cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5255/ukda-sn-855629
title: Promoting Social Inclusion in Housing with Care and Support for Older People in England and Wales, 2019-2021

description: This collection consists of the following mixed-methods data: 1) survey data generated from a survey of older people living in housing with care and support (collected 2019-20); 2) qualitative interview transcripts from interviews with 72 older people living in housing with care and support (cross-sectional and longitudinal interviews) (collected 2019-21); and 3) qualitative interview transcripts from interviews with staff supporting older people in housing schemes (this includes managers, support staff and carers) (collected 2020-21).
DOI: 10.5281/zenodo.6405496
title: Clustering has a meaning: optimization of angular similarity to detect geometric anomalies in geological terrains - Input and processed data.

description: This companion dataset relates to the manuscript "<strong>Clustering has a meaning: optimization of angular similarity </strong> <strong>to detect geometric anomalies in geological terrains</strong>", by Michał P. Michalak, Lesław Teper, Florian Wellmann, Jerzy Żaba, Krzysztof Gaidzik, Marcin Kostur, Yuriy P. Maystrenko, Paulina Leonowicz The archive contains the input and processed data. The input data contains XYZ coordinates of points documenting the investigated interfaces. The output files contains calculated orientations and coordinates of vectors. The output files can be processed in RStudio.
DOI: 10.5281/zenodo.5552647
title: Clustering has a meaning: optimization of angular similarity to detect geometric anomalies in geological terrains - Input and processed data.

description: This companion dataset relates to the manuscript "<strong>Clustering has a meaning: optimization of angular similarity </strong> <strong>to detect geometric anomalies in geological terrains</strong>", by Michał P. Michalak, Lesław Teper, Florian Wellmann, Jerzy Żaba, Krzysztof Gaidzik, Marcin Kostur, Yuriy P. Maystrenko, Paulina Leonowicz The archive contains the input and processed data. The input data contains XYZ coordinates of points documenting the investigated interfaces. The output files contains calculated orientations and coordinates of vectors. The output files can be processed in RStudio.
DOI: 10.7479/672a-zd40
title: Georeferencing Germany: Presenting coordinates and uncertainty measures for administrative boundaries

description: Often historic collection data contains verbatim place names for specimens collected. Some of these places are vague and non-specific, referring to entire states or cities, while others were more detailed descriptions. This georeferencing process ultimately resulted in a searchable table of place names in Germany, at different administrative levels. For each, there was a latitude, longitude and uncertainty radius assigned. These can be used in most collection databases as it provides a way to pinpoint vague locations.
DOI: 10.17028/rd.lboro.20300814
title: Supplementary information files for An open workflow to gain insights about low-likelihood high-impact weather events from initialized predictions

description: Supplementary information files for article An<strong> </strong>open workflow to gain insights about low-likelihood high-impact weather events from initialized predictions Abstract Low-likelihood weather events can cause dramatic impacts, especially when they are unprecedented. In 2020, amongst other high-impact weather events, UK floods caused more than £300 million damage, prolonged heat over Siberia led to infrastructure failure and permafrost thawing, while wildfires ravaged California. Such rare phenomena cannot be studied well from historical records or reanalysis data. One way to improve our awareness is to exploit ensemble prediction systems, which represent large samples of simulated weather events. This ‘UNSEEN’ method has been successfully applied in several scientific studies, but uptake is hindered by large data and processing requirements, and by uncertainty regarding the credibility of the simulations. Here, we provide a protocol to apply and ensure credibility of UNSEEN for studying low-likelihood high-impact weather events globally, including an open workflow based on Copernicus Climate Change Services (C3S) seasonal predictions. Demonstrating the workflow using European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5, we find that the 2020 March–May Siberian heatwave was predicted by one of the ensemble members; and that the record-shattering August 2020 California-Mexico temperatures were part of a strong increasing trend. However, each of the case studies exposes challenges with respect to the credibility of UNSEEN and the sensitivity of the outcomes to user decisions. We conclude that UNSEEN can provide new insights about low-likelihood weather events when the decisions are transparent, and the challenges and sensitivities are acknowledged. Anticipating plausible low-likelihood extreme events and uncovering unforeseen hazards under a changing climate warrants further research at the science-policy interface to manage high impacts.
DOI: 10.17028/rd.lboro.20300814.v1
title: Supplementary information files for An open workflow to gain insights about low-likelihood high-impact weather events from initialized predictions

description: Supplementary information files for article An<strong> </strong>open workflow to gain insights about low-likelihood high-impact weather events from initialized predictions Abstract Low-likelihood weather events can cause dramatic impacts, especially when they are unprecedented. In 2020, amongst other high-impact weather events, UK floods caused more than £300 million damage, prolonged heat over Siberia led to infrastructure failure and permafrost thawing, while wildfires ravaged California. Such rare phenomena cannot be studied well from historical records or reanalysis data. One way to improve our awareness is to exploit ensemble prediction systems, which represent large samples of simulated weather events. This ‘UNSEEN’ method has been successfully applied in several scientific studies, but uptake is hindered by large data and processing requirements, and by uncertainty regarding the credibility of the simulations. Here, we provide a protocol to apply and ensure credibility of UNSEEN for studying low-likelihood high-impact weather events globally, including an open workflow based on Copernicus Climate Change Services (C3S) seasonal predictions. Demonstrating the workflow using European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5, we find that the 2020 March–May Siberian heatwave was predicted by one of the ensemble members; and that the record-shattering August 2020 California-Mexico temperatures were part of a strong increasing trend. However, each of the case studies exposes challenges with respect to the credibility of UNSEEN and the sensitivity of the outcomes to user decisions. We conclude that UNSEEN can provide new insights about low-likelihood weather events when the decisions are transparent, and the challenges and sensitivities are acknowledged. Anticipating plausible low-likelihood extreme events and uncovering unforeseen hazards under a changing climate warrants further research at the science-policy interface to manage high impacts.
DOI: 10.5281/zenodo.6827112
title: Raw sequencing data PhD Mixoplankton spatio-temporal diversity and its environmental drivers in the North Sea

description: Raw sequencing data PhD Mixoplankton spatio-temporal diversity and its environmental drivers in the North Sea
DOI: 10.5281/zenodo.6827111
title: Raw sequencing data PhD Mixoplankton spatio-temporal diversity and its environmental drivers in the North Sea

description: Raw sequencing data PhD Mixoplankton spatio-temporal diversity and its environmental drivers in the North Sea
DOI: 10.5255/ukda-sn-855832
title: Face Identification within Realistic Contexts, 2019-2022

description: In many situations, security relies on accurate person identification. At airports, routine person identifications are based on face matching, in which passport officers have to decide whether the face photograph in a presented identity document actually depicts its bearer. This project investigated these person identifications by examining face matching with laboratory paradigms and in virtual reality. Data from a series of studies is provided that describe how to build and validate person avatars for such experimentation in VR and examine the role of different factors in face matching (similarity; moles; mismatch frequency; distractions).
DOI: 10.5281/zenodo.6598376
title: Ground obeservation data (meteorological factors and air pollution) in Greater Bay Area, 2015 - 2021

description: Ground observation data used in the paper <em>Development of an LSTM-Broadcasting deep-learning framework for regional air pollution forecast improvement</em>.
DOI: 10.5281/zenodo.6827327
title: Data for "Deterministic measurement of a Rydberg superatom qubit via cavity-enhanced single-photon emission"

description: This dataset is for the research article "Deterministic measurement of a Rydberg superatom qubit via cavity-enhanced single-photon emission".
DOI: 10.5281/zenodo.6827326
title: Data for "Deterministic measurement of a Rydberg superatom qubit via cavity-enhanced single-photon emission"

description: This dataset is for the research article "Deterministic measurement of a Rydberg superatom qubit via cavity-enhanced single-photon emission".
DOI: 10.5281/zenodo.6824724
title: Semi-automated Quantitative Morphometric Analysis of E18 Rat Hippocampal Neurons from 0.5 to 6 Days In Vitro

description: This is the dataset presented in "Semi-automated quantitatve evaluation of neuron developmental morphology <em>in vitro</em> using the change-point test" by AS Liao, W Cui, VS Webster-Wood, and YJ Zhang (submitted to Neuroinformatics 2022).
description: This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE1745016, the Faculty Early Career Development Program under Grant No. ECCS-2044785 and the LEAP HI Program under Grant No. CMMI-1953323. The authors were also supported in part by a PITA (Pennsylvania Infrastructure Technology Alliance) grant and a PMFI (Pennsylvania Manufacturing Fellows Initiative) grant. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
description: {"references": ["Liao, Ashlee; Cui, Wenxin; Zhang, Yongjie Jessica; Webster-Wood, Victoria. \"Quantitative evaluation of neuron developmental morphology in vitro using the change-point test.\" Submitted to Neuroinformatics 2022.", "Liao, Ashlee; Cui, Wenxin; Zhang, Yongjie Jessica; Webster-Wood, Victoria. \"Neuron-Growth-Morphometrics-In-Vitro\" GitHub Repository. DOI: 10.5281/zenodo.6415605", "Byrne, RW; Noser, R; Bates, LA; Jupp, PE. \"How did they get here from there? Detecting changes of direction in terrestrial ranging.\" Animal Behavior. (2009). 77(3): 619-631. DOI: 10.1016/j.anbehav.2008.11.014.", "Meijering, E; Jacob, M; Sarria, J-CF; Steiner, P; Hirling, H; Unser, M. \"Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images.\" Cytometry. (2004). 58A(2): 167-176. DOI: 10.1002/cyto.a.20022"]}
DOI: 10.5281/zenodo.6415473
title: Semi-automated Quantitative Morphometric Analysis of E18 Rat Hippocampal Neurons from 0.5 to 6 Days In Vitro

description: This is the dataset presented in "Semi-automated quantitatve evaluation of neuron developmental morphology <em>in vitro</em> using the change-point test" by AS Liao, W Cui, VS Webster-Wood, and YJ Zhang (submitted to Neuroinformatics 2022).
description: This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE1745016, the Faculty Early Career Development Program under Grant No. ECCS-2044785 and the LEAP HI Program under Grant No. CMMI-1953323. The authors were also supported in part by a PITA (Pennsylvania Infrastructure Technology Alliance) grant and a PMFI (Pennsylvania Manufacturing Fellows Initiative) grant. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
description: {"references": ["Liao, Ashlee; Cui, Wenxin; Zhang, Yongjie Jessica; Webster-Wood, Victoria. \"Quantitative evaluation of neuron developmental morphology in vitro using the change-point test.\" Submitted to Neuroinformatics 2022.", "Liao, Ashlee; Cui, Wenxin; Zhang, Yongjie Jessica; Webster-Wood, Victoria. \"Neuron-Growth-Morphometrics-In-Vitro\" GitHub Repository. DOI: 10.5281/zenodo.6415605", "Byrne, RW; Noser, R; Bates, LA; Jupp, PE. \"How did they get here from there? Detecting changes of direction in terrestrial ranging.\" Animal Behavior. (2009). 77(3): 619-631. DOI: 10.1016/j.anbehav.2008.11.014.", "Meijering, E; Jacob, M; Sarria, J-CF; Steiner, P; Hirling, H; Unser, M. \"Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images.\" Cytometry. (2004). 58A(2): 167-176. DOI: 10.1002/cyto.a.20022"]}
DOI: 10.6084/m9.figshare.16558056
title: Larval age at emergence

description: Age at emergence in western honey bee larvae fed different diets.<br>Key to variable names & types:<br>colony -- colony ID (categorical)plate -- test plate ID (categorical)well -- test plate well ID (categorical)treatment -- diet type (categorical)age_emergence -- age at emergence (days) (integer)<br>
DOI: 10.6084/m9.figshare.16558056.v2
title: Larval age at emergence

description: Age at emergence in western honey bee larvae fed different diets.<br>Key to variable names & types:<br>colony -- colony ID (categorical)plate -- test plate ID (categorical)well -- test plate well ID (categorical)treatment -- diet type (categorical)age_emergence -- age at emergence (days) (integer)<br>
DOI: 10.6084/m9.figshare.16558056.v1
title: Larval age at emergence

description: Age at emergence in western honey bee larvae fed different diets.<br>Key to variable names & types:<br>colony -- colony ID (categorical)plate -- test plate ID (categorical)well -- test plate well ID (categorical)treatment -- diet type (categorical)age_emergence -- age at emergence (days) (integer)<br>
DOI: 10.7910/dvn/sijba3
title: Replication Data for: prova

description: File di prova
DOI: 10.6084/m9.figshare.16558053
title: Larval age at pupation

description: Age at pupation in western honey bee larvae fed different diets.<br>Key to variable names & types:<br>colony -- colony ID (categorical)plate -- test plate ID (categorical)well -- test plate well ID (categorical)treatment -- diet type (categorical)age_pupation -- age at pupation (days) (integer)<br>
DOI: 10.6084/m9.figshare.16558053.v2
title: Larval age at pupation

description: Age at pupation in western honey bee larvae fed different diets.<br>Key to variable names & types:<br>colony -- colony ID (categorical)plate -- test plate ID (categorical)well -- test plate well ID (categorical)treatment -- diet type (categorical)age_pupation -- age at pupation (days) (integer)<br>
DOI: 10.6084/m9.figshare.16558053.v1
title: Larval age at pupation

description: Age at pupation in western honey bee larvae fed different diets.<br>Key to variable names & types:<br>colony -- colony ID (categorical)plate -- test plate ID (categorical)well -- test plate well ID (categorical)treatment -- diet type (categorical)age_pupation -- age at pupation (days) (integer)<br>
DOI: 10.5066/p9qrl294
title: Geospatial dataset of the bathymetry, hydrodynamics, water quality, infrastructure, and channel condition of the Old Erie Canal from DeWitt to its junction with the current Erie Canal in Verona, near Rome, New York, 2018-19

description: This data release contains spatial datasets of bathymetry, water velocity, water quality, and infrastructure of a 30.8 mile reach of the Old Erie Canal between the Town of DeWitt and its junction with the current Erie Canal of the New York State Canal System in Verona, near Rome, New York during 2018 and 2019, and is released in support of Open-File Report 2021-1125; Wernly, J.F., 2022, Characterization of the bathymetry, hydrodynamics, water quality, infrastructure, and channel condition of the Old Erie Canal from DeWitt to its junction with the current Erie Canal in Verona, near Rome, New York, 2018–19: U.S. Geological Survey Open-File Report 2021–1125, 75 p., https://doi.org/10.3133/ofr20211125
DOI: 10.34740/kaggle/dsv/3938413
title: COVID Vaccination in World (updated daily)
DOI: 10.5066/p9ib1gws
title: Fishway Structure Data in the Eastern United States

description: These data are a compilation of fishway structures collected by the Atlantic States Marine Fisheries Commission state representatives at the request of the U.S. Geological Survey. The variables included within this dataset range from locality information and structure metadata (eg. latitude/longitude and year of construction) to metrics specifically about the fishway structure (eg. fishway width). The dataset ranges in dates of construction from 1882 to 2020 and includes fishways from all states on the eastern coast of the United States. Requests were sent to the Atlantic States Marine Fisheries Commission state representatives to collect fishway data from the areas within their responsibility. The state representatives were requested [...]
DOI: 10.5281/zenodo.6818606
title: gnomAD SQLite database V3.1.2

description: This package scales the huge gnomAD files (on average ~120G/chrom) to a SQLite database with a size of 34G for WGS v2.1.1 (261.942.336 variants) and 98G for WGS v3.1.2 (about 759.302.267 variants), and allows scientists to look for various variant annotations present in gnomAD (i.e. Allele Count, Depth, Minor Allele Frequency, etc. - here you can find all selected features given the genome version). (A query containing 300.000 variants takes ~40s.) It extracts from a gnomAD vcf about 23 variant annotations. You can find further infromation about the exact fields here. gnomAD SQLite database V3.1.2
DOI: 10.5281/zenodo.6818605
title: gnomAD SQLite database V3.1.2

description: This package scales the huge gnomAD files (on average ~120G/chrom) to a SQLite database with a size of 34G for WGS v2.1.1 (261.942.336 variants) and 98G for WGS v3.1.2 (about 759.302.267 variants), and allows scientists to look for various variant annotations present in gnomAD (i.e. Allele Count, Depth, Minor Allele Frequency, etc. - here you can find all selected features given the genome version). (A query containing 300.000 variants takes ~40s.) It extracts from a gnomAD vcf about 23 variant annotations. You can find further infromation about the exact fields here. gnomAD SQLite database V3.1.2
DOI: 10.5517/ccdc.csd.cc2bjqk5
title: CCDC 2161027: Experimental Crystal Structure Determination

description: Related Article: Magdalena Barwiolek, Dominika Jankowska, Anna Kaczmarek-Kędziera, Tadeusz M. Muzioł|2022|Polyhedron|224|116004|doi:10.1016/j.poly.2022.116004
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.cc2bjql6
title: CCDC 2161028: Experimental Crystal Structure Determination

description: Related Article: Magdalena Barwiolek, Dominika Jankowska, Anna Kaczmarek-Kędziera, Tadeusz M. Muzioł|2022|Polyhedron|224|116004|doi:10.1016/j.poly.2022.116004
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.cc2bjqp9
title: CCDC 2161031: Experimental Crystal Structure Determination

description: Related Article: Magdalena Barwiolek, Dominika Jankowska, Anna Kaczmarek-Kędziera, Tadeusz M. Muzioł|2022|Polyhedron|224|116004|doi:10.1016/j.poly.2022.116004
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: 10.6084/m9.figshare.15157403
title: Machine learning in glaucoma: a bibliometric analysis comparing computer science and medical fields’ research

description: The aim of this study was to analyze the current literature on the use of machine learning in glaucoma, comparing the characteristics and citations received by articles on computer science focus or medical focus. We performed a search using the Scopus database on 28th of January 2021 using appropriate keywords for journal articles and conference articles that discussed glaucoma in the context of machine and deep learning. We used Scopus-based field classification and compared different characteristics and citation metrics between articles classified as belonging to the computer science field, medical field, or both fields combined. A total of 858 documents resulted from the search. Upon comparing the mean citation received by publications in the computer science field and medical field, we found a significant difference (p = 0.013). The highest mean citation received was for articles in the combined fields with a mean of 26.2 (SD 41.7), and the least mean citations received by articles in computer science field with a mean of 13.7 (SD 34.6). Most articles related to machine learning assessment of glaucoma were classified as computer science articles. Articles that belong to medical or both fields combined received higher number of citations.
DOI: 10.6084/m9.figshare.15157403.v1
title: Machine learning in glaucoma: a bibliometric analysis comparing computer science and medical fields’ research

description: The aim of this study was to analyze the current literature on the use of machine learning in glaucoma, comparing the characteristics and citations received by articles on computer science focus or medical focus. We performed a search using the Scopus database on 28th of January 2021 using appropriate keywords for journal articles and conference articles that discussed glaucoma in the context of machine and deep learning. We used Scopus-based field classification and compared different characteristics and citation metrics between articles classified as belonging to the computer science field, medical field, or both fields combined. A total of 858 documents resulted from the search. Upon comparing the mean citation received by publications in the computer science field and medical field, we found a significant difference (p = 0.013). The highest mean citation received was for articles in the combined fields with a mean of 26.2 (SD 41.7), and the least mean citations received by articles in computer science field with a mean of 13.7 (SD 34.6). Most articles related to machine learning assessment of glaucoma were classified as computer science articles. Articles that belong to medical or both fields combined received higher number of citations.