cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7950460
title: TRINITY DC 02.08.01 Curtailment distribution in the area of Greece

description: The experiment will take advantage of the possibility of the T-RES platform to distribute the limitation (curtailments) sent by the TSO among the RES assets in a portfolio (always respecting TSO limits). This distribution could be done based on different algorithms: uniform, shedding rota and generator size. The target of this experiment is to measure the impact of curtailments sent by TSO’s in the overall performance of a RES portfolio in (including an aggregation of renewables in a given area) and to test the behaviour of the aforementioned curtailment distribution mechanisms. These datasets contain the following data: "day-ahead-prices-GR2022.xlsx" contains day ahead prices of Greece for the year 2022. "gr-market-scada-units-2022-anon.xlsx" this dataset contains production data from 135 assets. Only assets 20, 30, 33, 52 y 87 have been used for the aggregation.
DOI: 10.5517/ccdc.csd.cc2c6nqz
title: CCDC 2181151: Experimental Crystal Structure Determination

description: Related Article: Silvia Rizzato, Gabriele Manca, Marie-Hélène Lemée, Luciano Marchiò, Flaminia Cesare Marincola, Annalisa Guerri, Andrea Ienco, Angela Serpe, Paola Deplano|2023|Inorg.Chem.|62|694|doi:10.1021/acs.inorgchem.2c02340
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: 10.17909/35k9-w010
title: doi_for_smacs0723
DOI: 10.5517/ccdc.csd.cc2dmhl4
title: CCDC 2223276: Experimental Crystal Structure Determination

description: Related Article: Kai Oberdorf, Anna Hanft, Xiulan Xie, F. Matthias Bickelhaupt, Jordi Poater, Crispin Lichtenberg|2023|Chemical Science|14|5214|doi:10.1039/D3SC01635H
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.cc2dmhn6
title: CCDC 2223278: Experimental Crystal Structure Determination

description: Related Article: Kai Oberdorf, Anna Hanft, Xiulan Xie, F. Matthias Bickelhaupt, Jordi Poater, Crispin Lichtenberg|2023|Chemical Science|14|5214|doi:10.1039/D3SC01635H
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.cc2ffysm
title: CCDC 2247741: Experimental Crystal Structure Determination

description: Related Article: Victor A. Brotsman, Nadezhda B. Tamm, Ilya N. Ioffe, Andrei A. Eliseev, Alexey A. Goryunkov, Konstantin A. Lyssenko, Sergey I. Troyanov|2023|Dalton Trans.|52|6244|doi:10.1039/D3DT00887H
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.cc2fsg4v
title: CCDC 2257827: Experimental Crystal Structure Determination

description: Related Article: V.A. Aliyeva, F.S. Aliyeva, M. Akkurt, S. Ö. Yıldırım, A. Bhattarai|2023|Acta Crystallogr.,Sect.E:Cryst.Commun.|79|508|doi:10.1107/S2056989023003602
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: 10.15128/r19s1616216
title: HTS Cable International Round Robin: 4.4 kA Critical Current Measurements [dataset]

description: The critical current of the core and shield of a 2.1 m long, 40 mm diameter Bi-2223 HTS coaxial cable was measured as part of an International Round Robin organised by Japan involving several international laboratories. In Durham, we found using a 100 µVm−1 criterion and an 8 minute trace time that Ic of the core and shield were 4386 A and 4143 A and the n-values were 19.6 and 14.4 respectively. 8 minute trace times were found to be a practical approximation of steady state conditions, with critical current values 0.5% and 1.2% larger than values obtained using 2 minute trace times, and n-values not significantly different to 2 minute trace values. Two minute trace time data were compared with equivalent data from other participating labs and excellent agreement was found:Ic was 0.6% and 1.5% larger for the core and shield respectively. The E-field range has a significant effect on the n-value measurements, which varied between groups. Good agreement between the participants for the shield was found for electric fields below 100 µVm−1, although larger n-values were found at higher electric fields at Durham and some other laboratories. The polarity of the current and the proximity of the return power cable were demonstrated to have negligible effect (&lt;0.1%) on the Ic measurements. Removing conductive and ferromagnetic materials that were part of Durham’s experimental set-up increased Ic by &lt;1%. An intrinsic trace time dependency was found for Ic for trace times of less than 8 minutes that we attribute to the time required for fluxons to equilibrate with the underlying pinning landscape in a changing net magnetic field.
DOI: 10.5281/zenodo.7950504
title: Data and code for Sr3NCl3

description: Procedure for using data and code: 1.use quantum espresso software in linux operating system for running all the code.<br> 2.Make a psedo file with the pseudopotential of Sr,N, and Cl.<br> 2.Run the scf file by using the command: pw.x&lt;Sr3NCl3.scf.in&gt;Sr3NCl3.scf.out<br> 3.Run the nscf file by using the command: pw.x&lt;Sr3NCl3.nscf.in&gt;Sr3NCl3.nscf.out<br> 4.Run the bands file by using the command: bands.x&lt;Sr3NCl3.bands.in&gt;Sr3NCl3.bands.out and using plotband.x plot the bands fig in xmgrace.<br> 5.Run the pdos file by using the command: projwfc.x&lt;Sr3NCl3.pdos.in&gt;Sr3NCl3.pdos.out<br> 6.Run the eps file by using the command: epsilon.x&lt;Sr3NCl3.eps.in&gt;Sr3NCl3.eps.out<br> 7.Run the jdos file by using the command: epsilon.x&lt;Sr3NCl3.jdos.in&gt;Sr3NCl3.jdos.out
DOI: 10.6084/m9.figshare.20738275
title: Social Identities and the Debate on Nuclear Energy Policy in France and Germany

description: This dataset includes the nodes and edges for the two-mode discourse network analyzed in the article entitled "<strong>Social Identities and the Debate on Nuclear Energy Policy in France and Germany". </strong>This includes the original artciles on which the discourse analysis is based, coded within the Discourse Network Analyzer (DNA)-
DOI: 10.34740/kaggle/dsv/5723036
title: Obesity Classification Dataset
DOI: 10.5281/zenodo.7950505
title: Data and code for Sr3NCl3

description: Procedure for using data and code: 1.use quantum espresso software in linux operating system for running all the code.<br> 2.Make a psedo file with the pseudopotential of Sr,N, and Cl.<br> 2.Run the scf file by using the command: pw.x&lt;Sr3NCl3.scf.in&gt;Sr3NCl3.scf.out<br> 3.Run the nscf file by using the command: pw.x&lt;Sr3NCl3.nscf.in&gt;Sr3NCl3.nscf.out<br> 4.Run the bands file by using the command: bands.x&lt;Sr3NCl3.bands.in&gt;Sr3NCl3.bands.out and using plotband.x plot the bands fig in xmgrace.<br> 5.Run the pdos file by using the command: projwfc.x&lt;Sr3NCl3.pdos.in&gt;Sr3NCl3.pdos.out<br> 6.Run the eps file by using the command: epsilon.x&lt;Sr3NCl3.eps.in&gt;Sr3NCl3.eps.out<br> 7.Run the jdos file by using the command: epsilon.x&lt;Sr3NCl3.jdos.in&gt;Sr3NCl3.jdos.out
DOI: 10.6084/m9.figshare.22877633
title: data file S3 The peptides identified by the customized peptide database

description: data file S3 The peptides identified by the customized peptide database
DOI: 10.6084/m9.figshare.22825115.v1
title: data file S2 The peptides identified by UniProtKBSwiss-Prot protein knowledgebase

description: data file S2 The peptides identified by UniProtKBSwiss-Prot protein knowledgebase
DOI: 10.5281/zenodo.7937697
title: Open Research Data for "A New Framework for Evaluating Model Simulated Inland Tropical Cyclone Wind Fields"

description: The (1) NOAA GFDL T-SHiELD outputs, (2) processed ASOS data, and (3) observation-based, theory-driven wind profiles data used in the manuscript "A New Framework for Evaluating Model Simulated Inland Tropical Cyclone Wind Fields".
DOI: 10.5281/zenodo.7937696
title: Open Research Data for "A New Framework for Evaluating Model Simulated Inland Tropical Cyclone Wind Fields"

description: The (1) NOAA GFDL T-SHiELD outputs, (2) processed ASOS data, and (3) observation-based, theory-driven wind profiles data used in the manuscript "A New Framework for Evaluating Model Simulated Inland Tropical Cyclone Wind Fields".
DOI: 10.5281/zenodo.7948501
title: DeepDance: Motion capture data of improvised dance (2019)

description: <em>When using this resource, please cite Wallace, B., Nymoen, K., Martin, C.P & Tøressen, J. DeepDance: Motion capture data of improvised dance (2019) (version 2.0). Zenodo 10.5281/zenodo.7948501</em> <strong>Abstract</strong> This dataset comprises full-body motion capture of improvised dance as well as corresponding audio files. 30 dancers were recorded individually, improvising to six different audio files. The motion was captured in units of mm at 240Hz using a Qualisys infra-red optical system. The experiment was carried out at the University of Oslo in October 2019. For each dancer, 3 performances are recorded for each musical piece, resulting in 540 1-minute motion capture files. The dataset was collected for use as training data in deep learning for motion generation. This dataset also includes MATLAB code to visualize the motion capture files. <strong>Music</strong> Skarphedinsson, M. Wallace, B. (2019). “Song a” Skarphedinsson, M. Wallace, B. (2019). “Song b” Skarphedinsson, M. Wallace, B. (2019). “Song c” Skarphedinsson, M. Wallace, B. (2019). “Song d” Skarphedinsson, M. Wallace, B. (2019). “Song f” LaClair, J. Bounce. Jesse LaClair, (2018) <em>Referenced here as “Song e”</em> <strong>Data Description</strong> The following data types are provided: Motion (marker position): Recorded with Qualisys Track Manager and saved as tab-separated .tsv files. Stimuli: audio .wav files containing 1 minute of the tracks described above. MATLAB script for animating the tsv files. (requires the MoCap Toolbox) Note: Recordings which contained errors such as missing markers have been replaced by subject 001. <strong>Acknowledgements</strong> This work was partially supported by the Research Council of Norway through its Centres of Excellence scheme, project number 262762. <strong>Conflicts of Interest</strong> The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
description: {"references": ["Benedikte Wallace, Charles P Martin, Jim T\u00f8rresen, and Kristian Nymoen. 2021. Learning Embodied Sound-Motion Mappings: Evaluating AI-Generated Dance Improvisation. In Creativity and Cognition. 1\u20139", "Benedikte Wallace, Charles P. Martin, Jim Torresen, and Kristian Nymoen. Towards movement generation with audio features. In Proceedings of the 11th International Conference on Computational Creativity, 2020."]}
DOI: 10.34740/kaggle/dsv/5723210
title: COVID Vaccination in World (updated daily)
DOI: 10.5281/zenodo.7950653
title: Phylogenetic analyses of hub genes accompanying the study "Environmental gradients reveal stress hubs predating plant terrestrialization"

description: 135 ML phylogenies of hub genes identified in the study "Environmental gradients reveal stress hubs predating plant terrestrialization"
DOI: 10.25678/000885
title: Data for: Predicting Microbial Water Quality in On-Site Water Reuse Systems with Online Sensors

description: Widespread implementation of on-site water reuse is hindered by the limited availability of monitoring approaches that ensure microbial quality during operation. In this study, we developed a methodology for monitoring microbial water quality in on-site water reuse systems using inexpensive and commercially available online sensors. An extensive dataset containing sensor and microbial water quality data for six of the most critical types of disruptions in membrane bioreactors with chlorination was collected. We then tested the ability of three typological machine learning algorithms – logistic regression, support-vector machine, and random forest – to predict the microbial water quality as “safe” or “unsafe” for reuse. The main criteria for model optimization was to ensure a low false positive rate (FPR) – the percentage of safe predictions when the actual condition is unsafe – which is essential to protect users health. This resulted in enforcing a fixed FPR ≤ 2%. Maximizing the true positive rate (TPR) – the percentage of safe predictions when the actual condition is safe – was given second priority. Our results show that logistic-regression-based models using only two out of the six sensors (free chlorine and oxidation-reduction potential) achieved the highest TPR. Including sensor slopes as engineered features allowed to reach similar TPRs using only one sensor instead of two. Analysis of the occurrence of false predictions showed that these were mostly early alarms, a characteristic that could be regarded as an asset in alarm management. In conclusion, the simplest algorithm in combination with only one or two sensors performed best at predicting the microbial water quality. This result provides useful insights for water quality modeling or for applications where small datasets are a common challenge and a general advantage might be gained by using simpler models that reduce the risk of overfitting, allow better interpretability, and require less computational power.
DOI: 10.5281/zenodo.7950652
title: Phylogenetic analyses of hub genes accompanying the study "Environmental gradients reveal stress hubs predating plant terrestrialization"

description: 135 ML phylogenies of hub genes identified in the study "Environmental gradients reveal stress hubs predating plant terrestrialization"
DOI: 10.6084/m9.figshare.22960841
title: SSE price data

description: 1-minute price data from 2015-01-05 to 2020-10-30 of the 56 constituent stocks of the Shanghai Composite Index.
DOI: 10.6084/m9.figshare.22825040.v1
title: data file S1 The customized peptide database.zip

description: data file S1 The customized peptide database
DOI: 10.6084/m9.figshare.22825040
title: data file S1 The customized peptide database.zip
DOI: 10.15468/dl.4wc3ur
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query: { "TaxonKey" : [ "is Hymenorchis tanii Schuit. & de Vogel" ] } The dataset includes 0 records from 0 constituent datasets:
DOI: 10.5281/zenodo.7950401
title: DeepDance: Motion capture data of improvised dance (2019)

description: <em>When using this resource, please cite Wallace, B., Nymoen, K., Martin, C.P & Tøressen, J. DeepDance: Motion capture data of improvised dance (2019) (version 2.0). Zenodo 10.5281/zenodo.5838178</em> <strong>Abstract</strong> This dataset comprises full-body motion capture of improvised dance as well as corresponding audio files. 30 dancers were recorded individually, improvising to six different audio files. The motion was captured in units of mm at 240Hz using a Qualisys infra-red optical system. The experiment was carried out at the University of Oslo in October 2019. For each dancer, 3 performances are recorded for each musical piece, resulting in 540 1-minute motion capture files. The dataset was collected for use as training data in deep learning for motion generation. This dataset also includes MATLAB code to visualize the motion capture files. <strong>Music</strong> Skarphedinsson, M. Wallace, B. (2019). “Song a” Skarphedinsson, M. Wallace, B. (2019). “Song b” Skarphedinsson, M. Wallace, B. (2019). “Song c” Skarphedinsson, M. Wallace, B. (2019). “Song d” Skarphedinsson, M. Wallace, B. (2019). “Song f” LaClair, J. Bounce. Jesse LaClair, (2018) <em>Referenced here as “Song e”</em> <strong>Data Description</strong> The following data types are provided: Motion (marker position): Recorded with Qualisys Track Manager and saved as tab-separated .tsv files. Stimuli: audio .wav files containing 1 minute of the tracks described above. MATLAB script for animating the tsv files. (requires the MoCap Toolbox) Note: Recordings which contained errors such as missing markers have been replaced by subject 001. <strong>Acknowledgements</strong> This work was partially supported by the Research Council of Norway through its Centres of Excellence scheme, project number 262762. <strong>Conflicts of Interest</strong> The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
description: {"references": ["Benedikte Wallace, Charles P Martin, Jim T\u00f8rresen, and Kristian Nymoen. 2021. Learning Embodied Sound-Motion Mappings: Evaluating AI-Generated Dance Improvisation. In Creativity and Cognition. 1\u20139", "Benedikte Wallace, Charles P. Martin, Jim Torresen, and Kristian Nymoen. Towards movement generation with audio features. In Proceedings of the 11th International Conference on Computational Creativity, 2020."]}
DOI: 10.17037/data.00003389
title: Online questionnaire results for Serbian perspectives on Afghan refugee women’s maternity experiences in Serbia

description: Dataset containing 38 responses to an online questionnaire that was administered as part of a study aiming to understand the awareness and attitudes of Serbian health professionals and members of women’s civil society organisations about the perinatal experiences of Afghan refugee women in Serbia.
DOI: 10.5281/zenodo.5838178
title: DeepDance: Motion capture data of improvised dance (2019)

description: <em>When using this resource, please cite Wallace, B., Nymoen, K., Martin, C.P & Tøressen, J. DeepDance: Motion capture data of improvised dance (2019) (version 2.0). Zenodo 10.5281/zenodo.5838178</em> <strong>Abstract</strong> This dataset comprises full-body motion capture of improvised dance as well as corresponding audio files. 30 dancers were recorded individually, improvising to six different audio files. The motion was captured in units of mm at 240Hz using a Qualisys infra-red optical system. The experiment was carried out at the University of Oslo in October 2019. For each dancer, 3 performances are recorded for each musical piece, resulting in 540 1-minute motion capture files. The dataset was collected for use as training data in deep learning for motion generation. This dataset also includes MATLAB code to visualize the motion capture files. <strong>Music</strong> Skarphedinsson, M. Wallace, B. (2019). “Song a” Skarphedinsson, M. Wallace, B. (2019). “Song b” Skarphedinsson, M. Wallace, B. (2019). “Song c” Skarphedinsson, M. Wallace, B. (2019). “Song d” Skarphedinsson, M. Wallace, B. (2019). “Song f” LaClair, J. Bounce. Jesse LaClair, (2018) <em>Referenced here as “Song e”</em> <strong>Data Description</strong> The following data types are provided: Motion (marker position): Recorded with Qualisys Track Manager and saved as tab-separated .tsv files. Stimuli: audio .wav files containing 1 minute of the tracks described above. MATLAB script for animating the tsv files. (requires the MoCap Toolbox) Note: Recordings which contained errors such as missing markers have been replaced by subject 001. <strong>Acknowledgements</strong> This work was partially supported by the Research Council of Norway through its Centres of Excellence scheme, project number 262762. <strong>Conflicts of Interest</strong> The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
description: {"references": ["Benedikte Wallace, Charles P Martin, Jim T\u00f8rresen, and Kristian Nymoen. 2021. Learning Embodied Sound-Motion Mappings: Evaluating AI-Generated Dance Improvisation. In Creativity and Cognition. 1\u20139", "Benedikte Wallace, Charles P. Martin, Jim Torresen, and Kristian Nymoen. Towards movement generation with audio features. In Proceedings of the 11th International Conference on Computational Creativity, 2020."]}
DOI: 10.13140/rg.2.2.24844.08321
title: PWs in East Pakistan War
DOI: 10.13140/rg.2.2.18133.19686
title: PWs in East Pakistan War
DOI: 10.13140/rg.2.2.14777.75369
title: East Pakistan War
DOI: 10.5519/qd.mz4de8tc
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (0 records)
DOI: 10.5517/ccdc.csd.cc2c9qfv
title: CCDC 2184087: Experimental Crystal Structure Determination

description: Related Article: Maryne A. J. Dubois, Juan J. Rojas, Alistair J. Sterling, Hannah C. Broderick, Milo A. Smith, Andrew J. P. White, Philip W. Miller, Chulho Choi, James J. Mousseau, Fernanda Duarte, James A. Bull|2023|J.Org.Chem.|88|6476|doi:10.1021/acs.joc.3c00083
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.cc2c9qgw
title: CCDC 2184088: Experimental Crystal Structure Determination

description: Related Article: Maryne A. J. Dubois, Juan J. Rojas, Alistair J. Sterling, Hannah C. Broderick, Milo A. Smith, Andrew J. P. White, Philip W. Miller, Chulho Choi, James J. Mousseau, Fernanda Duarte, James A. Bull|2023|J.Org.Chem.|88|6476|doi:10.1021/acs.joc.3c00083
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.cc2c9qhx
title: CCDC 2184089: Experimental Crystal Structure Determination

description: Related Article: Maryne A. J. Dubois, Juan J. Rojas, Alistair J. Sterling, Hannah C. Broderick, Milo A. Smith, Andrew J. P. White, Philip W. Miller, Chulho Choi, James J. Mousseau, Fernanda Duarte, James A. Bull|2023|J.Org.Chem.|88|6476|doi:10.1021/acs.joc.3c00083
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.