cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12286715.v1
title: Preanalytical variables that affect the outcome of cell-free DNA measurements

description: Fragments of cell-free DNA (cfDNA) in human body fluids often carry disease-specific alterations and are now widely recognized as ideal biomarkers for the detection and monitoring of genomic disorders, especially cancer, that are normally difficult to examine noninvasively. However, the conversion of promising research findings into tools useful in routine clinical testing of cancer has been a slow-moving process. A major reason is that the diagnostic sensitivity and specificity of cfDNA-based clinical assays are negatively impacted by a combination of suboptimal and inter-institutional differences in preanalytical procedures. The most prominent factors include: (i) a poor understanding of the biological factors that determine the characteristics of the cfDNA population in a biospecimen prior to collection, (ii) inattention to how cfDNA with different structures and physical properties are affected differently by a given preanalytical step, and (iii) the sheer number of possible conditions that can be selected from for each preanalytical step along with a continually expanding menu of commercial products that often show varying degrees of bias and efficiency. The convergence of these variables makes it difficult for research groups and institutions to reach a consensus on optimal preanalytical procedures and a challenging task to establish widely applied standards, which ultimately hamper the development of cfDNA assays that are fit for broad clinical implementation. In this review, we follow a systematic approach to explore the most confounding preanalytical factors that affect the outcome of cfDNA measurements.
DOI: 10.5281/zenodo.4284733
title: Shortcomings of Event-Based Metrics [Supplement to PhD "Machine-Actionable Assessment of Research Data Products"]

description: This deposition includes a tabular overview of all publications analyzed by my PhD to identify and classify shortcomings of event-based metrics for research data products. The tabular overview is linked via its field "Key" to the bibtex file which holds the bibliographic information to replicate the results shown in the table. The deposition is supplementary material to the dissertation "Machine-Actionable Assessment of Research Data Products" (Tobias Weber, yet unpublished), especially chapter 3.
DOI: 10.5281/zenodo.4284732
title: Shortcomings of Event-Based Metrics [Supplement to PhD "Machine-Actionable Assessment of Research Data Products"]

description: This deposition includes a tabular overview of all publications analyzed by my PhD to identify and classify shortcomings of event-based metrics for research data products. The tabular overview is linked via its field "Key" to the bibtex file which holds the bibliographic information to replicate the results shown in the table. The deposition is supplementary material to the dissertation "Machine-Actionable Assessment of Research Data Products" (Tobias Weber, yet unpublished), especially chapter 3.
DOI: 10.5281/zenodo.4284737
title: Evaluation Data [Supplement to PhD "Machine-Actionable Assessment of Research Data Products"]

description: This deposition consists of the raw data used for the evaluation chapter 6 of the dissertation "Machine-Actionable Assessment of Research Data Products" (Tobias Weber, yet unpublished).
DOI: 10.5281/zenodo.4284736
title: Evaluation Data [Supplement to PhD "Machine-Actionable Assessment of Research Data Products"]

description: This deposition consists of the raw data used for the evaluation chapter 6 of the dissertation "Machine-Actionable Assessment of Research Data Products" (Tobias Weber, yet unpublished).
DOI: 10.5281/zenodo.4267589
title: SCYM Maize Yield Maps (Low Resolution) from Deines et al. 2021, Lobell et al. 2020

description: Annual maize yield maps for the US Corn Belt for 1999-2018, aggregated to 10 km resolution. Yield maps are produced with the SCYM methodology at 30 m resolution using Landsat satellite data. The original high resolution yield maps are aggregated to a 10 km grid resolution to preserve privacy. Dataset creation is described in Deines et al., 2020 in Remote Sensing of Environment (open access). The 2008-2018 map dataset described therein was extended back to 1999 for Lobell et al. 2020 in Nature Food (read-only accessible version). Maps for 2008-2018 use the USDA's Cropland Data Layers to identify maize pixels; maps for 1999-2007 use the Corn-Soy Data Layer produced by Wang et al. 2000 (data + manuscript are open access). <strong>Metadata</strong> Yield values are provided in metric tons/hectare. No Data value = -999 (grid cells outside of the study area + cells with &lt;1% coverage of SCYM 30 m yield maps) Map projection: EPSG:5070, CONUS Albers Equal Area Preferred citation(s): Deines, J.M., R. Patel, S. Lang, W. Dado, & D.B. Lobell. 2020. A million kernels of truth: Insights in to scalable satellite maize yield mapping and yield gap analysis from an extensive ground dataset in the US Corn Belt. <em>Remote Sensing of Environment. </em>https://doi.org/10.1016/j.rse.2020.112174 D.B. Lobell, J.M. Deines, & S. Di Tomasso. 2020. Changes in the drought sensitivity of U.S. maize yields. <em>Nature F</em><em>o</em><em>o</em><em>d</em> 1:729-735. https://doi.org/10.1038/s43016-020-00165-w
DOI: 10.5281/zenodo.4267588
title: SCYM Maize Yield Maps (Low Resolution) from Deines et al. 2021, Lobell et al. 2020

description: Annual maize yield maps for the US Corn Belt for 1999-2018, aggregated to 10 km resolution. Yield maps are produced with the SCYM methodology at 30 m resolution using Landsat satellite data. The original high resolution yield maps are aggregated to a 10 km grid resolution to preserve privacy. Dataset creation is described in Deines et al., 2020 in Remote Sensing of Environment (open access). The 2008-2018 map dataset described therein was extended back to 1999 for Lobell et al. 2020 in Nature Food (read-only accessible version). Maps for 2008-2018 use the USDA's Cropland Data Layers to identify maize pixels; maps for 1999-2007 use the Corn-Soy Data Layer produced by Wang et al. 2000 (data + manuscript are open access). <strong>Metadata</strong> Yield values are provided in metric tons/hectare. No Data value = -999 (grid cells outside of the study area + cells with &lt;1% coverage of SCYM 30 m yield maps) Map projection: EPSG:5070, CONUS Albers Equal Area Preferred citation(s): Deines, J.M., R. Patel, S. Lang, W. Dado, & D.B. Lobell. 2020. A million kernels of truth: Insights in to scalable satellite maize yield mapping and yield gap analysis from an extensive ground dataset in the US Corn Belt. <em>Remote Sensing of Environment. </em>https://doi.org/10.1016/j.rse.2020.112174 D.B. Lobell, J.M. Deines, & S. Di Tomasso. 2020. Changes in the drought sensitivity of U.S. maize yields. <em>Nature F</em><em>o</em><em>o</em><em>d</em> 1:729-735. https://doi.org/10.1038/s43016-020-00165-w
DOI: 10.5281/zenodo.3543791
title: Inland limits to diffusion of thinning along Greenland Ice Sheet outlet glaciers

description: This dataset contains limits to the inland diffusion of terminus-initiated thinning along 141 Greenland Ice Sheet outlet glaciers. The data is separated in 187 netcdf files, which represent individual outlet glaciers as well as individual branches of glaciers. Files are named <em>glacier XXXX.nc </em>where <em>XXXX </em>is the identifier for an individual glacier or branch. Identifiers that begin with a letter ('a', 'b', 'c', or 'd') represent a branch of an outlet glacier. Each file contains geometry, dynamic thinning, Peclet number Example code that can be used to read and plot the data can be found here: https://github.com/dfelikson/GrIS-thinning-limits-and-knickpoints.
DOI: 10.5281/zenodo.4090891
title: Inland limits to diffusion of thinning along Greenland Ice Sheet outlet glaciers

description: This dataset contains limits to the inland diffusion of terminus-initiated thinning along 141 Greenland Ice Sheet outlet glaciers. The data is separated in 187 netcdf files, which represent individual outlet glaciers as well as individual branches of glaciers. Files are named <em>glacier XXXX.nc </em>where <em>XXXX </em>is the identifier for an individual glacier or branch. Identifiers that begin with a letter ('a', 'b', 'c', or 'd') represent a branch of an outlet glacier. Each file contains glacier geometry, dynamic thinning, Peclet number, and identified knickpoints along 6-18 flowlines spanning the width of each glacier. Example code that can be used to read and plot the data can be found here: https://github.com/dfelikson/GrIS-thinning-limits-and-knickpoints.
DOI: 10.5281/zenodo.4284759
title: Inland limits to diffusion of thinning along Greenland Ice Sheet outlet glaciers

description: This dataset contains limits to the inland diffusion of terminus-initiated thinning along 141 Greenland Ice Sheet outlet glaciers. The data is separated in 187 files and each file contains data for an individual outlet glacier or a branch of an outlet glacier. Shapefiles and NetCDF files are provided with data for 6 primary flowlines, and up to 18 iterated flowlines, spanning the width of each glacier. The iterated flowlines are used to find the furthest inland thinning limits (see the associated publication for more details). Files are named <em>glacierXXXX </em>where <em>XXXX </em>is the identifier for an individual glacier or branch. Identifiers that begin with a letter ('a', 'b', 'c', or 'd') represent a separate branch of an outlet glacier. Shapefiles filenames have an additional <em>_iterNN </em>suffix, where <em>NN</em> identifies the iteration number of that set of flowlines. The shapefiles contain multiple features, each of which is one flowline. Each flowline has a "flowline" attribute, a two-digit identifier that corresponds to the same identifier in the NetCDF files. The NetCDF files contains glacier geometry, dynamic thinning, Peclet number, and identified knickpoints, extracted and calculated along the primary and iterated flowlines. The flowlines in the NetCDF files have a regular 50-meter spacing between nodes, whereas the flowlines in the shapefiles have a coarser and irregular spacing. More information on how the flowlines were derived and how data was extracted and calculated is in the associated publication (Felikson et al., 2020), with a DOI to be provided. Example code that can be used to read and plot the data can be obtained at http://doi.org/10.5281/zenodo.4284715.
DOI: 10.5281/zenodo.3543790
title: Inland limits to diffusion of thinning along Greenland Ice Sheet outlet glaciers

description: This dataset contains limits to the inland diffusion of terminus-initiated thinning along 141 Greenland Ice Sheet outlet glaciers. The data is separated in 187 files and each file contains data for an individual outlet glacier or a branch of an outlet glacier. Shapefiles and NetCDF files are provided with data for 6 primary flowlines, and up to 18 iterated flowlines, spanning the width of each glacier. The iterated flowlines are used to find the furthest inland thinning limits (see the associated publication for more details). Files are named <em>glacierXXXX </em>where <em>XXXX </em>is the identifier for an individual glacier or branch. Identifiers that begin with a letter ('a', 'b', 'c', or 'd') represent a separate branch of an outlet glacier. Shapefiles filenames have an additional <em>_iterNN </em>suffix, where <em>NN</em> identifies the iteration number of that set of flowlines. The shapefiles contain multiple features, each of which is one flowline. Each flowline has a "flowline" attribute, a two-digit identifier that corresponds to the same identifier in the NetCDF files. The NetCDF files contains glacier geometry, dynamic thinning, Peclet number, and identified knickpoints, extracted and calculated along the primary and iterated flowlines. The flowlines in the NetCDF files have a regular 50-meter spacing between nodes, whereas the flowlines in the shapefiles have a coarser and irregular spacing. More information on how the flowlines were derived and how data was extracted and calculated is in the associated publication (Felikson et al., 2020), with a DOI to be provided. Example code that can be used to read and plot the data can be obtained at http://doi.org/10.5281/zenodo.4284715.
DOI: 10.5281/zenodo.4284782
title: Ethical Perspectives in AI: A Two-folded Exploratory Study From Literature and Active Development Projects - Supplementary Material

description: This is the Supplementary Material provided for the work accepted on HICSS 2021. Title of work: Ethical Perspectives in AI: A Two-folded Exploratory Study From Literature and Active Development Projects. This dataset depicts 589 GitHub README files explored in our article. We devised each repository into 4 categories. AI Applications (78), reference lists (486), Explainable AI tool (6), Ethical AI tool (15). Moreoveer, 4 repositories could not be found, and 181 of them had a Programming Language. In addition, we classified them according to our judgement of Interesting (82) or Great (33), regarding the objectives of our work. 75 repositories were not related to AI Ethics, and 7 of them mentioned COVID-19 in their repositories in some manner. Highlighted repositories in red pertain to categories 3 and 4, that is, tools for implementing AI Ethics, while those in yellow are marked as “Great”. Explainable AI tool and Ethical AI tool were added to 21 occurrences of tools for implementing AI ethics publicly available in repositories. Furthermore, it is seen that many papers open-sourced their codes on GitHub (as in https://github.com/lopusz/awesome-interpretable-machine-learning), meaning that the academic field has also made good progress in implementing ethics in AI, hence, the combined energy of both sources fosters an enhanced debate and stimulates progress towards AI ethics in practice.
DOI: 10.5281/zenodo.4284781
title: Ethical Perspectives in AI: A Two-folded Exploratory Study From Literature and Active Development Projects - Supplementary Material

description: This is the Supplementary Material provided for the work accepted on HICSS 2021. Title of work: Ethical Perspectives in AI: A Two-folded Exploratory Study From Literature and Active Development Projects. This dataset depicts 589 GitHub README files explored in our article. We devised each repository into 4 categories. AI Applications (78), reference lists (486), Explainable AI tool (6), Ethical AI tool (15). Moreoveer, 4 repositories could not be found, and 181 of them had a Programming Language. In addition, we classified them according to our judgement of Interesting (82) or Great (33), regarding the objectives of our work. 75 repositories were not related to AI Ethics, and 7 of them mentioned COVID-19 in their repositories in some manner. Highlighted repositories in red pertain to categories 3 and 4, that is, tools for implementing AI Ethics, while those in yellow are marked as “Great”. Explainable AI tool and Ethical AI tool were added to 21 occurrences of tools for implementing AI ethics publicly available in repositories. Furthermore, it is seen that many papers open-sourced their codes on GitHub (as in https://github.com/lopusz/awesome-interpretable-machine-learning), meaning that the academic field has also made good progress in implementing ethics in AI, hence, the combined energy of both sources fosters an enhanced debate and stimulates progress towards AI ethics in practice.
DOI: 10.5519/qd.xb007itg
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (28 records)
DOI: 10.5281/zenodo.3876154
title: CauseNet: Towards a Causality Graph Extracted from the Web

description: Causal knowledge is seen as one of the key ingredients to advance artificial intelligence. Yet, few knowledge bases comprise causal knowledge to date, possibly due to significant efforts required for validation. Notwithstanding this challenge, we compile CauseNet, a large-scale knowledge base of <em>claimed </em>causal relations between causal concepts. By extraction from different semi- and unstructured web sources, we collect more than 11 million causal relations with an estimated extraction precision of 83% and construct the first large-scale and open-domain causality graph. We analyze the graph to gain insights about causal beliefs expressed on the web and we demonstrate its benefits in basic causal question answering. Future work may use the graph for causal reasoning, computational argumentation, multi-hop question answering, and more. When using the data, please make sure to refer to it as follows: <pre><code>@inproceedings{heindorf2020causenet, author = {Stefan Heindorf and Yan Scholten and Henning Wachsmuth and Axel-Cyrille Ngonga Ngomo and Martin Potthast}, title = {CauseNet: Towards a Causality Graph Extracted from the Web}, booktitle = {{CIKM}}, pages = {3023--3030}, publisher = {{ACM}}, year = {2020} }</code></pre>
description: {"references": ["Stefan Heindorf, Yan Scholten, Henning Wachsmuth, Axel-Cyrille Ngonga Ngomo, and Martin Potthast. CauseNet: Towards a Causality Graph Extracted from the Web. In CIKM 2020, pages 2023-3030. ACM."]}
DOI: 10.5281/zenodo.3876153
title: CauseNet: Towards a Causality Graph Extracted from the Web

description: Causal knowledge is seen as one of the key ingredients to advance artificial intelligence. Yet, few knowledge bases comprise causal knowledge to date, possibly due to significant efforts required for validation. Notwithstanding this challenge, we compile CauseNet, a large-scale knowledge base of <em>claimed </em>causal relations between causal concepts. By extraction from different semi- and unstructured web sources, we collect more than 11 million causal relations with an estimated extraction precision of 83% and construct the first large-scale and open-domain causality graph. We analyze the graph to gain insights about causal beliefs expressed on the web and we demonstrate its benefits in basic causal question answering. Future work may use the graph for causal reasoning, computational argumentation, multi-hop question answering, and more. When using the data, please make sure to refer to it as follows: <pre><code>@inproceedings{heindorf2020causenet, author = {Stefan Heindorf and Yan Scholten and Henning Wachsmuth and Axel-Cyrille Ngonga Ngomo and Martin Potthast}, title = {CauseNet: Towards a Causality Graph Extracted from the Web}, booktitle = {{CIKM}}, pages = {3023--3030}, publisher = {{ACM}}, year = {2020} }</code></pre>
description: {"references": ["Stefan Heindorf, Yan Scholten, Henning Wachsmuth, Axel-Cyrille Ngonga Ngomo, and Martin Potthast. CauseNet: Towards a Causality Graph Extracted from the Web. In CIKM 2020, pages 2023-3030. ACM."]}
DOI: 10.5281/zenodo.4284818
title: Data for: Susceptibility of domain experts to color manipula-tion indicate a need for design principles in data visualization (PLoS one)

description: This data set accompanies the paper "<strong>Susceptibility of domain experts to color manipulation indicate a need for design principles in data visualization</strong>" by Markus Christen, Peter Brugger and Sara Irina Fabrikant, revision submitted to PLoS One. The paper will be open access, further information will be available there.
DOI: 10.5281/zenodo.4284817
title: Data for: Susceptibility of domain experts to color manipula-tion indicate a need for design principles in data visualization (PLoS one)

description: This data set accompanies the paper "<strong>Susceptibility of domain experts to color manipulation indicate a need for design principles in data visualization</strong>" by Markus Christen, Peter Brugger and Sara Irina Fabrikant, revision submitted to PLoS One. The paper will be open access, further information will be available there.
DOI: 10.6084/m9.figshare.13271546
title: Research dataset: ClinicalTrials.gov 20/11/2020 Search query: conditions: covid / other terms: vaccine | Filters: phase III clinical trials

description: Dataset of an ongoing systematic review evaluation of SARS-CoV-2
DOI: 10.6084/m9.figshare.13271546.v1
title: Research dataset: ClinicalTrials.gov 20/11/2020 Search query: conditions: covid / other terms: vaccine | Filters: phase III clinical trials

description: Dataset of an ongoing systematic review evaluation of SARS-CoV-2
DOI: 10.5281/zenodo.3598595
title: Machine learning estimates of eddy covariance carbon flux in a scrub in the Mexican highland

description: Arid and semi-arid ecosystems contain relatively high species diversity and are subject to intense use, in particular extensive cattle grazing, which has favoured the expansion and encroachment of perennial thorny shrubs into the grasslands, thus decreasing the value of the rangeland. However, these environments have been shown to positively impact global carbon dynamics. Machine learning and remote sensing had enhanced our knowledge about carbon dynamics, but they need to be further developed and adapted to particular analysis. We measured the net ecosystem exchange of C (NEE) with the Eddy Covariance (EC) method and estimated GPP in a thorny scrub at Bernal in Mexico. We tested the agreement between EC estimates and remotely sensed GPP estimates from MODIS, and also with two alternative modelling methods: ordinary least squares multiple regression (OLS) or ensembles of machine learning algorithms (EML). The variables used as predictors were Moderate Resolution Spectroradiometer (MODIS) spectral bands, vegetation indices and products, as well as gridded environmental variables. The Bernal site was a carbon sink despite it was overgrazed, the average NEE during fifteen months of 2017 and 2018 was -0.78 g C m<sup>-2</sup> d<sup>-1</sup> and the flux was negative or neutral during the measured months. The probability of agreement (θs) represented the agreement between observed and estimated values of GPP across the range of measurement. According to the mean value of θs, agreement was higher for the EML (0.6) followed by OLS (0.5) and then MODIS (0.24). This graphic metric was more informative than r<sup>2</sup> (0.98, 0.67, 0.58 respectively) to evaluate the model performance. This was particularly true for MODIS because the maximum θs of 4.3 was for measurements of 0.8 g C m<sup>-2</sup> d<sup>-1</sup> and then decreased steadily below 1 θs for measurements above 6.5 g C m<sup>-2</sup> d<sup>-1 </sup>for this scrub vegetation. In the case of EML and OLS the θs was stable across the range of measurement. We used an EML for the Ameriflux site US-SRM, which is similar in vegetation and climate, to predict GPP at Bernal, but θs was low (0.16) indicating the local specificity of this model. Although cacti were an important component of the vegetation, the night time flux was characterized by positive NEE, suggesting that the photosynthetic dark-cycle flux of cacti was lower than ecosystem respiration. The discrepancy between MODIS and EC GPP estimates stresses the need to understand the limitations of both methods.