cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17876/plate/dr.3/plates/203_30494
title: Digital object identifier (DOI) for the plate 30494 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30495
title: Digital object identifier (DOI) for the plate 30495 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30496
title: Digital object identifier (DOI) for the plate 30496 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30497
title: Digital object identifier (DOI) for the plate 30497 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30498
title: Digital object identifier (DOI) for the plate 30498 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30499
title: Digital object identifier (DOI) for the plate 30499 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30500
title: Digital object identifier (DOI) for the plate 30500 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30501
title: Digital object identifier (DOI) for the plate 30501 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30502
title: Digital object identifier (DOI) for the plate 30502 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30503
title: Digital object identifier (DOI) for the plate 30503 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30504
title: Digital object identifier (DOI) for the plate 30504 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30505
title: Digital object identifier (DOI) for the plate 30505 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30506
title: Digital object identifier (DOI) for the plate 30506 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30507
title: Digital object identifier (DOI) for the plate 30507 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30508
title: Digital object identifier (DOI) for the plate 30508 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30509
title: Digital object identifier (DOI) for the plate 30509 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30510
title: Digital object identifier (DOI) for the plate 30510 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30511
title: Digital object identifier (DOI) for the plate 30511 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30512
title: Digital object identifier (DOI) for the plate 30512 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30513
title: Digital object identifier (DOI) for the plate 30513 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30514
title: Digital object identifier (DOI) for the plate 30514 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30515
title: Digital object identifier (DOI) for the plate 30515 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30516
title: Digital object identifier (DOI) for the plate 30516 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30517
title: Digital object identifier (DOI) for the plate 30517 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30518
title: Digital object identifier (DOI) for the plate 30518 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30519
title: Digital object identifier (DOI) for the plate 30519 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30520
title: Digital object identifier (DOI) for the plate 30520 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30521
title: Digital object identifier (DOI) for the plate 30521 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30522
title: Digital object identifier (DOI) for the plate 30522 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30523
title: Digital object identifier (DOI) for the plate 30523 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30524
title: Digital object identifier (DOI) for the plate 30524 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30525
title: Digital object identifier (DOI) for the plate 30525 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30526
title: Digital object identifier (DOI) for the plate 30526 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30527
title: Digital object identifier (DOI) for the plate 30527 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30528
title: Digital object identifier (DOI) for the plate 30528 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30529
title: Digital object identifier (DOI) for the plate 30529 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30530
title: Digital object identifier (DOI) for the plate 30530 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30531
title: Digital object identifier (DOI) for the plate 30531 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30532
title: Digital object identifier (DOI) for the plate 30532 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30533
title: Digital object identifier (DOI) for the plate 30533 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30534
title: Digital object identifier (DOI) for the plate 30534 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30535
title: Digital object identifier (DOI) for the plate 30535 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30536
title: Digital object identifier (DOI) for the plate 30536 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30537
title: Digital object identifier (DOI) for the plate 30537 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30538
title: Digital object identifier (DOI) for the plate 30538 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30539
title: Digital object identifier (DOI) for the plate 30539 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30540
title: Digital object identifier (DOI) for the plate 30540 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30541
title: Digital object identifier (DOI) for the plate 30541 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30542
title: Digital object identifier (DOI) for the plate 30542 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30543
title: Digital object identifier (DOI) for the plate 30543 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30544
title: Digital object identifier (DOI) for the plate 30544 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30545
title: Digital object identifier (DOI) for the plate 30545 in archive 203 from the APPLAUSE DR3
DOI: 10.17876/plate/dr.3/plates/203_30546
title: Digital object identifier (DOI) for the plate 30546 in archive 203 from the APPLAUSE DR3
DOI: 10.4121/ea9f1238-0cc8-47ba-b8c6-3e0846fb8d19
title: Data underlying the publication: Service Design and Frequency Setting for the European High-Speed Rail Network

description: We present a customised version of the Transit Network Design and Frequency Setting Problem (TNDFSP) for the long-distance transport context and HSR in particular. This allows to asses the potential of design and policy choices for HSR network design. We apply an adapted version of a heuristic solution approach to analyse the users’, operators’ and societal performance of a European HSR-network by conducting an extensive series of experiments to test the network’s performance under various policy priorities and HSR design variables. This dataset contains the two python scripts used: the first to estimate long-distance travel demand and secondly our heuristic solution approach to solve the TNDFSP. Also, input data is included , which allows to run the scripts for new experiments
DOI: 10.4121/ea9f1238-0cc8-47ba-b8c6-3e0846fb8d19.v1
title: Data underlying the publication: Service Design and Frequency Setting for the European High-Speed Rail Network

description: We present a customised version of the Transit Network Design and Frequency Setting Problem (TNDFSP) for the long-distance transport context and HSR in particular. This allows to asses the potential of design and policy choices for HSR network design. We apply an adapted version of a heuristic solution approach to analyse the users’, operators’ and societal performance of a European HSR-network by conducting an extensive series of experiments to test the network’s performance under various policy priorities and HSR design variables. This dataset contains the two python scripts used: the first to estimate long-distance travel demand and secondly our heuristic solution approach to solve the TNDFSP. Also, input data is included , which allows to run the scripts for new experiments
DOI: 10.17876/plate/dr.3/plates/203_30547
title: Digital object identifier (DOI) for the plate 30547 in archive 203 from the APPLAUSE DR3
DOI: 10.5281/zenodo.7114587
title: PRISMS RDF dump

description: RDF dump of the entire PRISMS knowledge graph in N-Quads format. www.prisms.digital
DOI: 10.5281/zenodo.7096382
title: Reproduction Package for SAS 2022 Article 'Case Study on Verification-Witness Validators: Where We Are and Where We Go'

description: This artifact is a reproduction package for the SAS 2022 article 'Case Study on Verification-Witness Validators: Where We Are and Where We Go'. It contains instructions, data, and scripts for reproducing our results, and the data and tables that we generated in our experiment.
DOI: 10.5281/zenodo.7096381
title: Reproduction Package for SAS 2022 Article 'Case Study on Verification-Witness Validators: Where We Are and Where We Go'

description: This artifact is a reproduction package for the SAS 2022 article 'Case Study on Verification-Witness Validators: Where We Are and Where We Go'. It contains instructions, data, and scripts for reproducing our results, and the data and tables that we generated in our experiment.
DOI: 10.25403/upresearchdata.20424384
title: Eco-driving optimisation

description: These datasets consist of two excel files: The first file is the FEDEO algorithm where in the 1st column of data uses the time intervals. The 2nd column is route elevation in meters, and then the elevation of the route profile is in the 3rd column given as meters. This data is critical for obtaining the optimal speed trajectory for the journey by calculating the lowest energy usage for measurement between optimisation points on the route profile. More data can be added based on the distance and elevation changes of the route section. The second file gives the stations on the route to which the optimisation algorithm must be applied to. It is critical to give the distance point of the station as well as the names in 2 columns. More stations can be added based on the distance that the train travels. The sequence of distance points should be measured in kilometers and called into the main code or algorithm.
DOI: 10.25403/upresearchdata.20424384.v1
title: Eco-driving optimisation

description: These datasets consist of two excel files: The first file is the FEDEO algorithm where in the 1st column of data uses the time intervals. The 2nd column is route elevation in meters, and then the elevation of the route profile is in the 3rd column given as meters. This data is critical for obtaining the optimal speed trajectory for the journey by calculating the lowest energy usage for measurement between optimisation points on the route profile. More data can be added based on the distance and elevation changes of the route section. The second file gives the stations on the route to which the optimisation algorithm must be applied to. It is critical to give the distance point of the station as well as the names in 2 columns. More stations can be added based on the distance that the train travels. The sequence of distance points should be measured in kilometers and called into the main code or algorithm.
DOI: 10.6084/m9.figshare.21214538
title: preB_ATAC_D210_S20.coverage.bw

description: bigwig of preb
DOI: 10.6084/m9.figshare.21214538.v1
title: preB_ATAC_D210_S20.coverage.bw

description: bigwig of preb
DOI: 10.5281/zenodo.7113655
title: X-ray computed tomography dataset of a walnut

description: walnut_scan: scan performed with a conventional micro-CT 1601 acquired projections as tiff stack info file containing corresponding metadata walnut_rec: reconstructed volume as tiff stack info file containing corresponding metadata reconstruction performed with pyXIT (see reference)
description: {"references": ["https://www.physik.uni-wuerzburg.de/en/lrm/research/software/"]}
DOI: 10.5281/zenodo.7113656
title: X-ray computed tomography dataset of a walnut: scan

description: scan performed with a conventional micro-CT 1601 acquired projections as tiff stack info file containing corresponding metadata
description: {"references": ["https://www.physik.uni-wuerzburg.de/en/lrm/research/software/"]}
DOI: 10.5281/zenodo.7115152
title: X-ray computed tomography dataset of a walnut

description: walnut_scan: scan performed with a conventional micro-CT 1601 acquired projections as tiff stack info file containing corresponding metadata walnut_rec: reconstructed volume as tiff stack info file containing corresponding metadata reconstruction performed with pyXIT (see reference)
description: {"references": ["https://www.physik.uni-wuerzburg.de/en/lrm/research/software/"]}
DOI: 10.6084/m9.figshare.21214403
title: Travel_time_to_cities_and_ports.ipynb

description: a jupyter notebook to visualize travel time to cities and ports in the year 2015 <br> If you want to run this jupyter notebook in an executable environement, use this link: <pre><code>https://mybinder.org/v2/figshare/10.6084/m9.figshare.21214403.v2/</code></pre>
DOI: 10.6084/m9.figshare.21214403.v2
title: Travel_time_to_cities_and_ports.ipynb

description: a jupyter notebook to visualize travel time to cities and ports in the year 2015 <br> If you want to run this jupyter notebook in an executable environement, use this link: <pre><code>https://mybinder.org/v2/figshare/10.6084/m9.figshare.21214403.v2/</code></pre>
DOI: 10.6084/m9.figshare.21214403.v1
title: Travel_time_to_cities_and_ports.ipynb

description: a jupyter notebook to visualize travel time to cities and ports in the year 2015
DOI: 10.25403/upresearchdata.21162520
title: Mode of action and antibacterial activity of antimicrobial peptides

description: Data representing all experiments conducted for a study on investigating the antibacterial activity and mode of action of antimicrobial peptides Os and Os-C against <em>Staphylococcus epidermidis</em>. The data for different experiments are separated in the different sheets. It contains averages of three experimental repeats then calculated into percentages and/or ratios. Standard deviations in addition to standard deviation percentages were also calculated. For experiments with a standard deviation percentage higher than 10%, the experiment was redone and the average modified to minimise variability due to human error.
DOI: 10.25403/upresearchdata.21162520.v1
title: Mode of action and antibacterial activity of antimicrobial peptides

description: Data representing all experiments conducted for a study on investigating the antibacterial activity and mode of action of antimicrobial peptides Os and Os-C against <em>Staphylococcus epidermidis</em>. The data for different experiments are separated in the different sheets. It contains averages of three experimental repeats then calculated into percentages and/or ratios. Standard deviations in addition to standard deviation percentages were also calculated. For experiments with a standard deviation percentage higher than 10%, the experiment was redone and the average modified to minimise variability due to human error.
DOI: 10.5517/ccdc.csd.cc2c6x9t
title: CCDC 2181386: Experimental Crystal Structure Determination

description: Related Article: Kevin Lindenau, Anke Spannenberg, Fabian Reiß, Torsten Beweries|2022|RSC Advances|12|26277|doi:10.1039/D2RA04955D
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2c6xbv
title: CCDC 2181387: Experimental Crystal Structure Determination

description: Related Article: Kevin Lindenau, Anke Spannenberg, Fabian Reiß, Torsten Beweries|2022|RSC Advances|12|26277|doi:10.1039/D2RA04955D
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.21227/1fdb-6r46
title: Thermal Infrared Face (TIF)

description: We create a thermal infrared face dataset (TIF) for fever screening. TIF is collected at the entrance of our university’s engineering building. The infrared face images are captured by an infrared camera under different environmental conditions.
DOI: 10.6084/m9.figshare.21214646
title: Online Learning - Vietnam

description: Pilot Data collected in May 2022 for online learning, Vietnam. Main Data collected in July 2022 for online learning, Vietnam.
DOI: 10.6084/m9.figshare.21214646.v1
title: Online Learning - Vietnam

description: Pilot Data collected in May 2022 for online learning, Vietnam. Main Data collected in July 2022 for online learning, Vietnam.
DOI: 10.5281/zenodo.7116176
title: Peak discharge and total flood volume dataset

description: Peak discharge and total flood volume dataset extracted from the compound flood events in the upper reaches of the Yangtze River based on peak-over-threshold approach.
DOI: 10.17909/7ftv-0w17
title: Data from paper
DOI: 10.34740/kaggle/dsv/4255670
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5281/zenodo.7116137
title: Ocena możliwości retencjonowania wody na zmeliorowanych obszarach podmokłych w świetle ich zagospodarowania i współczesnych zmian klimatycznych_part 1

description: Ogólne mapy obszaru badań
DOI: 10.5281/zenodo.7116136
title: Ocena możliwości retencjonowania wody na zmeliorowanych obszarach podmokłych w świetle ich zagospodarowania i współczesnych zmian klimatycznych_part 1

description: Ogólne mapy obszaru badań
DOI: 10.5281/zenodo.5070541
title: MEDDOPROF corpus: complete gold standard annotations for occupation detection in medical documents in Spanish

description: The MEDDOPROF Shared Task tackles the detection of occupations and employment statuses in clinical cases in Spanish from different specialties. Systems capable of automatically processing clinical texts are of interest to the medical community, social workers, researchers, the pharmaceutical industry, computer engineers, AI developers, policy makers, citizen’s associations and patients. Additionally, other NLP tasks (such as anonymization) can also benefit from this type of data. MEDDOPROF has three different sub-tasks: <strong>1) MEDDOPROF-NER</strong>: Participants must find the beginning and end of occupation mentions and classify them as PROFESION (PROFESSION), SITUACION_LABORAL (WORKING_STATUS) or ACTIVIDAD (ACTIVIDAD). <strong>2) MEDDOPROF-CLASS: </strong>Participants must find the beginning and end of occupation mentions and classify them according to their referent (PACIENTE [patient], FAMILIAR [family member], SANITARIO [health professional] or OTRO [other]). <strong>3) MEDDOPROF-NORM</strong>: Participants must find the beginning and end of occupation mentions and normalize them according to a reference codes list. This is the complete Gold Standard. Annotations for the NER and CLASS sub-track are provided both separately and joint together (with each annotation level separated by a dash, e.g. PROFESION-PACIENTE). The normalized mentions are given as tab-separated file (.tsv) with four columns: filename, mention text, span and code. <strong>Please cite if you use this resource:</strong> Salvador Lima-López, Eulàlia Farré-Maduell, Antonio Miranda-Escalada, Vicent Brivá-Iglesias and 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. In Procesamiento del Lenguaje Natural, 67. 2021. <pre><code>@article{meddoprof, title={NLP applied to occupational health: MEDDOPROF shared task at IberLEF 2021 on automatic recognition, classification and normalization of professions and occupations from medical texts}, author={Lima-López, Salvador and Farré-Maduell, Eulàlia and Miranda-Escalada, Antonio and Brivá-Iglesias, Vicent and Krallinger, Martin}, journal = {Procesamiento del Lenguaje Natural}, volume = {67}, year={2021}, issn = {1989-7553}, url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6393}, pages = {243--256} }</code></pre> <strong>Resources:</strong> - Web - Training Data - Test set - Codes Reference List (for MEDDOPROF-NORM) - Annotation Guidelines MEDDOPROF is part of the IberLEF 2021 workshop, which is co-located with the SEPLN 2021 conference. For further information, please visit https://temu.bsc.es/meddoprof/ or email us at encargo-pln-life@bsc.es MEDDOPROF is promoted by the Plan de Impulso de las Tecnologías del Lenguaje de la Agenda Digital (Plan TL).