cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.13026/s438-qy42
title: Learning to Ask Like a Physician: a Discharge Summary Clinical Questions (DiSCQ) Dataset

description: Existing question answering (QA) datasets derived from electronic health
records (EHR) are artificially generated and consequently fail to capture
realistic physician information needs. We present Discharge Summary Clinical
Questions (DiSCQ), a newly curated question dataset composed of 2,000+
questions paired with the snippets of text (triggers) that prompted each
question. The questions are generated by medical experts from 100+ MIMIC-III,
version 1.4, discharge summaries. These discharge summaries overlap with the
n2c2 challenge, so they are filled in with surrogate PHI. We analyze this
dataset to characterize the types of information sought by medical experts. We
also train baseline models for trigger detection and question generation (QG),
paired with unsupervised answer retrieval over EHRs. Our baseline model is
able to generate high quality questions in over 62% of cases when prompted
with human selected triggers. We release this dataset (and a link to all code
to reproduce baseline model results) to facilitate further research into
realistic clinical QA and QG.
DOI: 10.13026/7v8e-h745
title: Learning to Ask Like a Physician: a Discharge Summary Clinical Questions (DiSCQ) Dataset

description: Existing question answering (QA) datasets derived from electronic health
records (EHR) are artificially generated and consequently fail to capture
realistic physician information needs. We present Discharge Summary Clinical
Questions (DiSCQ), a newly curated question dataset composed of 2,000+
questions paired with the snippets of text (triggers) that prompted each
question. The questions are generated by medical experts from 100+ MIMIC-III,
version 1.4, discharge summaries. These discharge summaries overlap with the
n2c2 challenge, so they are filled in with surrogate PHI. We analyze this
dataset to characterize the types of information sought by medical experts. We
also train baseline models for trigger detection and question generation (QG),
paired with unsupervised answer retrieval over EHRs. Our baseline model is
able to generate high quality questions in over 62% of cases when prompted
with human selected triggers. We release this dataset (and a link to all code
to reproduce baseline model results) to facilitate further research into
realistic clinical QA and QG.
DOI: 10.5281/zenodo.5237565
title: policy and regulation interventions in EU sovereign credit risk markets _ replication material

description: Replication data for the article " Assessing the Impact of Policy and Regulation Interventions in<br> European Sovereign Credit Risk Networks: What worked best?" by Rebekka Buse, Melanie Schienle and Jörg Urban<br>
DOI: 10.5281/zenodo.5237564
title: policy and regulation interventions in EU sovereign credit risk markets _ replication material

description: Replication data for the article " Assessing the Impact of Policy and Regulation Interventions in<br> European Sovereign Credit Risk Networks: What worked best?" by Rebekka Buse, Melanie Schienle and Jörg Urban<br>
DOI: 10.18434/mds2-2411
title: Mission Critical Voice Start-of-Word Correction for Access Delay Measurement System Measurement Data
DOI: 10.18434/mds2-2456
title: Mission critical voice quality of experience measurement software
DOI: 10.18434/mds2-2432
title: Optimal Transmit Volume Conditions for MCV QoE Measurement Systems Data
DOI: 10.18434/mds2-2441
title: Optimal Transmit Volume Conditions for MCV QoE Measurement Systems Software
DOI: 10.5281/zenodo.6930000
title: Surface velocities due to the Southern San Andreas Fault from Sentinel-1 InSAR data

description: Line of sight (LOS), fault-parallel, and vertical velocities in the area around the Southern San Andreas Fault in California, USA. Gzipped tar archive. All data are in Generic Mapping Tools (GMT) Netcdf format.
DOI: 10.5281/zenodo.6929999
title: Surface velocities due to the Southern San Andreas Fault from Sentinel-1 InSAR data

description: Line of sight (LOS), fault-parallel, and vertical velocities in the area around the Southern San Andreas Fault in California, USA. Gzipped tar archive. All data are in Generic Mapping Tools (GMT) Netcdf format.
DOI: 10.5281/zenodo.6929863
title: Far-Ultraviolet Photometric Characteristics of JSC-1A and LMS-1 Lunar Regolith Simulants: Comparative Investigations with Apollo 10084

description: The .txt files listed here contain the data used for Figure 3 and Supplemental Figure S3 of the paper titled "Far-Ultraviolet Photometric Characteristics of JSC-1A and LMS-1 Lunar Regolith Simulants: Comparative Investigations with Apollo 10084." The experimentally derived phase curves are contained in the files which have the material name (JSC-1A, LMS-1, or Apollo 10084) followed by the wavelength alone (Lyman-a, 140 nm, 160 nm) in the file name. All files with "Hapke" in the file name are the Hapke photometric model (Hapke, 2012) fitted phase curves for the associated experimental data. Two of the JSC-1A files have either "LT38" or "GT150" included in the file name; these are the sieved grain size category data referenced in S3. Also included are .txt files of the data points used in Figure 4 for the JSC-1A and LMS-1 simulants. These each have "Fig4" in the file name.
DOI: 10.5281/zenodo.6930083
title: A mid-infrared lab-on-a-chip for dynamic reaction monitoring

description: This file contains the as measured source data of the above-mentioned manuscript.
DOI: 10.5281/zenodo.6930082
title: A mid-infrared lab-on-a-chip for dynamic reaction monitoring

description: This file contains the as measured source data of the above-mentioned manuscript.
DOI: 10.5281/zenodo.5576446
title: Global Economic Limits of Groundwater used for Irrigation

description: Input and output files of the analysis of the global economic limits of groundwater used for Irrigation
DOI: 10.5281/zenodo.5576445
title: Global Economic Limits of Groundwater used for Irrigation

description: Input and output files of the analysis of the global economic limits of groundwater used for Irrigation
DOI: 10.5281/zenodo.6895054
title: Training and testing labelled dataset of human faces for different indoor visual comfort and glare visual discomfort situations - videos

description: The video dataset is generated from experimental tests on 23 different participants and includes a certain variety in characteristics: age, gender, face feature, glasses, etc. The original dataset was generated from experimental tests on 24 different participants but all the data of one of the participants was entirely excluded as this participant did not want his/her data to be publically available. After synchronization of the video footages with the answers of the participants regarding their visual discomfort at the different phases of the experiment to label the video dataset, the video footages are resampled at a 0.5 FPS rate, and the YOLOFace extraction algorithm is used to crop the image down to the face of the occupants only and generate the labelled image dataset.
DOI: 10.5281/zenodo.6895053
title: Training and testing labelled dataset of human faces for different indoor visual comfort and glare visual discomfort situations - videos

description: The video dataset is generated from experimental tests on 23 different participants and includes a certain variety in characteristics: age, gender, face feature, glasses, etc. The original dataset was generated from experimental tests on 24 different participants but all the data of one of the participants was entirely excluded as this participant did not want his/her data to be publically available. After synchronization of the video footages with the answers of the participants regarding their visual discomfort at the different phases of the experiment to label the video dataset, the video footages are resampled at a 0.5 FPS rate, and the YOLOFace extraction algorithm is used to crop the image down to the face of the occupants only and generate the labelled image dataset.
DOI: 10.5281/zenodo.6360747
title: Estimating drivers and pathways for hydroelectric reservoir methane emissions using a new mechanistic model (estimated methane emissions for hydropower reservoir surfaces and potential dam emissions)

description: Methane emissions data from hydropower reservoir surfaces and dams, as estimated with the ResME model. Emissions estimates available for hydropower reservoirs in the GRanD database (Lehner et al., 2011). References: Lehner, B., Liermann, C. Reidy, Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J.C., Rodel, R., Sindorf, N., and Wisser, D. (2011). High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management. Frontiers in Ecology and the Environment, 9 (9): 494-502. https://doi.org/10.1890/100125.
DOI: 10.5281/zenodo.4730936
title: Estimating drivers and pathways for hydroelectric reservoir methane emissions using a new mechanistic model (estimated methane emissions for hydropower reservoir surfaces and potential dam emissions)

description: Methane emissions data from hydropower reservoir surfaces and dams, as estimated with the ResME model. Emissions estimates available for hydropower reservoirs in the GRanD database (Lehner et al., 2011). References: Lehner, B., Liermann, C. Reidy, Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J.C., Rodel, R., Sindorf, N., and Wisser, D. (2011). High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management. Frontiers in Ecology and the Environment, 9 (9): 494-502. https://doi.org/10.1890/100125.
DOI: 10.5281/zenodo.6895080
title: Training and testing labelled dataset of human faces for different indoor visual comfort and glare visual discomfort situations - images

description: The image dataset is generated from experimental tests on 23 different participants and includes a certain variety in characteristics: age, gender, face feature, glasses, etc. The original dataset was generated from experimental tests on 24 different participants but all the data of one of the participants was entirely excluded as this participant did not want his/her data to be publically available. After synchronization of the video footages with the answers of the participants regarding their visual discomfort at the different phases of the experiment to label the video dataset, the video footages are resampled at a 0.5 FPS rate, and the YOLOFace extraction algorithm is used to crop the image down to the face of the occupants only and generate the labelled image dataset.
DOI: 10.5281/zenodo.6895079
title: Training and testing labelled dataset of human faces for different indoor visual comfort and glare visual discomfort situations - images

description: The image dataset is generated from experimental tests on 23 different participants and includes a certain variety in characteristics: age, gender, face feature, glasses, etc. The original dataset was generated from experimental tests on 24 different participants but all the data of one of the participants was entirely excluded as this participant did not want his/her data to be publically available. After synchronization of the video footages with the answers of the participants regarding their visual discomfort at the different phases of the experiment to label the video dataset, the video footages are resampled at a 0.5 FPS rate, and the YOLOFace extraction algorithm is used to crop the image down to the face of the occupants only and generate the labelled image dataset.
DOI: 10.6084/m9.figshare.20399265.v1
title: S03_V6 missing file

description: data
DOI: 10.6084/m9.figshare.20399265
title: S03_V6 missing file

description: data
DOI: 10.6084/m9.figshare.19578970
title: Figure_6e

description: Raw data for Figure 6e
DOI: 10.6084/m9.figshare.19578970.v1
title: Figure_6e

description: Raw data for Figure 6e
DOI: 10.6084/m9.figshare.19578883.v1
title: Figure_5b

description: Raw data for Figure 5b
DOI: 10.6084/m9.figshare.19578883
title: Figure_5b

description: Raw data for Figure 5b
DOI: 10.6084/m9.figshare.19578853.v1
title: Figure_5a

description: Raw data for Figure 5a
DOI: 10.6084/m9.figshare.19578745
title: Figure_4d_PMN

description: Raw data for Figure 4d, PMNs
DOI: 10.6084/m9.figshare.19578745.v1
title: Figure_4d_PMN

description: Raw data for Figure 4d, PMNs
DOI: 10.6084/m9.figshare.19578724
title: Figure_4c_24h

description: Raw data for figure 4c, 24 h
DOI: 10.6084/m9.figshare.19578736
title: Figure_4d_LCN2

description: Raw data for Figure 4d, LCN2
DOI: 10.6084/m9.figshare.19578736.v1
title: Figure_4d_LCN2

description: Raw data for Figure 4d, LCN2
DOI: 10.6084/m9.figshare.19578967
title: Figure_6d

description: Raw data for Figure 6d
DOI: 10.6084/m9.figshare.19578967.v1
title: Figure_6d

description: Raw data for Figure 6d
DOI: 10.6084/m9.figshare.19578724.v1
title: Figure_4c_24h

description: Raw data for figure 4c, 24 h
DOI: 10.6084/m9.figshare.19578718.v1
title: Figure_4b_MPO

description: Raw data for Figure 4b, MPO
DOI: 10.6084/m9.figshare.19578733
title: Figure_4c_72h

description: Raw data for Figure 4c, 72 h.
DOI: 10.6084/m9.figshare.19578631.v1
title: Figure_4b_LCN2

description: Raw data for Figure 4b, LCN-2
DOI: 10.6084/m9.figshare.19578718
title: Figure_4b_MPO

description: Raw data for Figure 4b, MPO
DOI: 10.6084/m9.figshare.19578733.v1
title: Figure_4c_72h

description: Raw data for Figure 4c, 72 h.
DOI: 10.6084/m9.figshare.19578628
title: Figure 4a

description: Raw data for figure 4a
DOI: 10.6084/m9.figshare.19578631
title: Figure_4b_LCN2

description: Raw data for Figure 4b, LCN-2
DOI: 10.6084/m9.figshare.19578628.v1
title: Figure 4a

description: Raw data for figure 4a
DOI: 10.6084/m9.figshare.19372073
title: Figure_3e

description: Raw data for Figure 3e
DOI: 10.6084/m9.figshare.19372073.v1
title: Figure_3e

description: Raw data for Figure 3e
DOI: 10.6084/m9.figshare.19134251
title: Figure_3c

description: raw kinetic data for Figure 3c
DOI: 10.6084/m9.figshare.19134242
title: Figure_3a

description: Figure 3a. Raw data from positional frequencies of epitope mapped peptides.
DOI: 10.6084/m9.figshare.19134251.v1
title: Figure_3c

description: raw kinetic data for Figure 3c
DOI: 10.6084/m9.figshare.19105124
title: Figure_2b

description: Source data for Figure 2b
DOI: 10.6084/m9.figshare.19134242.v1
title: Figure_3a

description: Figure 3a. Raw data from positional frequencies of epitope mapped peptides.
DOI: 10.6084/m9.figshare.19105124.v1
title: Figure_2b

description: Source data for Figure 2b
DOI: 10.17909/ksn7-y471
title: Data from "Hubble Space Telescope Imaging Reveals that SN 2015bh is Much Fainter than its Progenitor"

description: HST data used in "Hubble Space Telescope Imaging Reveals that SN 2015bh is Much Fainter than its Progenitor" by J. Jencson et al.
DOI: 10.6084/m9.figshare.20399271.v1
title: BNLL telemetry data 2016-2018

description: Relocation data for lizards. Estimate shrub presence from ArcGis algorithm.
DOI: 10.6084/m9.figshare.20399271
title: BNLL telemetry data 2016-2018

description: Relocation data for lizards. Estimate shrub presence from ArcGis algorithm.
DOI: 10.5281/zenodo.6920831
title: Keyahta/grabay: Indoor localization dataset

description: HUSTData is an indoor localization dataset. It is collected from a typical lecture building D, with a total area of 482m2 in Huazhong University of Science and Technology. Source codes of our previous work using this dataset with detailed explanations are in progress. Click the GitHub button on the right or use https://github.com/Keyahta/grabay to go to the dataset.
DOI: 10.5281/zenodo.6920830
title: Keyahta/grabay: Indoor localization dataset

description: HUSTData is an indoor localization dataset. It is collected from a typical lecture building D, with a total area of 482m2 in Huazhong University of Science and Technology. Source codes of our previous work using this dataset with detailed explanations are in progress. Click the GitHub button on the right or use https://github.com/Keyahta/grabay to go to the dataset.
DOI: 10.6084/m9.figshare.19291439.v3
title: Whole-genome assembly and annotation of little yellow croaker (Larimichthys polyactis) provide insights into the evolution of hermaphroditism and gonochorism

description: The whole genome assembly and annotation of Larimichthys polyactis and raw data of BioNano sequence results.
DOI: 10.6084/m9.figshare.14977657
title: Does exposure to radiofrequency radiation (RFR) affect the circadian rhythm of rest-activity patterns and behavioral sleep variables in humans?

description: We evaluated the effects of the exposure to radio-frequency radiation emanating from the base transceiver station (BTS) on the characteristics of circadian rest-activity rhythm and behavioral sleep variables in humans. We performed this exploratory field study in a sample of 89 healthy subjects randomly chosen out of 1434 individuals surveyed for the purpose. We divided 89 subjects into five groups, including the control, as a function of distance from the BTS. The E-field strength was higher in the groups of the inter-tower region and between 0 and 150 m away from the BTS. The E-field (distance) did not significantly affect the circadian rhythm parameters and behavioral sleep variables, except a marginal delay in the peak timings of the rest-activity rhythm of subjects in the inter-tower and 300–500 m groups. Notable secondary effects of the factor gender were noticed on circadian amplitude, sleep efficiency, dichotomy index, and wake after sleep onset. We concluded that exposure to radiation from the BTS did not modulate actigraphy-based behavioral sleep variables of people residing around BTS installations. We recommend more extensive field-based studies with rigorous longitudinal designs to validate the effects of radiation from the BTS in humans.
DOI: 10.6084/m9.figshare.14977657.v1
title: Does exposure to radiofrequency radiation (RFR) affect the circadian rhythm of rest-activity patterns and behavioral sleep variables in humans?

description: We evaluated the effects of the exposure to radio-frequency radiation emanating from the base transceiver station (BTS) on the characteristics of circadian rest-activity rhythm and behavioral sleep variables in humans. We performed this exploratory field study in a sample of 89 healthy subjects randomly chosen out of 1434 individuals surveyed for the purpose. We divided 89 subjects into five groups, including the control, as a function of distance from the BTS. The E-field strength was higher in the groups of the inter-tower region and between 0 and 150 m away from the BTS. The E-field (distance) did not significantly affect the circadian rhythm parameters and behavioral sleep variables, except a marginal delay in the peak timings of the rest-activity rhythm of subjects in the inter-tower and 300–500 m groups. Notable secondary effects of the factor gender were noticed on circadian amplitude, sleep efficiency, dichotomy index, and wake after sleep onset. We concluded that exposure to radiation from the BTS did not modulate actigraphy-based behavioral sleep variables of people residing around BTS installations. We recommend more extensive field-based studies with rigorous longitudinal designs to validate the effects of radiation from the BTS in humans.
DOI: 10.15468/bfndyy
title: Global Register of Introduced and Invasive Species - Atoll Nui, Tuvalu

description: The Global Register of Introduced and Invasive Species (GRIIS) presents validated and verified national checklists of introduced (alien) and invasive alien species at the country, territory, and associated island level. Checklists are living entities, especially for biological invasions given the growing nature of the problem. GRIIS checklists are based on a published methodology and supported by the Integrated Publishing Tool that jointly enable ongoing improvements and updates to expand their taxonomic coverage and completeness. All kingdoms of organisms occurring in all environments and systems are covered. Checklists are reviewed and verified by networks of country or species experts. Verified checklists/ species records, as well as those under review, are presented on the online GRIIS website (www.griis.org) in addition to being published through the GBIF Integrated Publishing Tool.
DOI: 10.6084/m9.figshare.19578853
title: Figure_5a

description: Raw data for Figure 5a
DOI: 10.5281/zenodo.6613741
title: Merging Predictions from Cell Morphology and Chemical Fingerprint Models by Leveraging Similarity to Training Data

description: The applicability domain of machine learning models trained on structural fingerprints for the prediction of biological endpoints, is often limited by the diversity of chemical space of the training data. In this work, we developed “similarity-based merger models” which combined the output of individual models trained on cell morphology (based on Cell Painting) and chemical structural (based on chemical fingerprints). Using a combination of a decision tree and logistic regression models on the structural versus morphological feature space of the training data, which leveraged the similarity of test compounds to training compounds, the similarity-based merger models used logistic equations to weight individual model outputs We applied these models to predict assay hit calls of 92 assays from ChEMBL and PubChem and 89 anonymised assays released by the Broad Institute, where the required Cell Painting annotations were available. We found that, for the 181 assays used in this study the similarity-based merger model improved AUC in relative terms by 16.3% compared to the models using chemical structure alone (mean AUC of 0.75 vs. 0.64), and by 21.3% compared to the models using Cell Painting data alone (mean AUC of 0.62). Our results demonstrate that similarity-based merger models combining structure and cell morphology models can more accurately predict a wide range of biological assay outcomes and expand the applicability domain by better extrapolating to new structural and morphology spaces.
description: Release dataset
description: {"references": ["Bray, M. A.; Gustafsdottir, S. M.; Rohban, M. H.; Singh, S.; Ljosa, V.; Sokolnicki, K. L.; Bittker, J. A.; Bodycombe, N. E.; Dan\u010d\u00edk, V.; Hasaka, T. P.; Hon, C. S.; Kemp, M. M.; Li, K.; Walpita, D.; Wawer, M. J.; Golub, T. R.; Schreiber, S. L.; Clemons, P. A.; Shamji, A. F.; Carpenter, A. E. A Dataset of Images and Morphological Profiles of 30 000 Small-Molecule Treatments Using the Cell Painting Assay. Giga-Science. 2017, pp 1\u20135.", "Moshkov, N.; Moshkov, T.; Yang, K.; Horvath, P.; Dancik, V.; Wagner, B. K.; Clemons, P. A.; Singh, S.; Carpenter, A. E.; Caicedo, J. C. Predicting Compound Activity from Phenotypic Profiles and Chem-ical Structures. bioRxiv 2022, 2020.12.15.422887"]}
DOI: 10.15468/8wnw2w
title: Global Register of Introduced and Invasive Species - Atoll Nukulaelae, Tuvalu

description: The Global Register of Introduced and Invasive Species (GRIIS) presents validated and verified national checklists of introduced (alien) and invasive alien species at the country, territory, and associated island level. Checklists are living entities, especially for biological invasions given the growing nature of the problem. GRIIS checklists are based on a published methodology and supported by the Integrated Publishing Tool that jointly enable ongoing improvements and updates to expand their taxonomic coverage and completeness. All kingdoms of organisms occurring in all environments and systems are covered. Checklists are reviewed and verified by networks of country or species experts. Verified checklists/ species records, as well as those under review, are presented on the online GRIIS website (www.griis.org) in addition to being published through the GBIF Integrated Publishing Tool.
DOI: 10.6084/m9.figshare.20326611
title: COVID_19 Data.dta

description: Data collected on side effects experienced following COVID-19 vaccination, inlcliding socio-demographic variables.