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2,373 results for “working”
MLR - Mapping Collaboration Challenges in Hybrid Working Environments of Information Technology Teams
<p> Supplementary artifact for Mapping Collaboration Challenges in Hybrid Working Environments of Information Technology Teams</p>
Selection of Wikidata works of art
<p>The dataset contains a selection of works of art stored in Wikidata. It has been extracted via Wikidata API with action "wbgetentities"in date 01/05/2022. </p> <p>The dataset comprises literary productions, visual artworks, cultural sites, operatic works, performing arts, journals etc. We semiautomatically surveyed the existing Wikidata subclasses of work of art and removed all elements belonging to music and cinematographic domains as well as statistical rumor.</p> <p>In total, the dataset contains 2'156'363 works of art (wikidata entities) described by 18'924'333 statements and belonging to 6'612 selected classes. The dataset is stored in a zip folder organised in 10'782 json files, each storing 200 entities and their metadata.</p>
Data for MILP algorithm for control strategy optimisation of a hybrid solar thermal plant consisting of two different solar fields working for two processes at different temperatures, a three tanks storage and a boiler for SHIP applications
<p>This repository contains the results of simulations exposed in the article <strong>MILP algorithm for control strategy optimisation of a hybrid solar thermal plant consisting of two different solar fields working for two processes at different temperatures, a three tanks storage and a boiler for SHIP applications.</strong></p> <p>Notebooks for easy plotting of the results are contained in this repository. The README.txt contains light explainations regarding how to use those notebooks.</p> <p>This article is yet to be published at the time of writing.</p> <p> </p>
UTC STRIDE Project G2: Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation
<p>This repository contains the extracted traffic data used in the case study for UTC STRIDE project G2 "Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation". The traffic data was extracted using manual or AI-based methods (presented in the project final report) from two 30-minute videos with high and low traffic density. </p> <p>Below are the description of each data file:</p> <ul> <li><strong>high_density_both_lane_raw_AI_speed.csv</strong> <ul> <li><strong>Description: </strong>extracted individual vehicle speed data of the high traffic density video using the AI-based method.</li> <li><strong>Data Fields:</strong> <ul> <li>object_id: unique id for each detected and tracked vehicle</li> <li>frame_index: frame index of the video when other tracked vehicle exit the virtual speed loop.</li> <li>lane_id: lane id (1=outer lane, 2=inner lane)</li> <li>speed: average speed traveling through the virtual speed loop (kph)</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_AI_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>AI extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the inner lane of the high traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_AI_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>AI extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the outer lane of the high traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>low_density_inner_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the inner lane of the low traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>low_density_inner_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the inner lane of the low traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>low_density_outer_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the outer lane of the low traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>low_density_outer_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the outer lane of the low traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> </ul>
Do Team Trust and Leader Support Matter at Work and Beyond?
<p>Team effectiveness is examined using the Arc of Purposeful Leadership model to investigate mediational effects of employees’ departmental impact, organizational commitment, and leader support on work-family synergy and emotional exhaustion. Structural equation modelling and Hayes’ PROCESS macro were used to analyze the responses of 319 participants. Findings support the Input-Process-Output (IPO) model of team effectiveness. Relationships between team purpose/support and outcome variables (work-family synergy and emotional exhaustion) were mediated by employees’ departmental impact, organizational commitment, and leader support. Findings reveal the complexities of team processes in relation to outcomes, including work-family synergy and emotional exhaustion, that extend beyond the workplace.</p> <p>Data were collected at University of Redlands in 2020 during the COVID-19 pandemic.</p>
Data for: Simultaneously enhanced tenacity, rupture work, and thermal conductivity of carbon nanotubes fibers by raising effective tube portion
<p><span>Although individual carbon nanotubes (CNTs) are superior as constituents to polymer chains, the mechanical and thermal properties of CNT fibers (CNTFs) remain inferior to </span><span>synthetic fibers</span><span> due to the failure of embedding CNTs effectively in superstructures. Conventional techniques resulted in a mild improvement of target properties while degrading </span><span>others. Here, a Double-Drawing technique is developed to rearrange the constituent CNTs in both mesoscale and nanoscale morphology. Consequently, the mechanical and thermal properties of the resulting CNTFs can simultaneously reach their highest performances with specific strength ~3.30 N/tex (4.60 GPa), work of rupture ~70 J/g, and thermal conductivity ~354 W/m/K, despite starting from low-crystallinity materials (<em>I</em><sub>G</sub>:<em>I</em><sub>D</sub>~5). The processed CNTFs are more versatile than comparable carbon fiber, Zylon and Dyneema. Based on evidence of load transfer efficiency on individual CNTs measured with In-Situ-Stretching-Raman, we find the main contributors to property enhancements are the increasing of the effective tube contribution, in addition to the known optimization on CNTs alignment and stacking.</span></p>
Work-Family conflict among hotel housekeepers in the Balearic Islands (Spain)
<p>The aim of this study is to describe the domestic burden and difficulties in work-life balance and to understand the intersection of work and family spheres among hotel housekeepers who work in the Balearic Islands. </p>
Euclid in print, 1482–1703. A catalogue of the editions of the Elements and other Euclidean works
<p>This catalogue lists, in intention, every edition of one or more works attributed to Euclid printed in any language down to the year 1703. It is intended to improve upon the previous Euclidean bibliographies of Riccardi, Duarte, Thomas-Stanford and Steck, which were compiled before the availability of digital catalogues. It was produced as part of the 2016–18 project 'Reading Euclid', based at the University of Oxford and funded by the Arts and Humanities Research Council of the UK. </p>
Spreadsheet working analysis (uncleaned): correlation of global warming with Gross World Product (PPP) for 1850–2021
<p>This workbook has not been peer-reviewed. Do not cite. Use with caution.</p> <p>For analysis charts see the sheet entitled <em>CHARTS GDP totals and CO2.</em></p> <p><strong>Source data:</strong></p> <p>Gross world product data from:<br> Bolt, J., van Zanden, J.L., 2020. Maddison Project Database 2020 [WWW Document]. Univ. Gron. URL https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-project-database-2020 (accessed 11.22.22).</p> <p>Global CO2 data from:<br> CDIAC, 2017. Global, Regional, and National Fossil-Fuel CO2 Emissions. Carbon Dioxide Information Analysis Center.<br> Friedlingstein, P., O’Sullivan, M., Jones, M.W., Andrew, R.M., Gregor, L., Hauck, J., Le Quéré, C., Luijkx, I.T., Olsen, A., Peters, G.P., Peters, W., Pongratz, J., Schwingshackl, C., Sitch, S., Canadell, J.G., Ciais, P., Jackson, R.B., Alin, S.R., Alkama, R., Arneth, A., Arora, V.K., Bates, N.R., Becker, M., Bellouin, N., Bittig, H.C., Bopp, L., Chevallier, F., Chini, L.P., Cronin, M., Evans, W., Falk, S., Feely, R.A., Gasser, T., Gehlen, M., Gkritzalis, T., Gloege, L., Grassi, G., Gruber, N., Gürses, Ö., Harris, I., Hefner, M., Houghton, R.A., Hurtt, G.C., Iida, Y., Ilyina, T., Jain, A.K., Jersild, A., Kadono, K., Kato, E., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken, J.I., Landschützer, P., Lefèvre, N., Lindsay, K., Liu, J., Liu, Z., Marland, G., Mayot, N., McGrath, M.J., Metzl, N., Monacci, N.M., Munro, D.R., Nakaoka, S.-I., Niwa, Y., O’Brien, K., Ono, T., Palmer, P.I., Pan, N., Pierrot, D., Pocock, K., Poulter, B., Resplandy, L., Robertson, E., Rödenbeck, C., Rodriguez, C., Rosan, T.M., Schwinger, J., Séférian, R., Shutler, J.D., Skjelvan, I., Steinhoff, T., Sun, Q., Sutton, A.J., Sweeney, C., Takao, S., Tanhua, T., Tans, P.P., Tian, X., Tian, H., Tilbrook, B., Tsujino, H., Tubiello, F., van der Werf, G.R., Walker, A.P., Wanninkhof, R., Whitehead, C., Willstrand Wranne, A., Wright, R., Yuan, W., Yue, C., Yue, X., Zaehle, S., Zeng, J., Zheng, B., 2022. Global Carbon Budget 2022. Earth Syst. Sci. Data 14, 4811–4900. https://doi.org/10.5194/essd-14-4811-2022</p>
Computational results and python files for the work "Divergence-conforming velocity and vorticity approximations for incompressible fluids obtained with minimal facet coupling"
<p><br> This repository contains data accompanying the paper "Divergence-conforming velocity and vorticity approximations for incompressible fluids obtained with minimal facet coupling".</p> <p>The implementation is based on the python-interface of the NGSolve open source Finite Element library (ngsolve.org).</p> <p>The file solve_problem_allione.py represents a minimum working example where the proposed MCS/HDG (set the use_MCS flag) method is used to solve the problem from the numerics section of the paper.</p> <p>The files FlowTemplates.py and krylovspace_extension.py contain a somewhat larger and more modular implementation of the proposed method that also features preconditioned iterative solvers, including support for the NgsAMG NGSolve extension library as well as the NGSolve-PETSc interface.</p> <p>The files errors_hdg.pickle, errors_mcs.pickle and kappas.pickle contain the raw data the tables and pictures in the paper were generated from.</p> <p>This data was generated with the scripts conv3d_hdg.py, conv3d_mcs.py and calc_kappas.py which use the FlowTemplates.py infrastructure.</p>
Bionomia: a versioned archive of associations between people and the biodiversity data records they worked on. hash://sha256/4b192ed16cfe8577c2e275ada76bfdc19fe5a5381547139c8f8f4079e704b6f2 hash://md5/a69075f7a9a19f069c6d0c6d8f312259
<p>Biodiversity data describe nature in digital form. Bionomia [1] helps to associate the people behind the creation of biodiversity data and the records they worked on. Bionomia is updated constantly, and this publication provides a versioned snapshots of Bionomia data products, such as:</p> <p>https://bionomia.net/data/bionomia-public-profiles.csv</p> <p>and </p> <p>https://bionomia.net/data/bionomia-public-claims.csv.gz</p> <p>The data downloads are versioned using Preston [2], a biodiversity data tracker, by running the following command:</p> <pre><code class="language-bash">preston track\ https://bionomia.net/data/bionomia-public-claims.csv.gz\ https://bionomia.net/data/bionomia-public-profiles.csv</code></pre> <p>The history of this publication is:</p> <pre><code><hash://sha256/4b192ed16cfe8577c2e275ada76bfdc19fe5a5381547139c8f8f4079e704b6f2> <http://www.w3.org/ns/prov#wasDerivedFrom> <hash://sha256/22afc7a3e4e1c3bc289ce39573463331d3b594a11512c1233e39436973aea974> . <urn:uuid:0659a54f-b713-4f86-a917-5be166a14110> <http://purl.org/pav/hasVersion> <hash://sha256/22afc7a3e4e1c3bc289ce39573463331d3b594a11512c1233e39436973aea974> .</code></pre> <p>as obtained via </p> <pre><code class="language-bash">preston history\ --remote https://zenodo.org/record/7810635/files,https://linker.bio\ --anchor hash://sha256/4b192ed16cfe8577c2e275ada76bfdc19fe5a5381547139c8f8f4079e704b6f2</code></pre> <p>The aliases include are </p> <pre><code><https://bionomia.net/data/bionomia-public-claims.csv.gz> <http://purl.org/pav/hasVersion> <hash://sha256/9d345ffa98f2556aa77609fa3604e61efbd8e53c067153038a65c8b4d3705ac1> <urn:uuid:05efc676-b344-45f8-a109-de4df5b85fd2> . <https://bionomia.net/data/bionomia-public-profiles.csv> <http://purl.org/pav/hasVersion> <hash://sha256/80265c7f885a261396df909163ad8df6bc32246b55350a8bc65c20a05b30a04c> <urn:uuid:b3437df4-7dd5-44b3-a154-2e06320ade2a> . <https://bionomia.net/data/bionomia-public-claims.csv.gz> <http://purl.org/pav/hasVersion> <hash://sha256/cca558f470657a3c3fb99be70907d5705e7b5c20d12412073307fc9146e94394> <urn:uuid:fa6c6542-57bc-405a-b518-01c225f474b1> . <https://bionomia.net/data/bionomia-public-profiles.csv> <http://purl.org/pav/hasVersion> <hash://sha256/80265c7f885a261396df909163ad8df6bc32246b55350a8bc65c20a05b30a04c> <urn:uuid:36c4c1ff-3baf-4250-8c1f-fbfb2f33dfaf> .</code></pre> <p>as obtained via</p> <pre><code class="language-bash">preston alias\ --remote https://zenodo.org/record/7810635/files,https://linker.bio\ --anchor hash://sha256/4b192ed16cfe8577c2e275ada76bfdc19fe5a5381547139c8f8f4079e704b6f2</code></pre> <p>The first 5 lines of the tracked content are:</p> <pre><code>Subject,Predicate,Object https://gbif.org/occurrence/1839364365,http://rs.tdwg.org/dwc/iri/identifiedBy,https://orcid.org/0000-0001-9008-0611 https://gbif.org/occurrence/657804907,http://rs.tdwg.org/dwc/iri/identifiedBy,https://orcid.org/0000-0001-9008-0611 https://gbif.org/occurrence/657804727,http://rs.tdwg.org/dwc/iri/identifiedBy,https://orcid.org/0000-0001-9008-0611 https://gbif.org/occurrence/657804529,http://rs.tdwg.org/dwc/iri/identifiedBy,https://orcid.org/0000-0001-9008-0611</code></pre> <p>as obtained via:</p> <pre><code class="language-bash">preston cat\ --remote https://zenodo.org/record/7810635/files,https://linker.bio\ --anchor hash://sha256/4b192ed16cfe8577c2e275ada76bfdc19fe5a5381547139c8f8f4079e704b6f2\ https://bionomia.net/data/bionomia-public-claims.csv.gz\ | gunzip\ | head -n5 </code></pre> <p> </p> <p>and </p> <pre><code>Family,Given,Particle,OtherNames,Country,Keywords,wikidata,ORCID,URL Page,Roderic,,R. D. M. Page|Roderic D. M. Page|Rod Page,United Kingdom,"",,0000-0002-7101-9767,https://bionomia.net/0000-0002-7101-9767 Chatzimanolis,Stylianos,,S. Chatzimanolis|Stelios Chatzimanolis|Stelianos Chatzimanolis,United States,Taxonomist,,0000-0001-9008-0611,https://bionomia.net/0000-0001-9008-0611 Bachman,Steven,,Steven P Bachman|Steven Philip Bachman|Steve Bachman,United Kingdom,Red List|Conservation|Plants|Plantae|GIS|English,,0000-0003-1085-6075,https://bionomia.net/0000-0003-1085-6075 Robbins,Tod,,Todd Robbins,"",libraries|archives|Mormon studies|linked data|digital humanities|digital asset management,,0000-0002-6752-9721,https://bionomia.net/0000-0002-6752-9721</code></pre> <p>obtained via </p> <pre><code class="language-bash">preston cat\ --remote https://zenodo.org/record/7810635/files,https://linker.bio\ --anchor hash://sha256/4b192ed16cfe8577c2e275ada76bfdc19fe5a5381547139c8f8f4079e704b6f2\ https://bionomia.net/data/bionomia-public-profiles.csv\ | head -n5</code></pre> <p> </p> <p>This data publication was created in part in context of "Bee-hind the Scenes: Documenting Digital Traces of Prominent Natural History Bee Specimens" (see https://beehind.org), and is used together with an exhaustive list of record identifiers and associated institution, collection and catalog information [3].</p> <p><strong>References</strong></p> <p>[1] Shorthouse DP (2020) Slinging With Four Giants on a Quest to Credit Natural Historians for our Museums and Collections. Biodiversity Information Science and Standards 4: e59167. <a href="https://doi.org/10.3897/biss.4.59167">https://doi.org/10.3897/biss.4.59167</a></p> <p>[2] MJ Elliott, JH Poelen, JAB Fortes (2020). Toward Reliable Biodiversity Dataset References. Ecological Informatics. <a href="https://doi.org/10.1016/j.ecoinf.2020.101132">https://doi.org/10.1016/j.ecoinf.2020.101132</a></p> <p>[3] Poelen, Jorrit. (2023). Global Biodiversity Informatics Facility (GBIF): an exhaustive list of gbif record ids, dataset keys, and their associated Occurrence IDs, Institution Code, Collection Codes and Catalog Numbers. hash://sha256/ea88f03a7bfd1ba853fdbea3203d54ab81ac3cdc8e8da7c96bbbba9c4b05d933 hash://md5/c49fe34785354847b37ea4509261e130 (0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7789866</p> <p> </p>
How doctors apply semantic components to specify search in work-related information retrieval
<p>Workplace searching is often context-specific and targets a ‘right answer’ within some<br> domain-specific aspect of the search topic. We have developed the semantic component<br> (SC) model that allows searchers to specify a search within context-specific aspects of the<br> main topic of documents. The goal of our study was to gain insight into how family practice<br> physicians at sundhed.dk, a national healthcare portal in Denmark, applied the SC model<br> to formulate queries to solve work-related search tasks. The results showed that doctors<br> used the model purposively when choosing search facets and search concepts. They were<br> relatively consistent in their use. The findings provide promising evidence of the model’s<br> potential usefulness.</p>
Publication Database on Recent Trends and Perspectives of Collaborative Robotics in Work 4.0
<p>This publication database is part of a literature review project aimed at providing a comprehensive and up-to-date (31st of January 2022) overview of recent trends and perspectives of robotics in the working world 4.0. The related journal article can be found here: DOI (https://doi.org/10.3390/robotics12030084). The database contains bibliographic information of all the publications that were included in the literature review.</p> <p> </p> <p>This publication database (including data and complementary documents) is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) by INSTITUTE OF MECHANISM THEORY, MACHINE DYNAMICS AND ROBOTICS - RWTH AACHEN UNIVERSITY.</p>
Data from the Bioradar Work Package of the German BMBF Research Project SORTIE
<p>This research is part of the German research project SORTIE and is supported by the German Federal Ministry of Education and Research (BMBF) under grant number 13N15189.</p> <p>Related journal articles:</p> <p><em>[1]</em> <em>Shi, D.; Gidion, G.; Aftab, T.; Reindl, L.M.; Rupitsch, S.J. Frequency Comb-Based Ground-Penetrating Bioradar: System Implementation and Signal Processing. Sensors 2023, 23, 1335. </em><a href="https://doi.org/10.3390/s23031335">https://doi.org/10.3390/s23031335</a><em>.</em></p> <p><em>[2] Shi, D.; Gidion, G.; Reindl, L.M.; Rupitsch, S.J. Automatic life detection based on efficient features of ground-penetrating rescue radar signals. Submitted to Sensors.</em></p>
PRISMA DIAGRAM FOR ARTICLE: Lessons learned during the health crisis caused by COVID-19 in the work of the nursing professional: A systematic review
<p>PRISMA DIAGRAM FOR ARTICLE: Lessons learned during the health crisis caused by COVID-19 in the work of the nursing professional: A systematic review</p>
MEDDOPROF Silver Standard: Predictions for Occupations, Working Statuses and Activities in Clinical Case Reports
<p><strong>MEDDOPROF Silver Standard</strong></p> <p>This repository contains the <strong>MEDDOPROF Silver Standard</strong>, a collection of clinical case reports of multiple specialties that have been automatically annotated with occupations, working statuses and activities using named entity recognition models trained on the MEDDOPROF corpus. MEDDOPROF and this Silver Standard have been created by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis.</p> <p><strong>About the MEDDOPROF Corpus</strong></p> <p><a href="https://temu.bsc.es/meddoprof/">MEDDOPROF</a> is a corpus and shared task focused on the detection and normalization of occupations and related information such as working statuses and non-paid activities in clinical texts in Spanish. All mentions in the corpus have been normalized to SNOMED CT, with occupations also being normalized to the European Skills, Competences, and Occupations classification ESCO. MEDDOPLACE was released as part of the IberLEF 2021 workshop, which in turn was held within the SEPLN 2021 conference.</p> <p><strong>About this Silver Standard</strong></p> <p>The Silver Standard includes 13,357 documents and a total of 5,568,844 tokens. The documents belong to over 10 different medical specialties, including occupational health, oncology, radiology, infectology, dermatology, psichiatry and more.</p> <p>The corpus includes three different labels: PROFESION (occupation), SITUACION-LABORAL (working status) and ACTIVIDAD (activity/hobby). There is a total of 4,332 predictions (1,325 unique), with the most common label being PROFESION with 3,599 predictions (1,181 unique), followed by SITUACION-LABORAL with 682 (180 unique) and ACTIVIDAD with 51 (44 unique).</p> <p>The annotations are presented in .ann format, which is the format used by the annotation tool brat.</p> <p>For more information, please check the attached README file.</p> <p><strong>Citation</strong></p> <p>If you use this Silver Standard or any MEDDOPROF materials, please cite the task's overview paper:<br> Lima-López, Salvador, Eulàlia Farré-Maduell, Antonio Miranda-Escalada, Vicent Brivá-Iglesias, & Martin Krallinger. “NLP applied to occupational health: MEDDOPROF shared task at IberLEF 2021 on automatic recognition, classification and normalization of professions and occupations from medical texts.” Procesamiento del Lenguaje Natural [Online], 67 (2021): 243-256.</p> <p><strong>License</strong><br> This work is licensed under a Creative Commons Attribution 4.0 International License.</p> <p><strong>Contact</strong><br> If you have any questions or suggestions, please contact us at:</p> <p>- Salvador Lima-López (<salvador [dot] limalopez [at] gmail [dot] com>)<br> - Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p> <p><strong>Acknowledgements</strong><br> MEDDOPROF was promoted by the Spanish government's 2020 Proyectos de I+D+i RTI Tipo A (AI4ProfHealth - DESCIFRANDO EL PAPEL DE LAS PROFESIONES EN LA SALUD DE LOS PACIENTES A TRAVES DE LA MINERIA DE TEXTOS (PID2020-119266RA-I00)).</p> <p><strong>Related Links</strong></p> <p>- MEDDOPROF website: <a href="https://temu.bsc.es/meddoprof/">https://temu.bsc.es/meddoprof/</a></p> <p>- MEDDOPROF Gold Standard: <a href="https://doi.org/10.5281/zenodo.5070540">https://doi.org/10.5281/zenodo.5070540</a></p> <p>- MEDDOPROF Annotation Guidelines: <a href="https://doi.org/10.5281/zenodo.4694675">https://doi.org/10.5281/zenodo.4694675</a></p>
Wikidata Dump Joconde database works
<p>RDF dump of wikidata produced with <a href="//wdumps.toolforge.org/">wdumper</a>.</p><p>Artworks with a Joconde ID (Joconde is a french database of artworks in the french cultural heritage)<br><a href="//wdumps.toolforge.org/dump/3269">View on wdumper</a></p><p><b>entity count<b>: 18099, <b>statement count</b>: 374647, <b>triple count</b>: 484207</b></b></p>
Wikidata Dump Joconde database works
<p>RDF dump of wikidata produced with <a href="//wdumps.toolforge.org/">wdumper</a>.</p><p>Artworks with a Joconde ID (Joconde is a french database of artworks in the french cultural heritage)<br><a href="//wdumps.toolforge.org/dump/3269">View on wdumper</a></p><p><b>entity count<b>: 18099, <b>statement count</b>: 374647, <b>triple count</b>: 484207</b></b></p>
Compressed Datasets for Work "Predicting Pulsed-Laser Deposition SrTiO3 Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction"
<p>Compressed version of RHEED image datasets and Gaussia fitting parameter datasets for samples "treated_213nm", "treated_81nm" and "untreated_162nm" in the work "Predicting Pulsed Laser Deposition SrTiO<sub>3 </sub>Homoepitaxy Growth Dynamics using High-Speed Reflection High-Energy Electron Diffraction."</p>
Working code and data for a wall-to-wall mapping of carbon loss within the Chornobyl Exclusion Zone after the 2020 catastrophic wildfire
<p>Links to data in this code are from <a href="https://github.com/Janzeero-PhD/C_loss_Chornobyl_2022">GitHub repository</a>. But can be replaced by the files supplied here.</p> <p>Only code to generate models and to create figures for their validation is provided. Also code to create GMFR function (from Riemann et al. 2010 and adapted in Henderson et al. 2019) is provided.</p> <p>Code to: calculate C stock for training and validation data set per biomass compartment; prepare raster data (mostly in Google Earth Engine); make raster predictions; calculate C loss according to local combustion factors and NBR severity levels is available <em>upon request</em>.</p> <p>MetaData.xlsx contains a description of all files within this package, and variable explanation for provided data sets.</p>
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