on July 29th, 2024. The size of the .parquet file is about 46 MB.</p>
PanRes - Collection of antimicrobial resistance genes
<p><strong>PanRes database of antimicrobial resistance genes</strong></p><p>Many different collections of antimicrobial resistance genes (ARGs) have been collected and used for various purposes. In order to develop a workflow for mass screening of public metagenomes, we recently gathered up and filtered in a number of these gene collections to produce PanRes.</p><p>For details, please see the methods section in the following publication:</p><p><strong> "ARGfinder - a pipeline for large-scale analysis of antimicrobial resistance genes and their flanking regions in metagenomic datasets" (Unpublished, submitted)</strong></p><p>Briefly, the PanRes gene collection is gathered from a combination of other resistance gene collections into one, so each unique sequence has an "pan_" identifier (PanRes_genes). A separate table (PanRes_data) provides an overview of all the genes, their origin database and which genes cluster together in high-identity clusters.<br><br>A number of previously published collections of ARGs were used in the creation of PanRes (See references):</p><p><strong>ResFinder</strong> (downloaded 2023-01-20, (Bortolaia et al. 2020)),<br><strong>ResFinderFG</strong> (version 2.0, (Gschwind et al. 2023))<br><strong>CARD</strong> (version 3.2.5, (Alcock et al. 2023))<br><strong>MegaRes</strong> (version 3.0.0, (Bonin et al. 2023))<br><strong>AMRFinderPlus</strong> (version 3.11/2022-12-19.1, (Feldgarden et al. 2021))<br><strong>ARGANNOT</strong> (V6_July2019, (Gupta et al. 2014))<br><strong>The 'CsabaPal' collection</strong> (Provided by Csaba Pál and Zoltán Farkas in November 2022, Daruka et al. 2023))<br><strong>BacMet</strong> (version 1.1, (Pal et al. 2014))</p>
Graphic Illustration of our Digital Collections Data and Tracking Disease Workshop Session: Discussion and Synthesis
<p>Karina Branson of <a href="https://www.conversketch.com/" target="_blank" rel="noopener">ConverSketch</a>, graphically recorded and helped to facilitate this Discussion section of our NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
Graphic Illustration of Talks in our Digital Collections Data and Tracking Disease Workshop Section: Case Studies
<p>Karina Branson of <a href="https://www.conversketch.com/" target="_blank" rel="noopener">ConverSketch</a>, graphically recorded and helped to facilitate this Case Studies section of our NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
Graphic Illustration of Molly McDonough's Talk: Exploring bat coronaviruses using the FMNH cryo collection
<p><a href="https://lib.ku.edu/people/courtney-foat" target="_blank" rel="noopener">Courtney Foat</a>, Advisor for Strategic Initiatives & Organizational Engagement at the University of Kansas, graphically recorded this invited talk by Molly McDonough at an NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
Graphic Illustration of Talks in our Digital Collections Data and Tracking Disease Workshop Section: Museum Perspectives
<p>Karina Branson of <a href="https://www.conversketch.com/" target="_blank" rel="noopener">ConverSketch</a>, graphically recorded and helped to facilitate this Museum Perspectives section of our NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
COCONUT: the COlleCtion of Open NatUral producTs.
<p>COCONUT is a COlleCtion of Open NatUral producTs.</p> <p> </p> <p>The database is now available at <a href="https://coconut.naturalproducts.net/">coconut.naturalproducts.net</a>, where the latest updates will appear before being available here.</p> <p>To assemble COCONUT, data from 55 open access collections and databases of natural products was retrieved and curated.</p> <p>This archive contains two files:</p> <ul> <li>The MongoDB dump, the most complete version of the dataset, with extensive molecular annotations</li> <li>The COCONUT4MetFrag file, used for <a href="https://msbi.ipb-halle.de/MetFrag/">MetFrag</a>. The last version of COCONUT4MetFrag is in the file "COCONUT4MetFrag_april.csv"</li> <li>The COCONUT.sdf file containing all unique NP molecules with selected metadata</li> </ul> <p>To restore the dataset in MongoDB:</p> <pre><code class="language-bash">unzip COCONUT_2021_03.zip cd COCONUT_2021_03/COCONUT_2021_03/ mongorestore --db=COCONUT --noIndexRestore . </code></pre> <p>It is generally useful to avoid restoring indexes, as they can interfere with the local installation. Here are the commands to rebuild indexes:</p> <pre><code class="language-json">mongo use COCONUT db.sourceNaturalProduct.createIndex( {source:1}) db.sourceNaturalProduct.createIndex( {simpleInchi:"hashed"}) db.sourceNaturalProduct.createIndex( {simpleInchiKey:1}) db.sourceNaturalProduct.createIndex( {originalInchiKey:1}) db.sourceNaturalProduct.createIndex( {originalSmiles:"hashed"}) db.sourceNaturalProduct.createIndex( {absoluteSmiles:"hashed"}) db.sourceNaturalProduct.createIndex( {idInSource:1}) db.uniqueNaturalProduct.createIndex( {inchi:"hashed"}) db.uniqueNaturalProduct.createIndex( {inchikey:1}) db.uniqueNaturalProduct.createIndex( {clean_smiles: "hashed"}) db.uniqueNaturalProduct.createIndex( {molecular_formula:1}) db.uniqueNaturalProduct.createIndex( {name:1}) db.uniqueNaturalProduct.createIndex( {coconut_id:1}) db.uniqueNaturalProduct.createIndex( {fragmentsWithSugar:"hashed"}) db.uniqueNaturalProduct.createIndex( {fragments:"hashed"}) db.fragment.createIndex({signature:1}) db.fragment.createIndex({signature:1, withsugar:-1}) db.sourceNaturalProduct.createIndex( {source:1}) db.sourceNaturalProduct.createIndex( {simpleInchi:"hashed"}) db.sourceNaturalProduct.createIndex( {simpleInchiKey:1}) db.sourceNaturalProduct.createIndex( {originalInchiKey:1}) db.sourceNaturalProduct.createIndex( {originalSmiles:"hashed"}) db.sourceNaturalProduct.createIndex( {absoluteSmiles:"hashed"}) db.sourceNaturalProduct.createIndex( {idInSource:1}) db.uniqueNaturalProduct.createIndex( {inchi:"hashed"}) db.uniqueNaturalProduct.createIndex( {inchikey:1}) db.uniqueNaturalProduct.createIndex( {clean_smiles: "hashed"}) db.uniqueNaturalProduct.createIndex( {molecular_formula:1}) db.uniqueNaturalProduct.createIndex( {name:1}) db.uniqueNaturalProduct.createIndex( {coconut_id:1}) db.uniqueNaturalProduct.createIndex( {fragmentsWithSugar:"hashed"}) db.uniqueNaturalProduct.createIndex( {fragments:"hashed"}) db.fragment.createIndex({signature:1}) db.fragment.createIndex({signature:1, withsugar:-1}) </code></pre> <p><br> <strong>This version of COCONUT is beta and will be curated further, but can already be used as it is.</strong></p>
Floating Car Data Collection for Processing and Benchmarking
<p>The dataset is outcome of a paper "Floating Car Data Map-matching Utilizing the Dijkstra Algorithm" accepted for 3rd International Conference on Data Management, Analytics & Innovation held in Kuala Lumpur, Malaysia in 2019.</p> <p>The floating car data (FCD representing movement of cars with their position in time) is produced by the traffic simulator software (further referred to as Simulator) published in [1] and can be used as an input for data processing and benchmarking. The dataset contains FCD of various quality levels based on the routing graph of the Czech Republic derived from Open Street Map <a href="https://www.openstreetmap.org">openstreetmap.org</a>.<br> <br> Should the dataset be exploited in scientific or other way, any acknowledgement or references to our paper [1] and dataset are welcomed and highly appreciated.</p> <p><strong>Archive contents</strong></p> <p>The archive contains following folders.</p> <p><strong>city_oneway</strong> and <strong>city_roadtrip </strong>- FCD from the city of Brno, Czech Republic where FCD is based on Origin-Destination in case of oneway and Origin-Destination-Origin in case of a road trip</p> <p><strong>intercity_oneway </strong>and <strong>intercity_roadtrip </strong>- FCD from cities of Brno, Ostrava, Olomouc and Zlin, all Czech Republic where FCD is based on Origin-Destination in case of oneway and Origin-Destination-Origin in case of a road trip</p> <p><strong>Content explanation</strong></p> <p>All four of mentioned folders contain raw FCD as they come from our Simulator, post-processed FCD enriching Simulator FCD, and obfuscated raw FCD (of both low and high obfuscation level). In the both obfuscated data sets, each measured point was moved in a random direction a number of meters given by drawing a number from a Gaussian distribution. We utilized two Gaussian distributions, one for the roads outside the city (N(0,10) for the lower and N(0,20) for the higher obfuscation level) and one for the roads inside the city (N(0,15) and N(0,30) respectively). Then some predefined number of randomly chosen points were removed (3% in our case). This approach should roughly represent real conditions encountered by FCD data as described by El Abbous and Samanta [2].</p> <p>In case of post-processed road trip data, there is one extra dataset with "cache" suffix representing the very same dataset limited to a 5-minute session memoization. This folder also contains a picture of processed FCD represented on a map.</p> <p><strong>Data format</strong><br> Standard UTF-8 encoded CSV files, separated by a semicolon with the following columns:</p> <p><strong>RAW</strong></p> <p><em>Header</em></p> <p>session_id;timestamp;lat;lon;speed;bearing;segment_id</p> <p><em>Data</em></p> <p>session_id: (Type: unsigned INT) - session (car) identifier<br> timestamp: (Type: datetime) - timestamp in UTC<br> lat: (Type: unsigned long) - latitude as used in Google maps<br> lon: (Type: unsigned long) - longitude as used in Google maps<br> speed: (Type: unsigned INT) - actual speed in kmh<br> bearing: (Type: unsigned INT) - actual bearing in angles 0-360<br> segment_id: (Type: unsigned long) - unique edge identifier</p> <p><strong>POST-PROCESSED</strong></p> <p><em>Header</em></p> <p><br> gid;car_id;point_time;lat;lon;segment_id;speed_kmh;speed_avg_kmh;distance_delta_m;distance_total_m;speedup_ratio;duration;segment_changed;duration_segment;moved;duration_move;good;duration_good;bearing;interpolated</p> <p><em>Data</em></p> <p>gid: (Type: unsigned long) - global identifier of a record<br> car_id: (Type: unsigned INT) - session (car) identifier<br> point_time: (Type: datetime) - timestamp with timezone<br> lat: (Type: unsigned long) - latitude as used in Google maps<br> lon: (Type: unsigned long) - longitude as used in Google maps<br> segment_id: (Type: unsigned long) - unique edge identifier<br> speed: (Type: unsigned INT) - actual speed in kmh<br> speed_avg_kmh: (Type: unsigned long) - actual average speed of a car in kmh<br> distance_delta_m: (Type: unsigned long) - actual distance delta in metres<br> distance_total_m: (Type: unsigned long) - actual total distance of a car in metres<br> speedup_ratio: (Type: unsigned long) - actual speed-up ratio of a car<br> duration: (Type: time) - actual duration of a car<br> segment_changed: (Type: boolean) - signals if actual segment of a car differs from the previous one<br> duration_segment: (Type: time) - actual duration on a segment of a car<br> moved: (Type: boolean) - signals if actual position of a car differs from the previous one<br> duration_move:(Type: time) - actual duration of a car since moving<br> good: signals if actual record values satisfies all data constraints (all true as derived from Simulator)<br> duration_good: actual duration of a car since when all constraints conditions satisfied<br> bearing: (Type: unsigned INT) - actual bearing in angles 0-360<br> interpolated: (Type: boolean) - signals if actual segment identifier is calculated (all false as derived from Simulator)</p> <p><strong>References</strong><br> <br> [1] <em>V. Ptošek, J. Ševčík, J. Martinovič, K. Slaninová, L. Rapant, and R. Cmar, </em><em>Real-time</em><em> traffic simulator for self-adaptive navigation system validation, Proceedings of EMSS-HMS: Modeling & </em><em>Simulation</em><em> in Logistics, Traffic & Transportation, 2018.</em></p> <p>[2] <em>A. El </em><em>Abbous</em><em> and N. Samanta. A </em><em>modeling</em><em> of GPS error </em><em>distri-butions</em><em>, In proceedings of 2017 European Navigation Conference (ENC), 2017.</em></p>
Costs and Benefits of Energy Communities - Collection of Literature
<p>The files contain references to studies of different impacts of energy communities, based on the collection reviewed in Berka & Creamer (2018) and with some additions. The typology of impacts differs from that used by Berka and Creamer.</p>
Arctic specimens in the NHMO DNA bank Arthropod collection 2022
<p>All Arctic specimens in the NHMO DNA bank Arthropod collection as of August 2022. See Johannessen et al. 2023 "Arctic specimens in the zoological collections at the Natural History Museum, University of Oslo, Norway (NHMO)" for further details.</p>
Arctic specimens in the NHMO DNA bank Fish & Herptile collection 2022
<p>All Arctic specimens in the NHMO DNA bank Fish & Herptile collection as of August 2022. See Johannessen et al. 2023 "Arctic specimens in the zoological collections at the Natural History Museum, University of Oslo, Norway (NHMO)" for further details.</p>
Arctic specimens in the NHMO Mammal collection 2022
<p>All Arctic specimens in the NHMO Mammal collection as of August 2022. See Johannessen et al. 2023 "Arctic specimens in the zoological collections at the Natural History Museum, University of Oslo, Norway (NHMO)" for further details.</p>
Arctic specimens in the NHMO DNA bank Bird collection 2022
<p>All Arctic specimens in the NHMO DNA bank Bird collection as of August 2022. See Johannessen et al. 2023 "Arctic specimens in the zoological collections at the Natural History Museum, University of Oslo, Norway (NHMO)" for further details.</p>
Arctic specimens in the NHMO Bird collection 2022
<p>All Arctic specimens in the NHMO Bird collection as of August 2022. See Johannessen et al. 2023 "Arctic specimens in the<br> zoological collections at the Natural History Museum, University of Oslo, Norway (NHMO)" for further details.</p>
Arctic specimens in the NHMO DNA bank Mammal collection 2022
<p>All Arctic specimens in the NHMO DNA bank Mammal collection as of August 2022. See Johannessen et al. 2023 "Arctic specimens in the zoological collections at the Natural History Museum, University of Oslo, Norway (NHMO)" for further details.</p>
DHP images collected from Alto Tajo and Cuellar in Spain.
<p>Digital Hemispherical Photography images taken in 33 30 x 30 m plots across two sites in Spain. Images were taken on a 10 m grid, making 16 locations per plot (see Flynn et al., 2022 for details). At each location, DHP images were captured with three exposure settings (automatic and ± one stop exposure compensation), levelling a Canon EOS 6D full frame DSLR sensor with a Sigma EX DG F3.5 fisheye lens, mounted on a Vanguard Alta Pro 263AT tripod. For each RGB image, the blue band was extracted, as this best represents sky/ vegetation contrast. For each plot, an exposure setting was chosen based on visual assessment and pixel brightness histograms of four images indicative of the whole plot. Automatic thresholding was carried out using the Ridler and Calvard method (1978), creating a binary image of sky and vegetation.</p>
Spectral decompositions dataset for the paper "Random walk informed heterogeneities detection reveals how the lymph node conduits network influences T-cells collective exploration behavior"
<p>This file contains the left and right approximated eigenvectors, as well as the approximated eigenvalues of the networks analyzed in the paper : Random walk informed heterogeneities detection reveals how<br> the lymph node conduits network influences T-cells collective<br> exploration behavior</p>
French Entity-Linking dataset between annotated tweets collected during major crises in France and French Wikipedia corpus
<p>Most of the available datasets are not particularly adapted to our target application: geolocate natural disasters from social networks. First, social media posts are largely underrepresented in these datasets, and the only Twitter dataset lacks Entity-Linking annotations. Second, none of the datasets focuses on a crisis or natural disaster event.</p> <p>To mitigate these issues, we extracted a collection of French tweets written during earthquakes and major floods that have occurred in France in recent years. We set up Label-Studio in order to annotate these tweets. A total of 4617 tweets were annotated, including 1678 tweets posted during earthquakes and 2939 during floods. For each annotated tweet, mentions were annotated using the set of labels described earlier in the paper as well as, when possible, the target Wikipedia title.</p> <p>Named “RéSoCIO” in reference to the research project in which it was carried out, the dataset resulting from this work contains a total of 12 828 annotated mentions and 1 513 distinct Wikipedia entities. 85% of mentions were associated with a Wikipedia page and 94 % if we ignore the RISKNAT and DAMAGES labels, which are often difficult to map to an existing entity.</p> <table> <tbody> <tr> <td><strong>Labels</strong></td> <td><strong>#Mentions</strong></td> <td><strong>#Linked</strong></td> <td><strong>#Entities</strong></td> </tr> <tr> <td>PERSON</td> <td>315</td> <td>263</td> <td>136</td> </tr> <tr> <td>ORG</td> <td>863</td> <td>790</td> <td>281</td> </tr> <tr> <td>GEOLOC</td> <td>4375</td> <td>4234</td> <td>701</td> </tr> <tr> <td>TRANSPORT</td> <td>250</td> <td>203</td> <td>101</td> </tr> <tr> <td>EVENT</td> <td>35</td> <td>21</td> <td>16</td> </tr> <tr> <td>FACILITY</td> <td>129</td> <td>94</td> <td>49</td> </tr> <tr> <td>RISKNAT</td> <td>5502</td> <td>4994</td> <td>128</td> </tr> <tr> <td>DAMAGES</td> <td>1136</td> <td>121</td> <td>56</td> </tr> <tr> <td>OTHER</td> <td>223</td> <td>200</td> <td>46</td> </tr> <tr> <td><strong>Total</strong></td> <td><strong>12828</strong></td> <td><strong>1322</strong></td> <td><strong>1513</strong></td> </tr> </tbody> </table> <p>Overview of the mentions annotated in the Twitter dataset. #Mentions shows the total number of mentions per label, #Linked the number of mentions linked to an entity and #Entities the number of distinct entities per label present in the dataset.</p> <table> <tbody> <tr> <td><strong>Labels</strong></td> <td><strong>#Mentions</strong></td> <td><strong>#Linked</strong></td> <td><strong>#Entitie</strong>s</td> </tr> <tr> <td>PERSON</td> <td>1100102</td> <td>1098406</td> <td>557697</td> </tr> <tr> <td>ORG</td> <td>750925</td> <td>749504</td> <td>130394</td> </tr> <tr> <td>GEOLOC</td> <td>2729702</td> <td>2728296</td> <td>215924</td> </tr> <tr> <td>TRANSPORT</td> <td>161539</td> <td>160487</td> <td>53405</td> </tr> <tr> <td>EVENT</td> <td>798433</td> <td>798251</td> <td>86471</td> </tr> <tr> <td>FACILITY</td> <td>258835</td> <td>258513</td> <td>109867</td> </tr> <tr> <td>RISKNAT</td> <td>5502</td> <td>4994</td> <td>127</td> </tr> <tr> <td>DAMAGES</td> <td>1136</td> <td>121</td> <td>56</td> </tr> <tr> <td>OTHER</td> <td>4340621</td> <td>4339658</td> <td>682458</td> </tr> <tr> <td><strong>Total</strong></td> <td><strong>10146795</strong></td> <td><strong>10138230</strong></td> <td><strong>1836399</strong></td> </tr> </tbody> </table> <p>Overview of the mentions annotated in the full dataset. #Mentions shows the total number of mentions per label, #Linked the number of mentions linked to an entity and #Entities the number of distinct entities per label present in the dataset.</p>
PluColl - The UNIPEN/NICI/HP data collection of Summer/Autumn 1994
<p>This file contains 'on-line' handwritten words data collected on a thin Wacom PL100V integrated tablet and grey-scale LCD screen (i.e., long before the iPad!) in Summer/Autumn 1994 in a collaboration project between the handwriting group at Nijmegen University and Hewlett-Packard Bristol. HP donated this data to the International Unipen Foundation. <strong>Not</strong> within the Unipen data set (10.5281/<em>zenodo</em>.1195802) were the individually labeled characters, which<strong> are included </strong>in this data set.</p> <p>___________________________________________________________________________________________________</p> <p><strong>Files overview</strong></p> <p>plucoll-1994-2023.pdf Our report to HP, from 1996. Old postscript version refurbished to pdf in 2023 with minor changes.<br> At the time of the report, 35 writers were in the data set. Ultimately there were 46 writers in total.<br> plucoll-1994-2023.txt Flat text version of the .pdf</p> <p>___________________________________________________________________________________________________<br> plucoll-2001.tgz Unipen file format pen-tip coordinates for words and .png images. <br> plucoll-2001-tgz.lst 46 writers, 210 isolated words per writer</p> <p>./plucoll/<br> ./plucoll/angelien/<br> ./plucoll/angelien/set1.dat<br> ./plucoll/angelien/set6.dat<br> ./plucoll/angelien/set2.dat<br> ./plucoll/angelien/test.dat<br> ./plucoll/angelien/set3.dat<br> ./plucoll/angelien/set4.dat<br> ./plucoll/willem/<br> ./plucoll/willem/set1.dat<br> ./plucoll/willem/set5.dat<br> ./plucoll/willem/set6.dat<br> ./plucoll/willem/test.dat<br> ./plucoll/willem/set3.dat<br> ./plucoll/willem/set4.dat<br> ./plucoll/piet/<br> ./plucoll/piet/set1.dat<br> ./plucoll/piet/set6.dat<br> ./plucoll/piet/set2.dat<br> ./plucoll/piet/test.dat<br> ./plucoll/piet/set3.dat<br> ./plucoll/piet/set4.dat<br> (etc.)<br> ___________________________________________________________________________________________________<br> Plucoll-hwr-lbl.tgz Separate plain ASCII coordinate files (.hwr) with x,y,z and corresponding label files (.lbl) for characters<br> Plucoll-hwr-lbl-tgz.txt</p> <p>./Plucoll-hwr-lbl/<br> ./Plucoll-hwr-lbl/miep/<br> ./Plucoll-hwr-lbl/miep/set1/<br> ./Plucoll-hwr-lbl/miep/set1/miep-set1-035-bouquet.hwr<br> ./Plucoll-hwr-lbl/miep/set1/miep-set1-035-bouquet.lbl<br> ./Plucoll-hwr-lbl/miep/set1/miep-set1-089-fjord.hwr<br> ./Plucoll-hwr-lbl/miep/set1/miep-set1-089-fjord.lbl<br> ./Plucoll-hwr-lbl/miep/set1/miep-set1-166-sandwich.hwr<br> ./Plucoll-hwr-lbl/miep/set1/miep-set1-166-sandwich.lbl<br> ./Plucoll-hwr-lbl/miep/set1/miep-set1-006-afghanistan.hwr<br> ./Plucoll-hwr-lbl/miep/set1/miep-set1-006-afghanistan.lbl</p> <p>(etc.)</p> <p>cat anton/set5/anton-set5-209-zigzag.lbl<br> z 12 18 0.95<br> i 53 28 0.95<br> g 95 35 0.95<br> z 149 45 0.95<br> a 213 32 0.95<br> g 243 42 0.95</p> <p>cat anton/set5/anton-set5-209-zigzag.hwr<br> 3584 3500 100<br> 3582 3502 100<br> 3578 3502 100<br> 3576 3502 100<br> 3576 3502 100<br> 3578 3502 100<br> 3582 3504 100<br> 3596 3508 100<br> 3612 3512 100<br> 3630 3522 100<br> 3648 3526 100<br> . . .<br> . . .</p> <p>(x y z 'pressure' 0=penup 100=pendown)<br> <br> ___________________________________________________________________________________________________PluColl-Letters-for-CogniGron.tgz Simplified version, ASCII with only (x,y) coordinates, 311925 characters<br> PluColl-Letters-for-CogniGron-tgz.lst This collection was used for our IOP article on bio-inspired twitch ensemble trajectory control.</p> <p> </p> <p>./Letters/<br> ./Letters/x/<br> ./Letters/x/Letter-x-ioff-349-npts-40-janneke-set3-018-appendix.xy<br> ./Letters/x/Letter-x-ioff-174-npts-45-marieke-set4-034-borax.xy<br> ./Letters/x/Letter-x-ioff-6-npts-35-hannie-set6-206-xylophone.xy<br> ./Letters/x/Letter-x-ioff-143-npts-50-corrie-set5-072-dixieland.xy<br> ./Letters/x/Letter-x-ioff-91-npts-45-janneke-set2-072-dixieland.xy<br> ./Letters/x/Letter-x-ioff-41-npts-41-eelco-set2-081-excellent.xy<br> ./Letters/x/Letter-x-ioff-186-npts-42-heleen-set6-034-borax.xy<br> ./Letters/x/Letter-x-ioff-78-npts-67-floris-set4-130-luxe.xy<br> ./Letters/x/Letter-x-ioff-62-npts-58-rintje-set3-146-oxford.xy<br> ./Letters/x/Letter-x-ioff-161-npts-36-martijn-set3-034-borax.xy<br> ./Letters/x/Letter-x-ioff-107-npts-33-saskia-set4-135-maxwell.xy<br> ./Letters/x/Letter-x-ioff-59-npts-49-corrie-set6-078-excellent.xy<br> ./Letters/x/Letter-x-ioff-168-npts-32-katrien-set4-161-reflex.xy</p> <p>(etc.) </p> <p>Filename tags:<br> <em>ioff </em>is the index of the first coordinate of a character in the original .hwr file<br> <em>npts</em> is the number of (x,y) points for that character</p> <p>cat Letter-_-ioff-203-npts-14-angelien-set3-161-reflex.xy <br> 3942 3434<br> 3952 3434<br> 3962 3434<br> 3982 3436<br> 4002 3442<br> 4026 3448<br> 4054 3458<br> 4082 3464<br> 4112 3474<br> 4134 3484<br> 4160 3492</p> <p>Note: the character '_' (underscore) represents the connecting stroke between two characters (if present)<br> Explicit modeling of the connecting stroke was important in 'formal' rule-based approaches to handwriting recognition.</p> <p>Lambert Schomaker, May 2023<br> </p>
Data for: A tool based on the Industry Foundation Classes standard for dynamic data collection and automatic generation of Building Automation Control Networks
<p>This dataset shows the results obtained for a case study at TRL4 for the research paper title <em><strong>A tool based on the Industry Foundation Classes standard for dynamic data collection and automatic generation of Building Automation Control Networks</strong></em>, with DOI: https://doi.org/10.1016/j.jobe.2023.107625</p> <p>This dataset is an enhanced IFC (Industry Foundation Classes) file with the creation of the BACN (Building Automation Control Network). This IFC file includes the devices created automatically by the BACN2BIM tool (developed by CARTIF Technology Centre) for the case study validated at TRL4. The original IFC was obtained from the Institute for Automation and Applied Informatics (IAI) / Karlsruhe Institute of Technology (KIT) https://www.ifcwiki.org/images/e/e3/AC20-FZK-Haus.ifc, under an unrestricted license, as served as one of the case studies for this research.</p> <p>*Depending on the IFC viewer used, the included sensors may not be represented correctly. In this case, it is recommended to try with another IFC viewer, for example xBIM explorer https://docs.xbim.net/downloads/xbimxplorer.html or BimCollab Zoom Free https://www.bimcollab.com/en/support/downloads/</p>
ScienceDex guides
Understand access before you commit
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.