cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6630408
title: Fibvid dataset with multiple extracted features (both sparse and dense)

description: This is a publication of the FibVid dataset originaly dedicated to fake news detection. We changed here the purpose of this dataset in order to use it in the context of event tracking in press documents. Kim, Jisu, Jihwan Aum, SangEun Lee, Yeonju Jang, Eunil Park, et Daejin Choi. 2021. « FibVID: Comprehensive Fake News Diffusion Dataset during the COVID-19 Period ». <em>Telematics and Informatics</em> 64 (novembre): 101688. https://doi.org/10.1016/j.tele.2021.101688. In this dataset, we provide multiple features extracted from the text itself. <strong>Please note the text is missing from the dataset published in the CSV format for copyright reasons. You can download the original datasets and manually add the missing texts from the original publications.</strong> Features are extracted using: - A corpus of reference articles in multiple languages languages for TF-IDF weighting. (<em>features_news</em>) [1] - A corpus of tweets reporting news for TF-IDF weighting. (<em>features_tweets)</em> [1] - A S-BERT model [2] that uses <em>distiluse-base-multilingual-cased-v1 </em>(called <em>features_use</em>) [3] - A S-BERT model [2] that uses <em>paraphrase-multilingual-mpnet-base-v2 </em>(called <em>features_mpnet</em>) [4] <strong>References:</strong> [1]: Guillaume Bernard. (2022). Resources to compute TF-IDF weightings on press articles and tweets (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6610406 [2]: Reimers, Nils, et Iryna Gurevych. 2019. « Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks ». In <em>Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)</em>, 3982‑92. Hong Kong, China: Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1410. [3]: https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1 [4]: https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2
DOI: 10.48425/zo9ztvkm
title: Protocol_incidental_observations

description: Protocol for incidental observations on the Varanger peninsula and Svalbard.
DOI: 10.5281/zenodo.6630421
title: Data set from "Absolute Treatment Effects for the Primary Outcome and All-cause Mortality in the Cardiovascular Outcome Trials of New Antidiabetic Drugs – A Meta-Analysis of Digitalized Individual Patient Data"

description: This data set contains the complete digitalized individual patient data that are used in the manuscript "Absolute Treatment Effects for the Primary Outcome and All-cause Mortality in the Cardiovascular Outcome Trials of New Antidiabetic Drugs – A Meta-Analysis of Digitalized Individual Patient Data", which is accepted from "Acta Diabetologica".<br> The data file is in CSV format and contains the five variables "OutcomeType" (with values "AllcauseMortality" or "PrimaryOutcome"), "Study" (denoting the respective cardiovascular outcome trial), "Treatment" (denoting the respective study treatment or placebo), "Event" (denoting if the respective has been observed (Event=1) or not (Event=0)), and "SurvivalTimeMonths" (denoting the respective time to the event or censoring in months).
DOI: 10.5281/zenodo.6630420
title: Data set from "Absolute Treatment Effects for the Primary Outcome and All-cause Mortality in the Cardiovascular Outcome Trials of New Antidiabetic Drugs – A Meta-Analysis of Digitalized Individual Patient Data"

description: This data set contains the complete digitalized individual patient data that are used in the manuscript "Absolute Treatment Effects for the Primary Outcome and All-cause Mortality in the Cardiovascular Outcome Trials of New Antidiabetic Drugs – A Meta-Analysis of Digitalized Individual Patient Data", which is accepted from "Acta Diabetologica".<br> The data file is in CSV format and contains the five variables "OutcomeType" (with values "AllcauseMortality" or "PrimaryOutcome"), "Study" (denoting the respective cardiovascular outcome trial), "Treatment" (denoting the respective study treatment or placebo), "Event" (denoting if the respective has been observed (Event=1) or not (Event=0)), and "SurvivalTimeMonths" (denoting the respective time to the event or censoring in months).
DOI: 10.5676/dwd_gpcc/clim_m_v2022_050
title: GPCC Climatology Version 2022 at  0.5° :  Monthly Land-Surface Precipitation Climatology for Every Month and the Total Year from Rain-Gauges built on GTS-based and Historical Data 

description: The new GPCC Precipitation Climatology (V.2022) is focusing on the period 1951-2000 and consists of normals from ca. 86,000 stations. The climatology comprises normals collected by WMO (CLINOs), delivered by the countries to GPCC or calculated from time- series of monthly data (with at least 10 complete years of data) available in our database. In case that time series of sufficient length (more than 40 years) for the period 1951-2000 were not available from a specific station, then climatological normals have also been calculated for 30-year reference periods within the period 1951-2000 (for 1961-1990, 1951-1980 or 1971-2000) with at least 20 years of data. If even this was not possible, normals have been calculated for other 30-year periods 1931-1960, 1941-1970, 1981- 2010 or 1991-2020 or for any other period with at least 10 complete years of data.
This climatology is available on a regular latitude/longitude grid with a spatial resolution of 0.25° x 0.25°, 0.5° x 0.5°, 1.0° x 1.0°, and 2.5° x 2.5°. This GPCC product has been developed as a background field, for superposition of an interpolated anomaly field according to the period of interest. All other GPCC products utilize the background fields of the GPCC Climatology to grid the monthly data at the corresponding resolution with the anomaly interpolation method.

In addition, the climatological periods 1931-1960, 1941-1970, 1951-1980, 1961-1990, 1971-2000, 1981-2010, 1991-2020 are available for download. The periods were calculated with at least 20 complete years and consists of data from ca. 20,000-40,000 stations.
DOI: 10.5281/zenodo.6630367
title: Event Registry dataset with multiple extracted features (both sparse and dense)

description: This is a republication of the Event Registry dataset originaly published by: Rupnik, Jan, Andrej Muhic, Gregor Leban, Primoz Skraba, Blaz Fortuna, et Marko Grobelnik. 2016. « News Across Languages - Cross-Lingual Document Similarity and Event Tracking ». <em>Journal of Artificial Intelligence Research</em> 55 (janvier): 283‑316. https://doi.org/10.1613/jair.4780. And reorganised for document tracking by: Miranda, Sebastião, Artūrs Znotiņš, Shay B. Cohen, et Guntis Barzdins. 2018. « Multilingual Clustering of Streaming News ». In <em>2018 Conference on Empirical Methods in Natural Language Processing</em>, 4535‑44. Brussels, Belgium: Association for Computational Linguistics. https://www.aclweb.org/anthology/D18-1483/. In this dataset, we provide multiple features extracted from the text itself. <strong>Please note the text is missing from the dataset published in the CSV format for copyright reasons. You can download the original datasets and manually add the missing texts from the original publications.</strong> Features are extracted using: - A corpus of reference articles in multiple languages languages for TF-IDF weighting. (<em>features_news</em>) [1] - A corpus of tweets reporting news for TF-IDF weighting. (<em>features_tweets)</em> [1] - A S-BERT model [2] that uses <em>distiluse-base-multilingual-cased-v1 </em>(called <em>features_use</em>) [3] - A S-BERT model [2] that uses <em>paraphrase-multilingual-mpnet-base-v2 </em>(called <em>features_mpnet</em>) [4] <strong>References:</strong> [1]: Guillaume Bernard. (2022). Resources to compute TF-IDF weightings on press articles and tweets (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6610406 [2]: Reimers, Nils, et Iryna Gurevych. 2019. « Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks ». In <em>Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)</em>, 3982‑92. Hong Kong, China: Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1410. [3]: https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1 [4]: https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2
DOI: 10.5281/zenodo.6630366
title: Event Registry dataset with multiple extracted features (both sparse and dense)

description: This is a republication of the Event Registry dataset originaly published by: Rupnik, Jan, Andrej Muhic, Gregor Leban, Primoz Skraba, Blaz Fortuna, et Marko Grobelnik. 2016. « News Across Languages - Cross-Lingual Document Similarity and Event Tracking ». <em>Journal of Artificial Intelligence Research</em> 55 (janvier): 283‑316. https://doi.org/10.1613/jair.4780. And reorganised for document tracking by: Miranda, Sebastião, Artūrs Znotiņš, Shay B. Cohen, et Guntis Barzdins. 2018. « Multilingual Clustering of Streaming News ». In <em>2018 Conference on Empirical Methods in Natural Language Processing</em>, 4535‑44. Brussels, Belgium: Association for Computational Linguistics. https://www.aclweb.org/anthology/D18-1483/. In this dataset, we provide multiple features extracted from the text itself. <strong>Please note the text is missing from the dataset published in the CSV format for copyright reasons. You can download the original datasets and manually add the missing texts from the original publications.</strong> Features are extracted using: - A corpus of reference articles in multiple languages languages for TF-IDF weighting. (<em>features_news</em>) [1] - A corpus of tweets reporting news for TF-IDF weighting. (<em>features_tweets)</em> [1] - A S-BERT model [2] that uses <em>distiluse-base-multilingual-cased-v1 </em>(called <em>features_use</em>) [3] - A S-BERT model [2] that uses <em>paraphrase-multilingual-mpnet-base-v2 </em>(called <em>features_mpnet</em>) [4] <strong>References:</strong> [1]: Guillaume Bernard. (2022). Resources to compute TF-IDF weightings on press articles and tweets (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6610406 [2]: Reimers, Nils, et Iryna Gurevych. 2019. « Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks ». In <em>Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)</em>, 3982‑92. Hong Kong, China: Association for Computational Linguistics. https://doi.org/10.18653/v1/D19-1410. [3]: https://huggingface.co/sentence-transformers/distiluse-base-multilingual-cased-v1 [4]: https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2
DOI: 10.5281/zenodo.6610406
title: Resources to compute TF-IDF weightings on press articles and tweets

description: These two datasets of features are used in order to compute TF-IDF weightings of documents. It is meant to be used with the compute-tf-idf-vectors program written in Python and available on Pypi.org. - features_tweets.csv contains features (tokens, lemmas and entities) extracted from Tweets published by press agencies in french, german, spanish and english. - features_news.csv contains features (tokens, lemmas and entities) extracted from articles published by Deutsche Welle in the same languages.
description: {"references": ["Miranda, Sebasti\u00e3o, Art\u016brs Znoti\u0146\u0161, Shay B. Cohen, et Guntis Barzdins. 2018. \u00ab\u00a0Multilingual Clustering of Streaming News\u00a0\u00bb. In 2018 Conference on Empirical Methods in Natural Language Processing, 4535\u201144. Brussels, Belgium: Association for Computational Linguistics. https://www.aclweb.org/anthology/D18-1483/."]}
DOI: 10.5281/zenodo.6610405
title: Resources to compute TF-IDF weightings on press articles and tweets

description: These two datasets of features are used in order to compute TF-IDF weightings of documents. It is meant to be used with the compute-tf-idf-vectors program written in Python and available on Pypi.org. - features_tweets.csv contains features (tokens, lemmas and entities) extracted from Tweets published by press agencies in french, german, spanish and english. - features_news.csv contains features (tokens, lemmas and entities) extracted from articles published by Deutsche Welle in the same languages.
description: {"references": ["Miranda, Sebasti\u00e3o, Art\u016brs Znoti\u0146\u0161, Shay B. Cohen, et Guntis Barzdins. 2018. \u00ab\u00a0Multilingual Clustering of Streaming News\u00a0\u00bb. In 2018 Conference on Empirical Methods in Natural Language Processing, 4535\u201144. Brussels, Belgium: Association for Computational Linguistics. https://www.aclweb.org/anthology/D18-1483/."]}
DOI: 10.5281/zenodo.6630448
title: ama1 Benchmarking bam file

description: reference database for sm-SNIPER pipeline
DOI: 10.5281/zenodo.6630447
title: Event Registry titles only dataset with multiple extracted features (both sparse and dense)

description: This is the same content as: Guillaume Bernard. (2022). Event Registry dataset with multiple extracted features (both sparse and dense) (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6630367 But with titles of articles only.
DOI: 10.5281/zenodo.6630446
title: Event Registry titles only dataset with multiple extracted features (both sparse and dense)

description: This is the same content as: Guillaume Bernard. (2022). Event Registry dataset with multiple extracted features (both sparse and dense) (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6630367 But with titles of articles only.
DOI: 10.5281/zenodo.6630458
title: ama1 Benchmarking bam file

description: reference database for sm-SNIPER pipeline
DOI: 10.5281/zenodo.6630140
title: ama1 Benchmarking bam file

description: reference database for sm-SNIPER pipeline
DOI: 10.5281/zenodo.6630467
title: Reference dataset for sm-SNIPER pipeline

description: Reference dataset for sm-SNIPER pipeline
DOI: 10.5281/zenodo.6630466
title: Reference dataset for sm-SNIPER pipeline

description: Reference dataset for sm-SNIPER pipeline
DOI: 10.34740/kaggle/dsv/3774868
title: Condition Data with Random Recording Time
DOI: 10.6084/m9.figshare.20003408.v1
title: horseradish genome

description: Horseradish genome FASTA
DOI: 10.6084/m9.figshare.20047367
title: Appendix to CRUS 4189

description: Supplementary figures and tables to Using landmark-based geometric morphometrics for holotype selection in cryptic species: a case study of Western Australian <em>Halicyclops</em> (Copepoda, Cyclopoida)
DOI: 10.6084/m9.figshare.20047367.v1
title: Appendix to CRUS 4189

description: Supplementary figures and tables to Using landmark-based geometric morphometrics for holotype selection in cryptic species: a case study of Western Australian <em>Halicyclops</em> (Copepoda, Cyclopoida)
DOI: 10.5281/zenodo.6630499
title: My hello world

description: Just a hello world
DOI: 10.5281/zenodo.6630498
title: My hello world

description: Just a hello world
DOI: 10.17034/b8881783-b828-4f41-b58a-5c63b76f3a99
title: User rotating complex received signal data

description: With a 9 m separation between the transmitter and receiver the user rotates in an indoor environment. The receiver antenna was attached to a human body phantom and was rotated using a turntable (Isunking MT320RL32). This is the complex received signal. Both antennas were connected to a Rhode & Schwarz ZVB-8 VNA using calibrated low loss coaxial cables. The VNA recorded snapshots of the complex S_{21} every 2 ms at 2.45 GHz using a 10 KHz resolution bandwidth. The duration of the experiment was 30 seconds.
DOI: 10.17034/1c4e68fb-08ed-427e-b75d-52632cbbffc0
title: Pedestrian crossing transmitter and receiver LoS path complex received signal data

description: With a 4 m separation between the transmitter and receiver a pedestrian crosses the LoS path between them in an indoor environment. This is the complex received signal. Both antennas were connected to a Rhode & Schwarz ZVB-8 VNA using calibrated low loss coaxial cables. The VNA recorded snapshots of the complex S_{21} every 2 ms at 2.45 GHz using a 10 KHz resolution bandwidth. The duration of the experiment was 30 seconds.
DOI: 10.5517/ccdc.csd.cc27fyl7
title: CCDC 2068989: Experimental Crystal Structure Determination

description: Related Article: Anna Brzechwa-Chodzyńska, Mateusz Gołdyn, Anna Walczak, Artur R. Stefankiewicz|2022|CSD Communication|||
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.cc25q222
title: CCDC 2017203: Experimental Crystal Structure Determination

description: Related Article: Alexander Finko, Anatolii Sokolov, Viktor Tafeenko, Dmitry Guk, Anna Moiseeva, Dmitry Skvortsov, Andrei Stomakhin, Andrei Beloglazkin, Roman Borisov, Vladimir Pergushov, Mikhail Melnikov, Nikolai Zyk, Alexander Majouga, Elena Beloglazkina|2022|CSD Communication|||
description: Related Article: Alexander V. Finko, Anatolii I. Sokolov, Dmitry A. Guk, Victor A. Tafeenko, Anna A. Moiseeva, Dmitry A. Skvortsov, Andrei A. Stomakhin, Andrei A. Beloglazkin, Roman S. Borisov, Vladimir I. Pergushov, Mikhail Ya. Melnikov, Nikolay V. Zyk, Alexander G. Majouga, Elena K. Beloglazkina|2022|RSC Advances|12|7133|doi:10.1039/D1RA08995A
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.cc25q211
title: CCDC 2017202: Experimental Crystal Structure Determination

description: Related Article: Alexander Finko, Anatolii Sokolov, Viktor Tafeenko, Dmitry Guk, Anna Moiseeva, Dmitry Skvortsov, Andrei Stomakhin, Andrei Beloglazkin, Roman Borisov, Vladimir Pergushov, Mikhail Melnikov, Nikolai Zyk, Alexander Majouga, Elena Beloglazkina|2022|CSD Communication|||
description: Related Article: Alexander V. Finko, Anatolii I. Sokolov, Dmitry A. Guk, Victor A. Tafeenko, Anna A. Moiseeva, Dmitry A. Skvortsov, Andrei A. Stomakhin, Andrei A. Beloglazkin, Roman S. Borisov, Vladimir I. Pergushov, Mikhail Ya. Melnikov, Nikolay V. Zyk, Alexander G. Majouga, Elena K. Beloglazkina|2022|RSC Advances|12|7133|doi:10.1039/D1RA08995A
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.cc295xl0
title: CCDC 2120852: Experimental Crystal Structure Determination

description: Related Article: Elena Beloglazkina|2022|CSD Communication|||
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.cc29blhr
title: CCDC 2125344: Experimental Crystal Structure Determination

description: Related Article: Alexander Finko, Anatolii Sokolov, Viktor Tafeenko, Dmitry Guk, Anna Moiseeva, Dmitry Skvortsov, Andrei Stomakhin, Andrei Beloglazkin, Roman Borisov, Vladimir Pergushov, Mikhail Melnikov, Nikolai Zyk, Alexander Majouga, Elena Beloglazkina|2022|CSD Communication|||
description: Related Article: Alexander V. Finko, Anatolii I. Sokolov, Dmitry A. Guk, Victor A. Tafeenko, Anna A. Moiseeva, Dmitry A. Skvortsov, Andrei A. Stomakhin, Andrei A. Beloglazkin, Roman S. Borisov, Vladimir I. Pergushov, Mikhail Ya. Melnikov, Nikolay V. Zyk, Alexander G. Majouga, Elena K. Beloglazkina|2022|RSC Advances|12|7133|doi:10.1039/D1RA08995A
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.cc29bljs
title: CCDC 2125345: Experimental Crystal Structure Determination

description: Related Article: Alexander Finko, Anatolii Sokolov, Viktor Tafeenko, Dmitry Guk, Anna Moiseeva, Dmitry Skvortsov, Andrei Stomakhin, Andrei Beloglazkin, Roman Borisov, Vladimir Pergushov, Mikhail Melnikov, Nikolai Zyk, Alexander Majouga, Elena Beloglazkina|2022|CSD Communication|||
description: Related Article: Alexander V. Finko, Anatolii I. Sokolov, Dmitry A. Guk, Victor A. Tafeenko, Anna A. Moiseeva, Dmitry A. Skvortsov, Andrei A. Stomakhin, Andrei A. Beloglazkin, Roman S. Borisov, Vladimir I. Pergushov, Mikhail Ya. Melnikov, Nikolay V. Zyk, Alexander G. Majouga, Elena K. Beloglazkina|2022|RSC Advances|12|7133|doi:10.1039/D1RA08995A
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.cc2bb6qm
title: CCDC 2154770: Experimental Crystal Structure Determination

description: Related Article: Harri Sopo, Reijo Sillanpää|2022|CSD Communication|||
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.cc2bb865
title: CCDC 2154816: Experimental Crystal Structure Determination

description: Related Article: Souvik Pandey, Peter Lönnecke, Evamarie Hey-Hawkins|2022|CSD Communication|||
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.cc2bb876
title: CCDC 2154817: Experimental Crystal Structure Determination

description: Related Article: Souvik Pandey, Peter Lönnecke, Evamarie Hey-Hawkins|2022|CSD Communication|||
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.