cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7544953
title: Synthetic datasets used for numerical testing of geology-geophyiscs integration

description: This datasets is a companion dataset to the manuscript "<strong>Integration of automatic implicit geological modelling in geophysical inversion with posterior topological analysis</strong>", by Jérémie Giraud, Guillaume Caumon, Lachlan Grose, Vitaliy Ogarko, and Paul Cupillard, for publication in Solid Earth. <br> It contains models and data shown in the paper that are not available elsewhere.<br> <br> The folder organisation is as follows, where <strong>bold</strong> refers to folders and subfolders, and text in <em>italic</em> corresponds to a succinct description of the contents. |-- <strong>synthetic 1 </strong>&gt; <em>synthetic dataset and results for the first synthetic example</em><br> | |-- <strong>geol_data_layered_model.pckl </strong>&gt; <em>geological data and model</em><br> | |-- <strong>grav_data_synthetic1.pckl </strong>&gt; <em>gravity data produced by the true model</em><br> | |-- <strong>inverted_model_no_correction.txt </strong>&gt; <em> inversion results</em><br> | |-- <strong>inverted_model_with_correction.txt </strong>&gt; <em> inversion results</em><br> | |--<strong> note.txt </strong>&gt; <em> metadata</em><br> | |--<strong> starting_model.txt </strong>&gt; <em> starting model for inversion</em><br> | |--<strong> true_model.txt </strong>&gt; <em> true model</em><br> <br> |-- <strong>synthetic 2 </strong>&gt; <em>synthetic dataset and results for the second synthetic example</em><br> | |-- <strong>case&lt;num&gt;.pckl </strong>&gt; <em>inversion results for case with number 1..5 as in the manuscript</em><br> | |-- <strong>grav_data_synthetic2.pckl </strong>&gt; <em>gravity data produced by the true model</em><br> | |-- <strong>inverted_model_no_correction.txt </strong>&gt; <em> inversion results</em><br> | |-- <strong>inverted_model_with_correction.txt </strong>&gt; <em> inversion results</em><br> | |--<strong> note.txt </strong>&gt; <em> metadata</em><br> | |--<strong> starting_model_case5.txt </strong>&gt; <em> starting_model_case5</em><br> | |--<strong> model_start_unconformity.pckl </strong>&gt; <em> starting model for inversion, cases 1..5.</em><br> | |--<strong> true_mod_geol_data.pckl </strong>&gt; <em> true model and geological data</em><br> | |--<strong> start_model_and_geol_data.pckl </strong>&gt; <em> starting geological model and corresponding geological data</em>
DOI: 10.6084/m9.figshare.24720510
title: OT cardio pulmonary resuscitation

description: Simulation training in cardiopulmonary resuscitation (CPR) is effective. Given the large number of students, active practice time is limited. To improve learning during observation, the use of an observer tool (OT) has been advocated. The aim was to assess the value of an OT to improve medical students' learning outcomes during simulation training of CPR.
DOI: 10.5281/zenodo.10252759
title: Data for "Advanced momentum sampling and Maslov phases for a precise semiclassical model of strong-field ionization"

description: Data and plot scripts used to produce the figures in "Advanced momentum sampling and Maslov phases for a precise semiclassical model of strong-field ionization", available in preprint on arXiv https://doi.org/10.48550/arXiv.2311.01845.
Abstract: Recollision processes are fundamental to strong-field physics and attoscience, thus models connecting recolliding trajectories to quantum amplitudes are a crucial part in furthering understanding of these processes. We report developments in the semiclassical path-integral-based Coulomb quantum-orbit strong-field approximation model for strong-field ionization by including an additional phase known as Maslov's phase and implementing a new solution strategy via Monte-Carlo-style sampling of the initial momenta. In doing so, we obtain exceptional agreement with solutions to the time-dependent Schrödinger equation for hydrogen, helium, and argon. We provide an in-depth analysis of the resulting photoelectron momentum distributions for these targets, facilitated by the quantum-orbits arising from the solutions to the saddle-point equations. The analysis yields a new class of rescattered trajectories that includes the well-known laser-driven long and short trajectories, along with novel Coulomb-driven rescattered trajectories. By virtue of the precision of the model, it opens the door to detailed investigations of a plethora of strong-field phenomena such as photoelectron holography, laser-induced electron diffraction and high-order above threshold ionization.
For more information on the files and their content, see the README file.
DOI: 10.5281/zenodo.10252760
title: Data for "Advanced momentum sampling and Maslov phases for a precise semiclassical model of strong-field ionization"

description: Data and plot scripts used to produce the figures in "Advanced momentum sampling and Maslov phases for a precise semiclassical model of strong-field ionization", available in preprint on arXiv https://doi.org/10.48550/arXiv.2311.01845.
Abstract: Recollision processes are fundamental to strong-field physics and attoscience, thus models connecting recolliding trajectories to quantum amplitudes are a crucial part in furthering understanding of these processes. We report developments in the semiclassical path-integral-based Coulomb quantum-orbit strong-field approximation model for strong-field ionization by including an additional phase known as Maslov's phase and implementing a new solution strategy via Monte-Carlo-style sampling of the initial momenta. In doing so, we obtain exceptional agreement with solutions to the time-dependent Schrödinger equation for hydrogen, helium, and argon. We provide an in-depth analysis of the resulting photoelectron momentum distributions for these targets, facilitated by the quantum-orbits arising from the solutions to the saddle-point equations. The analysis yields a new class of rescattered trajectories that includes the well-known laser-driven long and short trajectories, along with novel Coulomb-driven rescattered trajectories. By virtue of the precision of the model, it opens the door to detailed investigations of a plethora of strong-field phenomena such as photoelectron holography, laser-induced electron diffraction and high-order above threshold ionization.
For more information on the files and their content, see the README file.
DOI: 10.57754/fdat.rre1r-wsr46
title: Russisch-Deutscher-Spracherwerb

description: Im Rahmen des Projekts werden Spracherwerbsdaten von bilingualen Kindern sowie auch Vergleichsdaten von monolingualen Kindern erhoben (die Aufzeichnung erfolgt per Video mit externem Mikrofon) und nach dem System von Childes transkribiert. Die anonymisierten Transkripte werden nach Abschluss der Untersuchung öffentlich zugänglich gemacht.
description: Ziel des Projekts ist eine Analyse der Entwicklung des Verbalaspekts bei bilingualen Kindern im Kleinkind-, Vorschul- und Grundschulalter, die die Sprachen Russisch und Deutsch erwerben. Diese Kinder erlernen mit dem Russischen eine Sprache, in der der Aspekt eine grammatische Kategorie darstellt, und mit dem Deutschen eine Sprache, die nicht über eine derartige grammatische Kategorie verfügt.
Die Leitfrage des Projektes lautet: Verläuft die Entwicklung des Aspektsystems im Russischen bei bilingualen russisch-deutschen Kindern anders als bei monolingualen russischen Kindern, und wenn ja, wo liegen diese Unterschiede und wie sind sie zu erklären?
Daneben soll auch die Entwicklung ihres deutschen Verbalsystems betrachtet werden: Lassen sich bei bilingualen Kindern Einflüsse des russischen Aspektsystems auf die Entwicklung des deutschen Verbalsystems nachweisen, indem beispielsweise nach analogen Ausdrucksformen im Deutschen "gesucht" wird? Es sollen dabei die Verbverwendungen der bilingualen Kinder mit denjenigen monolingualer russischer und deutscher Kinder verglichen werden.
Über die genannte grammatischen Fragestellungen hinaus werden im Projekt allgemeine Fragestellungen und grundsätzliche Probleme des bilingualen deutsch-russischen Spracherwerbs in den Blick genommen. Die Untersuchung wirft auch ein Schlaglicht auf die sprachlichen Probleme einer heute in Deutschland rein quantitativ bedeutenden Bevölkerungsgruppe, nämlich der Immigranten russischer Sprache, und wird auch zu konkreten, praktisch umsetzbaren Hinweisen für die sprachliche Förderung der betroffenen Kinder führen.
In enger Anbindung an das Projekt entsteht die Dissertation der Mitarbeiterin Elena Dieser, deren Gegenstand der Erwerb des Genus bei bilingualen Kindern ist.
description: Research carried out in work package B16 of the SFB 441.
DOI: 10.24376/rd.sgul.24716424
title: Raw Data - Survey of self-sampling acceptability for cervical screening in colposcopy clinic

description: An anonymous cross sectional semi-structured survey analysis of experiences, views, motivators and barriers to self-sampling for high risk HPV for cervical screening, of women attending the colposcopy clinic at St. George's University Hospital. This data was collected in RedCap and exported to Excel and anonymised for analysis, qualitative data was analysed in NVivo. All data was collected with informed written consent and is ethically approved by the SGUL JRES.
DOI: 10.24376/rd.sgul.24716424.v1
title: Raw Data - Survey of self-sampling acceptability for cervical screening in colposcopy clinic

description: An anonymous cross sectional semi-structured survey analysis of experiences, views, motivators and barriers to self-sampling for high risk HPV for cervical screening, of women attending the colposcopy clinic at St. George's University Hospital. This data was collected in RedCap and exported to Excel and anonymised for analysis, qualitative data was analysed in NVivo. All data was collected with informed written consent and is ethically approved by the SGUL JRES.
DOI: 10.6084/m9.figshare.24720549
title: Supplementary tables.

description: Supplementary tables, see table legends for further details.
DOI: 10.6084/m9.figshare.24720549.v1
title: Supplementary tables.

description: Supplementary tables, see table legends for further details.
DOI: 10.5281/zenodo.7252237
title: DrugProt Complete PubMed Knowledge Graph

description: DrugProt Complete PubMed Knowledge Graph
Please cite if you use any DrugProt resource:
Antonio Miranda-Escalada, Farrokh Mehryary, Jouni Luoma, Darryl Estrada-Zavala, Luis Gasco, Sampo Pyysalo, Alfonso Valencia, Martin Krallinger, Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical–protein relations, Database, Volume 2023, 2023, baad080

@article{miranda2023overview,  title={Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical--protein relations},  author={Miranda-Escalada, Antonio and Mehryary, Farrokh and Luoma, Jouni and Estrada-Zavala, Darryl and Gasco, Luis and Pyysalo, Sampo and Valencia, Alfonso and Krallinger, Martin},  journal={Database},  volume={2023},  pages={baad080},  year={2023},  publisher={Oxford University Press UK} }
Description
This dataset contains a knowledge graph built from PubMed dump abstracts (December 2021). A NER system has been applied to each of the abstracts to extract mentions of type "CHEMICAL" and "GENE", as well as a RE system to detect existing relations between these mentions such as ACTIVATOR, INHIBITOR, AGONIST or PRODUCT_OF, among others (see article for a full list of relations considered).
Given the volume of the dataset, the repository is divided into 1114 folders. Each of these folders contains a chunk of PubMed abstracts, entities and relationships, divided into the following 3 files:

abstracts.tsv: Tabular file in which each line represents a pubmed document. The file has 3 columns:

Pubmed_id: Numerical identifier of the document in PubMed
Title: Title of the document
Abstract: Abstract text.
entities.tsv: List of the entities extracted from the abstracts. Each line represents an extracted entity, and has 5 columns:

Pubmed_id: Numerical identifier of the document in PubMed
Mention_id: Numerical identifier of the mention in the document.
Entity_type: Type of mention. It can be CHEMICAL or GENE.
Span_ini: Index of the first character of the annotated span in the text
Span_end: Index of the first character after the annotated span.
Span: Text span of the annotation
relations.tsv: File of existing relations between entities. Each line represents a relationship, and has the following fields:

Pubmed_id: Numerical identifier of the document in PubMed
Relation_type: DrugProt relation type among entities/arguments.
Arg1: Mention of CHEMICAL
Arg2: Mention of GENE
 
Files:

drugprot-silver-standard-kg.zip : Folders with the files previously explained
 
Related resources:

Web
DrugProt corpus
Evaluation library
Online evaluation (CodaLab)
Relation annotation guidelines
Gene and protein annotation guidelines
Chemicals and drugs annotation guidelines
FAQ
DrugProt Large Scale Additional SubTrack
DrugProt Large Scale document collection protocol
DrugProt Complete PubMed Knowledge Graph
DOI: 10.5281/zenodo.7252238
title: DrugProt Complete PubMed Knowledge Graph

description: DrugProt Complete PubMed Knowledge Graph
DOI: 10.5281/zenodo.8246229
title: DrugProt Complete PubMed Knowledge Graph

description: DrugProt Complete PubMed Knowledge Graph
Please cite if you use any DrugProt resource:
Antonio Miranda-Escalada, Farrokh Mehryary, Jouni Luoma, Darryl Estrada-Zavala, Luis Gasco, Sampo Pyysalo, Alfonso Valencia, Martin Krallinger, Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical–protein relations, Database, Volume 2023, 2023, baad080

@article{miranda2023overview,  title={Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical--protein relations},  author={Miranda-Escalada, Antonio and Mehryary, Farrokh and Luoma, Jouni and Estrada-Zavala, Darryl and Gasco, Luis and Pyysalo, Sampo and Valencia, Alfonso and Krallinger, Martin},  journal={Database},  volume={2023},  pages={baad080},  year={2023},  publisher={Oxford University Press UK} }
Description
This dataset contains a knowledge graph built from PubMed dump abstracts (December 2021). A NER system has been applied to each of the abstracts to extract mentions of type "CHEMICAL" and "GENE", as well as a RE system to detect existing relations between these mentions such as ACTIVATOR, INHIBITOR, AGONIST or PRODUCT_OF, among others (see article for a full list of relations considered).
Given the volume of the dataset, the repository is divided into 1114 folders. Each of these folders contains a chunk of PubMed abstracts, entities and relationships, divided into the following 3 files:

abstracts.tsv: Tabular file in which each line represents a pubmed document. The file has 3 columns:

Pubmed_id: Numerical identifier of the document in PubMed
Title: Title of the document
Abstract: Abstract text.
entities.tsv: List of the entities extracted from the abstracts. Each line represents an extracted entity, and has 5 columns:

Pubmed_id: Numerical identifier of the document in PubMed
Mention_id: Numerical identifier of the mention in the document.
Entity_type: Type of mention. It can be CHEMICAL or GENE.
Span_ini: Index of the first character of the annotated span in the text
Span_end: Index of the first character after the annotated span.
Span: Text span of the annotation
relations.tsv: File of existing relations between entities. Each line represents a relationship, and has the following fields:

Pubmed_id: Numerical identifier of the document in PubMed
Relation_type: DrugProt relation type among entities/arguments.
Arg1: Mention of CHEMICAL
Arg2: Mention of GENE
 
Files:

drugprot-silver-standard-kg.zip : Folders with the files previously explained
 
Related resources:

Web
DrugProt corpus
Evaluation library
Online evaluation (CodaLab)
Relation annotation guidelines
Gene and protein annotation guidelines
Chemicals and drugs annotation guidelines
FAQ
DrugProt Large Scale Additional SubTrack
DrugProt Large Scale document collection protocol
DrugProt Complete PubMed Knowledge Graph
DOI: 10.5281/zenodo.7252201
title: DrugProt Silver Standard Knowledge Graph

description: DrugProt Silver Standard Knowledge Graph
 
 
Please cite if you use any DrugProt resource:
Antonio Miranda-Escalada, Farrokh Mehryary, Jouni Luoma, Darryl Estrada-Zavala, Luis Gasco, Sampo Pyysalo, Alfonso Valencia, Martin Krallinger, Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical–protein relations, Database, Volume 2023, 2023, baad080

@article{miranda2023overview,  title={Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical--protein relations},  author={Miranda-Escalada, Antonio and Mehryary, Farrokh and Luoma, Jouni and Estrada-Zavala, Darryl and Gasco, Luis and Pyysalo, Sampo and Valencia, Alfonso and Krallinger, Martin},  journal={Database},  volume={2023},  pages={baad080},  year={2023},  publisher={Oxford University Press UK} }
Miranda, Antonio, et al. "Overview of DrugProt BioCreative VII track: quality evaluation and large scale text mining of drug-gene/protein relations." Proceedings of the seventh BioCreative challenge evaluation workshop. 2021.

@inproceedings{miranda2021overview,  title={Overview of DrugProt BioCreative VII track: quality evaluation and large scale text mining of drug-gene/protein relations},  author={Miranda, Antonio and Mehryary, Farrokh and Luoma, Jouni and Pyysalo, Sampo and Valencia, Alfonso and Krallinger, Martin},  booktitle={Proceedings of the seventh BioCreative challenge evaluation workshop},  year={2021} }
Description
 
 
Files:

drugprot-silver-standard-kg.zip : JSON files with the relations predicted by the DrugProt systems and their precision
large_scale_network_abstracts.tsv : PubMed abstracts
large_scale_network_entities.tsv : CHEMICAL/drug and GENE/protein entities predicted by DrugProt NER Taggers
large_scale_network_pmids.txt : list of PMIDs
 
Related resources:

Web
DrugProt corpus
Evaluation library
Online evaluation (CodaLab)
Relation annotation guidelines
Gene and protein annotation guidelines
Chemicals and drugs annotation guidelines
FAQ
DrugProt Large Scale Additional SubTrack
DrugProt Large Scale document collection protocol
DOI: 10.5281/zenodo.7252202
title: DrugProt Silver Standard Knowledge Graph

description: DrugProt Silver Standard Knowledge Graph
 
 
Please cite if you use any DrugProt resource:
Antonio Miranda-Escalada, Farrokh Mehryary, Jouni Luoma, Darryl Estrada-Zavala, Luis Gasco, Sampo Pyysalo, Alfonso Valencia, Martin Krallinger, Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical–protein relations, Database, Volume 2023, 2023, baad080

@article{miranda2023overview,  title={Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical--protein relations},  author={Miranda-Escalada, Antonio and Mehryary, Farrokh and Luoma, Jouni and Estrada-Zavala, Darryl and Gasco, Luis and Pyysalo, Sampo and Valencia, Alfonso and Krallinger, Martin},  journal={Database},  volume={2023},  pages={baad080},  year={2023},  publisher={Oxford University Press UK} }
Miranda, Antonio, et al. "Overview of DrugProt BioCreative VII track: quality evaluation and large scale text mining of drug-gene/protein relations." Proceedings of the seventh BioCreative challenge evaluation workshop. 2021.

@inproceedings{miranda2021overview,  title={Overview of DrugProt BioCreative VII track: quality evaluation and large scale text mining of drug-gene/protein relations},  author={Miranda, Antonio and Mehryary, Farrokh and Luoma, Jouni and Pyysalo, Sampo and Valencia, Alfonso and Krallinger, Martin},  booktitle={Proceedings of the seventh BioCreative challenge evaluation workshop},  year={2021} }
Description
 
 
Files:

drugprot-silver-standard-kg.zip : JSON files with the relations predicted by the DrugProt systems and their precision
large_scale_network_abstracts.tsv : PubMed abstracts
large_scale_network_entities.tsv : CHEMICAL/drug and GENE/protein entities predicted by DrugProt NER Taggers
large_scale_network_pmids.txt : list of PMIDs
 
Related resources:

Web
DrugProt corpus
Evaluation library
Online evaluation (CodaLab)
Relation annotation guidelines
Gene and protein annotation guidelines
Chemicals and drugs annotation guidelines
FAQ
DrugProt Large Scale Additional SubTrack
DrugProt Large Scale document collection protocol
DOI: 10.5519/qd.shza65rm
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (611 records)
DOI: 10.22008/fk2/bgkhsp
title: Geochemical analyses of Soil samples from Greenland

description: Samples of surface material, i.e. stream sediment, soil, and scree have been collected over large parts of Greenland from 1974 onwards mainly as part of mineral exploration programmes and more broadly for geochemical mapping by means of stream sediment (Steenfelt 1999, 2001). Following various sample preparation procedures, like drying and screening, making concentrates of heavy minerals from stream sediment or soil, certain fractions of the samples have been chemically analysed at diverse laboratories where a range of analytical methods were applied as they became available over the years. The present dataset contains the analytical data from soil samples as they were received from the laboratories together with administrative data, including sample location and grain-size fraction analysed. Many samples have been analysed at more than one laboratory and by more than one method and the analytical data for each sample and grain size fraction are listed lab by lab and method by method in the same row. Five samples are used in the Geochemical Atlas of Stream sediment samples of South-West Greenland (Steenfelt, 1999). 204 samples from North Greenland have been used in the Geochemical Atlas of Stream sediment samples for North Greenland (Thrane 2011). In addition soil samples were collected in the SEGMENT project (Kolb et al. 2016)