cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4058182
title: Dialogue and Argumentation for Cultural Literacy Learning in Schools: Multilingual Data Corpus

description: This dataset is the Multilingual Corpus of the DIALLS (DIalogue and Argumentation for Literacy Learning in Schools) project (dialls2020.eu/) and consists of a set of transcripts of classroom interactions of students from ages 5 to 15 years old. These classroom interactions took place in seven DIALLS participant countries (UK, Portugal, Germany, Lithuania, Spain, Cyprus, and Israel). The corpus is a set of 202 transcripts in the participant countries’ native language (English, Portuguese, German, Lithuanian, Catalan, Cypriot Greek, and Hebrew). The transcripts in each native language range from a maximum of 35 for Hebrew (more than 17% of the overall corpus) to a minimum of 19 transcripts for Cypriot Greek (10% of the corpus). More than 50% of the transcripts in a language different from English (90 transcripts) have associated their English translation. The topic of the project is cultural literacy through dialogue and argumentation in school children. The Multilingual Corpus is relevant to the following areas of research: Educational dialogue, Citizenship education, Argumentation and learning, Multimodal literacy, Dialogic teaching, Dialogue/discourse analysis, Arts-based education, Cultural studies, Teacher professional development and communities. The dataset is organised in two main sections: an Excel file, and a zip folder with .csv files matching the excel file. A description of the corpus and further information on the dataset can be accessed in a .pdf file.
DOI: 10.6084/m9.figshare.14554857
title: 2021 electric fields simulation data.rar

description: In the data files, 'ax', 'ay', 'az' mean vector potential A_x, A_y, A_z; 'bx', 'by', 'bz' mean magnetic field B_x, B_y, B_z; 'ex', 'ey', 'ez' mean electric field E_x, E_y, E_z; 'jx' and 'jz' mean current density j_x, j_z; 'uex' and 'uez' mean electron flow velocity V_ex, V_ez; 'dde' means electron density; 'ddi' means ion density; 'e1' means the electrostatic component of the electric field; 'phic' means electrostatic potential; '38500', '39000' '39500', '40000', '40500' mean time Omega_i*t = 38.5, 39, 39.5, 40, 40.5.
DOI: 10.6084/m9.figshare.13379084.v2
title: Data and R codes from: Garrido et al. 2021. Testing the underlying assumptions of dilution effect mechanisms through quantifying the long-term dynamics of a pathogen in multiple host species.

description: Data and R codes from: <i>Garrido et al. 2021. </i><i>Testing the underlying assumptions of dilution effect mechanisms through quantifying the long-term dynamics of a pathogen in multiple host species.</i>
DOI: 10.5285/c1a807c8-a4b0-4aab-e053-6c86abc0dd5c
title: Atlantic Meridional Transect cruise AMT27 (DY084/DY085) calibrated and corrected chlorophyll concentrations from the fluorometer on the CTD rosette between September and November 2017.

description: These are the corrected and calibrated chlorophyll concentrations estimated from the fluorometric profiles collected during the 78 CTD casts conducted along the Atlantic Meridional Transect programme cruise 27 (AMT27, DY084, DY085) between 23rd September 2017 and 5th November 2017. The cruise track between UK and Falkland Islands, via South Georgia, passed through both north and south Atlantic gyres. Corrections applied to the fluorometer measurements include subtracting a dark value using deep data and removing the influence of non-photochemical quenching following standard Biogeochemical-Argo QC procedures. These data are available under the most recent version of the NERC Open Government Licence.
DOI: 10.6084/m9.figshare.14554839
title: Creatividad digital para transformar la enseñanza: Empoderamiento del alumnado desde un enfoque com-educativo

description: Material complementario a la investigación del artículo: Tabla 1. Sistema de codificación
DOI: 10.5281/zenodo.3606662
title: CodiEsp-abstracs: Abstracts from Lilacs and Ibecs with ICD10 codes

description: JSON file with abstracts from Lilacs and Ibecs with ICD10 codes (ICD10-CM and ICD10-PCS) associated to them (CIE10 in Spanish). <strong>Please, cite us:</strong> Miranda-Escalada, A., Gonzalez-Agirre, A., Armengol-Estapé, J., Krallinger, M.: Overview of automatic clinical coding: annotations, guidelines, and solutions for non-English clinical cases at CodiEsp track of eHealth CLEF 2020.<em> In: CLEF (Working Notes) (2020)</em> <pre><code>@inproceedings{miranda2020overview, </code> <code>title={Overview of automatic clinical coding: annotations, guidelines, and solutions for non-english clinical cases at codiesp track of CLEF eHealth 2020}, </code> <code>author={Miranda-Escalada, Antonio and Gonzalez-Agirre, Aitor and Armengol-Estap{\'e}, Jordi and Krallinger, Martin}, </code> <code>booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings}, </code> <code>year={2020} }</code></pre> Lilacs and Ibecs databases have MeSH terms describing some of their documents. Then, using UMLS Metathesaurus, those MeSH terms have been translated into ICD10 codes (ICD10-CM and ICD10-PCS). Every abstract have at least one ICD10 code. In addition, MeSH codes given by the databases (Lilacs and Ibecs) have a "word" describing them. These "words" have been used to add further ICD10 codes. We have done strict string matching to find whether those "words" were a descriptor of any ICD10 code (in the Spanish version, CIE10). The format of the JSON file is the following: <pre>{'articles': [{'title': 'title', 'pmid': 'pmid', 'abstractText': 'abtract (in Spanish)', 'Mesh': [{'Code': 'MeSHCode', 'Word': 'reference', 'CIE': [CIE10_1, CIE10_2, ...]}, ...] }, ...] }</pre> Additionally, the compressed file includes a folder with all the abstracts extracted in individual UTF-8 encoded text files and a tab-separated file with 4 fields: pmid label cie10-code word Summary statistics: number of abstracts: 355 840 number abstracts with at least one ICD10 code: 176 294 Percentage of MeSH codes mapped to ICD10: 10.6% (there were 2 526 772 MeSH codes and 266 949 mapped to ICD10) average number of MeSH codes per article: 7.1 average number of ICD10 codes per article: 2.5 number of ICD10 codes that have an associated MeSH code in UMLS: 3293 number of ICD10 codes that have an associated MeSH code in UMLS and appear in this dataset: 3082
description: Funded by the Plan de Impulso de las Tecnologías del Lenguaje (Plan TL).
DOI: 10.5281/zenodo.3606625
title: CodiEsp-abstracs: Abstracts from Lilacs and Ibecs with ICD10 codes

description: JSON file with abstracts from Lilacs and Ibecs with ICD10 codes (ICD10-CM and ICD10-PCS) associated to them (CIE10 in Spanish). <strong>Please, cite us:</strong> Miranda-Escalada, A., Gonzalez-Agirre, A., Armengol-Estapé, J., Krallinger, M.: Overview of automatic clinical coding: annotations, guidelines, and solutions for non-English clinical cases at CodiEsp track of eHealth CLEF 2020.<em> In: CLEF (Working Notes) (2020)</em> <pre><code>@inproceedings{miranda2020overview, </code> <code>title={Overview of automatic clinical coding: annotations, guidelines, and solutions for non-english clinical cases at codiesp track of CLEF eHealth 2020}, </code> <code>author={Miranda-Escalada, Antonio and Gonzalez-Agirre, Aitor and Armengol-Estap{\'e}, Jordi and Krallinger, Martin}, </code> <code>booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings}, </code> <code>year={2020} }</code></pre> Lilacs and Ibecs databases have MeSH terms describing some of their documents. Then, using UMLS Metathesaurus, those MeSH terms have been translated into ICD10 codes (ICD10-CM and ICD10-PCS). Every abstract have at least one ICD10 code. In addition, MeSH codes given by the databases (Lilacs and Ibecs) have a "word" describing them. These "words" have been used to add further ICD10 codes. We have done strict string matching to find whether those "words" were a descriptor of any ICD10 code (in the Spanish version, CIE10). The format of the JSON file is the following: <pre>{'articles': [{'title': 'title', 'pmid': 'pmid', 'abstractText': 'abtract (in Spanish)', 'Mesh': [{'Code': 'MeSHCode', 'Word': 'reference', 'CIE': [CIE10_1, CIE10_2, ...]}, ...] }, ...] }</pre> Additionally, the compressed file includes a folder with all the abstracts extracted in individual UTF-8 encoded text files and a tab-separated file with 4 fields: pmid label cie10-code word Summary statistics: number of abstracts: 355 840 number abstracts with at least one ICD10 code: 176 294 Percentage of MeSH codes mapped to ICD10: 10.6% (there were 2 526 772 MeSH codes and 266 949 mapped to ICD10) average number of MeSH codes per article: 7.1 average number of ICD10 codes per article: 2.5 number of ICD10 codes that have an associated MeSH code in UMLS: 3293 number of ICD10 codes that have an associated MeSH code in UMLS and appear in this dataset: 3082
description: Funded by the Plan de Impulso de las Tecnologías del Lenguaje (Plan TL).
DOI: 10.5281/zenodo.3706838
title: CodiEsp codes: list of valid CIE10 codes for the CodiEsp task

description: <strong>Please cite if you use this dataset:</strong> Antonio Miranda-Escalada, Aitor Gonzalez-Agirre, Jordi Armengol-Estapé and Martin Krallinger. Overview of automatic clinical coding: annotations, guidelines, and solutions for non-English clinical cases at CodiEsp track of CLEF eHealth 2020. In CLEF (Working Notes). 2020 <pre><code>@inproceedings{miranda2020overview, title={Overview of automatic clinical coding: annotations, guidelines, and solutions for non-english clinical cases at codiesp track of CLEF eHealth 2020}, author={Miranda-Escalada, Antonio and Gonzalez-Agirre, Aitor and Armengol-Estap{\'e}, Jordi and Krallinger, Martin}, booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings}, year={2020} }</code></pre> This compressed folder contains two files: + codiesp-D_codes.tsv: list of CIE10-Diagnósticos terms (2018 version) with their description in Spanish and in English.<br> + codiesp-P_codes.tsv: list of CIE10-Procedimiento terms (2018 version) with their description in Spanish and in English. In addition, the list also contains the codes until the 4th axis, which are also used in the CodiEsp-P track due to annotation reasons. A limited number of codes do not have an English description because they were removed from the English version but maintained in the Spanish version of the terminology. Format: <br> Tab-separated files with 3 columns<br> code es-description en-description Spanish to English description mapping:<br> For CodiEsp-D, the mapping to the English description was done through the files in the National Center for Health Statistics webpage: https://www.cdc.gov/nchs/icd/icd10cm.htm<br> Specifically, the file used was: ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Publications/ICD10CM/2018/2018-ICD-10-CM-Codes-File.zip/icd10cm_codes_2018.txt For CodiEsp-P, the mapping to the English description was done through the files in the Centers for Medicare Services webpage: https://www.cms.gov/Medicare/Coding/ICD10/2018-ICD-10-PCS-and-GEMs<br> Specifically, the file used was: 2018_icd10pcs_codes_file.zip/icd10pcs_codes_2018.txt
description: Funded by the Plan de Impulso de las Tecnologías del Lenguaje (Plan TL).
DOI: 10.5281/zenodo.3632522
title: CodiEsp codes: list of valid CIE10 codes for the CodiEsp task

description: <strong>Please cite if you use this dataset:</strong> Antonio Miranda-Escalada, Aitor Gonzalez-Agirre, Jordi Armengol-Estapé and Martin Krallinger. Overview of automatic clinical coding: annotations, guidelines, and solutions for non-English clinical cases at CodiEsp track of CLEF eHealth 2020. In CLEF (Working Notes). 2020 <pre><code>@inproceedings{miranda2020overview, title={Overview of automatic clinical coding: annotations, guidelines, and solutions for non-english clinical cases at codiesp track of CLEF eHealth 2020}, author={Miranda-Escalada, Antonio and Gonzalez-Agirre, Aitor and Armengol-Estap{\'e}, Jordi and Krallinger, Martin}, booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings}, year={2020} }</code></pre> This compressed folder contains two files: + codiesp-D_codes.tsv: list of CIE10-Diagnósticos terms (2018 version) with their description in Spanish and in English.<br> + codiesp-P_codes.tsv: list of CIE10-Procedimiento terms (2018 version) with their description in Spanish and in English. In addition, the list also contains the codes until the 4th axis, which are also used in the CodiEsp-P track due to annotation reasons. A limited number of codes do not have an English description because they were removed from the English version but maintained in the Spanish version of the terminology. Format: <br> Tab-separated files with 3 columns<br> code es-description en-description Spanish to English description mapping:<br> For CodiEsp-D, the mapping to the English description was done through the files in the National Center for Health Statistics webpage: https://www.cdc.gov/nchs/icd/icd10cm.htm<br> Specifically, the file used was: ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Publications/ICD10CM/2018/2018-ICD-10-CM-Codes-File.zip/icd10cm_codes_2018.txt For CodiEsp-P, the mapping to the English description was done through the files in the Centers for Medicare Services webpage: https://www.cms.gov/Medicare/Coding/ICD10/2018-ICD-10-PCS-and-GEMs<br> Specifically, the file used was: 2018_icd10pcs_codes_file.zip/icd10pcs_codes_2018.txt
description: Funded by the Plan de Impulso de las Tecnologías del Lenguaje (Plan TL).
DOI: 10.5281/zenodo.3657429
title: MeSDiCon subset for CodiEsp: MESH terms in MeSDiCon mapped to ICD10 CM and ICD10 PCS

description: The MeSDiCon consists of a list or gazetteer of candidate names of diseases and symptoms mentioned in Spanish clinical texts. Thus MeSDiCon serves as a lexical resource or dictionary for automatic detection of disease/symptom mentions, as well as indexing or classification of medical texts with such concept types. Terms in MeSDiCon were mapped to MESH terminology. In this subset, we have mapped MESH codes to ICD10-CM and ICD10-PCS through UMLS Metathesaurus. Then, this resource contains diseases and symptoms terms from Spanish clinical texts mapped to MESH and ICD10. <strong>Please cite if you use this dataset:</strong> Antonio Miranda-Escalada, Aitor Gonzalez-Agirre, Jordi Armengol-Estapé and Martin Krallinger. Overview of automatic clinical coding: annotations, guidelines, and solutions for non-English clinical cases at CodiEsp track of CLEF eHealth 2020. In CLEF (Working Notes). 2020 <pre><code>@inproceedings{miranda2020overview, title={Overview of automatic clinical coding: annotations, guidelines, and solutions for non-english clinical cases at codiesp track of CLEF eHealth 2020}, author={Miranda-Escalada, Antonio and Gonzalez-Agirre, Aitor and Armengol-Estap{\'e}, Jordi and Krallinger, Martin}, booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings}, year={2020} }</code></pre> <strong>File structure</strong> TSV. Data is separated by tabs (\t). Every row of the file has the following fields: <pre><code>terminology identifier translatedTerm termCount documentCount ICD10CM-code ICD10PCS-code</code></pre> In case one MESH term is mapped to more than one ICD10 code, they are separated by commas.
description: Funded by the Plan de Impulso de las Tecnologías del Lenguaje (Plan TL).
DOI: 10.5281/zenodo.3657428
title: MeSDiCon subset for CodiEsp: MESH terms in MeSDiCon mapped to ICD10 CM and ICD10 PCS

description: The MeSDiCon consists of a list or gazetteer of candidate names of diseases and symptoms mentioned in Spanish clinical texts. Thus MeSDiCon serves as a lexical resource or dictionary for automatic detection of disease/symptom mentions, as well as indexing or classification of medical texts with such concept types. Terms in MeSDiCon were mapped to MESH terminology. In this subset, we have mapped MESH codes to ICD10-CM and ICD10-PCS through UMLS Metathesaurus. Then, this resource contains diseases and symptoms terms from Spanish clinical texts mapped to MESH and ICD10. <strong>Please cite if you use this dataset:</strong> Antonio Miranda-Escalada, Aitor Gonzalez-Agirre, Jordi Armengol-Estapé and Martin Krallinger. Overview of automatic clinical coding: annotations, guidelines, and solutions for non-English clinical cases at CodiEsp track of CLEF eHealth 2020. In CLEF (Working Notes). 2020 <pre><code>@inproceedings{miranda2020overview, title={Overview of automatic clinical coding: annotations, guidelines, and solutions for non-english clinical cases at codiesp track of CLEF eHealth 2020}, author={Miranda-Escalada, Antonio and Gonzalez-Agirre, Aitor and Armengol-Estap{\'e}, Jordi and Krallinger, Martin}, booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings}, year={2020} }</code></pre> <strong>File structure</strong> TSV. Data is separated by tabs (\t). Every row of the file has the following fields: <pre><code>terminology identifier translatedTerm termCount documentCount ICD10CM-code ICD10PCS-code</code></pre> In case one MESH term is mapped to more than one ICD10 code, they are separated by commas.
description: Funded by the Plan de Impulso de las Tecnologías del Lenguaje (Plan TL).
DOI: 10.15161/oar.it/73770
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.6084/m9.figshare.14554833
title: intropolis.v1.hg19.filtered.tsv.gz

description: Filtered dataset of normal samples from Intropolis (https://github.com/nellore/intropolis)
DOI: 10.6084/m9.figshare.14554833.v1
title: intropolis.v1.hg19.filtered.tsv.gz

description: Filtered dataset of normal samples from Intropolis (https://github.com/nellore/intropolis)
DOI: 10.5281/zenodo.4741973
title: R-group replacement resource

description: A database with R-groups frequently used in medicinal chemistry and their preferred replacements is provided. For frequently used R-groups, replacements are organized in hierarches as specified in the readme.txt file. The data deposition accompanies a forthcoming publication by the authors in which the database will be described in detail.
DOI: 10.5281/zenodo.4741972
title: R-group replacement resource

description: A database with R-groups frequently used in medicinal chemistry and their preferred replacements is provided. For frequently used R-groups, replacements are organized in hierarches as specified in the readme.txt file. The data deposition accompanies a forthcoming publication by the authors in which the database will be described in detail.
DOI: 10.5281/zenodo.4742064
title: New production channels for light dark matter in hadronic showers

description: This is the companion dataset to the publication "<em>New production channels for light dark matter in hadronic showers</em>" by the same authors. It includes the track-lengths for electrons and positrons for MiniBooNE, SHiP, NOvA and DUNE. An example process card and UFO model files for Maddump are further provided.
description: These track lengths have been obtained from simplified geometries of the experimental setups and are primarily dedicated to the simulation of light new physics production via lepton-induced processes. Version note: - 1.0.1: Correction of the form factor in the example UFO file - 1.1.0: Developped the example, included a maddump plugin distribution
DOI: 10.5281/zenodo.3890983
title: New production channels for light dark matter in hadronic showers

description: This is the companion dataset to the publication "<em>New production channels for light dark matter in hadronic showers</em>" by the same authors. It includes the track-lengths for electrons and positrons for MiniBooNE, SHiP, NOvA and DUNE. An example process card and UFO model files for Maddump are further provided.
description: These track lengths have been obtained from simplified geometries of the experimental setups and are primarily dedicated to the simulation of light new physics production via lepton-induced processes. Version note: - 1.0.1: Correction of the form factor in the example UFO file - 1.1.0: Developped the example, included a maddump plugin distribution
DOI: 10.6084/m9.figshare.10255961
title: Safety and efficacy of vildagliptin: 52-week post-marketing surveillance of Japanese patients with type 2 diabetes in combination with other oral antidiabetics and insulin

description: <b>Background</b>: Vildagliptin is a dipeptidyl peptidase-4 inhibitor that reduces glycemia in patients with type 2 diabetes mellitus (T2DM). When approved in 2013, data on vildagliptin combined with &gt;750 mg/day metformin in Japanese patients were limited. There is a need to confirm the safety and efficacy of vildagliptin in combination with oral antidiabetic drugs (OADs). <b>Research design and methods</b>: This 52-week post-marketing surveillance (PMS) observational study in Japanese T2DM patients evaluated the safety and efficacy of vildagliptin in combination with OADs including high-dose metformin or insulin but excluding combination with sulfonylureas alone. <b>Results</b>: During this survey of 3006 Japanese T2DM patients, 13.61% of patients experienced adverse events (AEs) and 2.20% reported a serious AE (SAE). The frequency of AEs/SAEs was similar when in combination with biguanides (12.93%/1.46%), metformin ≥1000 mg/day (12.92%/1.22%), metformin &lt;1000 mg/day (12.62%/1.54%), thiazolidine derivatives (16.71%/2.86%), α-glucosidase inhibitors (13.18%/1.90%), rapid-acting insulin secretagogues (glinides) (20.41%/5.71%), or insulin (15.87%/2.47%). The mean ± SD changes from baseline at endpoint in glycated hemoglobin and fasting blood glucose were −0.76 ± 1.27% and −23.3 ± 57.3 mg/dL, respectively, and these changes were consistent, regardless of concomitant OAD. <b>Conclusions</b>: Long-term vildagliptin combination therapy is safe and effective in Japanese T2DM patients in real-world settings.