cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4460076
title: Supplemental artifacts of the paper: Efficient Binary-Level Coverage Analysis

description: NOTE: the official repository of bcov is: https://github.com/abenkhadra/bcov This repository contains the artifacts accompanying our paper: "Efficient Binary-Level Coverage Analysis", which appeared in ESEC/FSE'20. The artifacts consists of two packages, namely, bcov-benchmarks.tar.gz and bcov-artifacts.tar.gz. The former package contains the complete list of binaries described in our experiments. The artifacts of the latter package are organized as follows: - <strong>sample-binaries</strong>. Folder that contains sample binaries patched with bcov. - <strong>dataset.tar.gz</strong>. Package containing experimental data in csv format. - <strong>figures</strong>. Folder that contains the python script used to generate the figures<br> of our paper. It assumes that the dataset was first extracted to the folder `dataset`. - <strong>install.sh</strong>. This script builds and installs bcov together with its dependencies. - <strong>experiment-01.sh</strong>. This script patches our sample binaries and shows how coverage<br> data can be collected. It assumes that bcov was installed using the previous script. - <strong>bcov.tar.gz</strong>. Source code of the first public version of `bcov`. The tool is distributed under an MIT license.<br>
DOI: 10.5281/zenodo.3876047
title: Supplemental artifacts of the paper: Efficient Binary-Level Coverage Analysis

description: NOTE: the official repository of bcov is: https://github.com/abenkhadra/bcov This repository contains the artifacts accompanying our paper: "Efficient Binary-Level Coverage Analysis", which appeared in ESEC/FSE'20. The artifacts consists of two packages, namely, bcov-benchmarks.tar.gz and bcov-artifacts.tar.gz. The former package contains the complete list of binaries described in our experiments. The artifacts of the latter package are organized as follows: - <strong>sample-binaries</strong>. Folder that contains sample binaries patched with bcov. - <strong>dataset.tar.gz</strong>. Package containing experimental data in csv format. - <strong>figures</strong>. Folder that contains the python script used to generate the figures<br> of our paper. It assumes that the dataset was first extracted to the folder `dataset`. - <strong>install.sh</strong>. This script builds and installs bcov together with its dependencies. - <strong>experiment-01.sh</strong>. This script patches our sample binaries and shows how coverage<br> data can be collected. It assumes that bcov was installed using the previous script. - <strong>bcov.tar.gz</strong>. Source code of the first public version of `bcov`. The tool is distributed under an MIT license.<br>
DOI: 10.6084/m9.figshare.12149241
title: Ooh Na Na

description: This dataset is now maintained on Zenodo. See: https://zenodo.org/record/4020486<br>
DOI: 10.6084/m9.figshare.12149241.v1
title: Ooh Na Na

description: This dataset is now maintained on Zenodo. See: https://zenodo.org/record/4020486<br>
DOI: 10.5281/zenodo.4460128
title: Mock Image Cube

description: A mock image cube made from the ALMA logo, for testing purposes with radio interferometers. Total flux is comparable to a bright protoplanetary disk in 12CO J=2-1.
DOI: 10.5281/zenodo.4460127
title: Mock Image Cube

description: A mock image cube made from the ALMA logo, for testing purposes with radio interferometers. Total flux is comparable to a bright protoplanetary disk in 12CO J=2-1.
DOI: 10.6084/m9.figshare.12000231
title: Antimicrobial resistance surveillance report, Patan Hospital, Nepal, 01 Jan 2018 to 30 Dec 2018

description: Updated on 18<sup>th</sup> January 2021<br> This update fixes inaccurate estimations of the proportion of non-susceptible isolates of <i>E. coli </i>and <i>K. pneumoniae</i> for ampicillin (page 9-10 in the AMR surveillance report) in AMASS 1.0. The updated AMR surveillance report and output files were generated by AMASS 1.1 using the same data sets and data dictionary files. AMASS 1.1 (figshare link https://figshare.com/articles/dataset/AMASS_zip/7763330) fixes a typo in the R code (z_Rcode.R file) of the AMASS 1.0 that caused the inaccurate estimations.<br>AutoMated tool for Antimicrobial resistance Surveillance System (AMASS) was developed as an offline, open-access and easy-to-use application that allows a hospital to perform data analysis independently and generate isolate-based and sample-based surveillance reports stratified by infection origin from routinely collected electronic databases. AMASS performs data analysis and generates reports automatically. The application can be downloaded from https://www.amass.website<br><br>This is a repository of the files for Patan Hospital. The attached three files are:<br>1) The AMR surveillance report automatically generated from AMASS using hospital admission and microbiology data of Patan Hospital in Nepal<br>2) Aggregated summary data in .csv format automatically generated from AMASS<br>3) Data dictionary for microbiology data file of Patan Hospital (configured by the hospital to be used with the AMASS application)<br><br>Please note that the two data dictionary files can be re-used with the data files of the hospital in the future in cases when variables names and values in the raw data remained unchanged.<br>
DOI: 10.6084/m9.figshare.12000231.v1
title: Antimicrobial resistance surveillance report, Patan Hospital, Nepal, 01 Jan 2018 to 30 Dec 2018

description: AutoMated tool for Antimicrobial resistance Surveillance System (AMASS) was developed as an offline, open-access and easy-to-use application that allows a hospital to perform data analysis independently and generate isolate-based and sample-based surveillance reports stratified by infection origin from routinely collected electronic databases. AMASS performs data analysis and generates reports automatically. The application can be downloaded from https://www.amass.website<br><br>This is a repository of the files for Patan Hospital. The attached three files are:<br>1) The AMR surveillance report automatically generated from AMASS using hospital admission and microbiology data of Patan Hospital in Nepal<br>2) Aggregated summary data in .csv format automatically generated from AMASS<br>3) Data dictionary for microbiology data file of Patan Hospital (configured by the hospital to be used with the AMASS application)<br><br>Please note that the two data dictionary files can be re-used with the data files of the hospital in the future in cases when variables names and values in the raw data remained unchanged.<br>
DOI: 10.5281/zenodo.4459519
title: SURF Machine Metric Dataset [2019-12-29, 2020-08-07]

description: An addition to our earlier uploaded dataset. This dataset contains an update of Prometheus: it now contains 327 metrics and spans more than 7 months. Note that the rack and node names have changed with respect to the previous dataset and invalid values are now set to NaN to better indicate what is an invalid value and what is a true zero value.
DOI: 10.5281/zenodo.4459518
title: SURF Machine Metric Dataset [2019-12-29, 2020-08-07]

description: An addition to our earlier uploaded dataset. This dataset contains an update of Prometheus: it now contains 327 metrics and spans more than 7 months. Note that the rack and node names have changed with respect to the previous dataset and invalid values are now set to NaN to better indicate what is an invalid value and what is a true zero value.
DOI: 10.25345/c54v24
title: MassIVE MSV000086757 - GNPS_Siderophores_Fe_Serratia_Pantoea
title: Fe_Siderophores in cycad_feeding insect microbiome
DOI: 10.5519/qd.29ocobcm
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.6084/m9.figshare.13634945
title: HFMDdata.csv

description: There are 16 columns in the current data set, including geographic codes, projection coordinates, latitude and longitude coordinates, time, the climatic data, consisting of the monthly average temperature (AT), relative humidity (RH), air pressure (AP), wind speed (AW) and monthly total precipitation (TP), hand, foot, and mouth disease cases, population sizes of children under 9 years old, the adjusted cumulative incidence (CI) values and their natural logarithm.
DOI: 10.6084/m9.figshare.13607204.v1
title: FISH_OIL+CPT-11_STUDY_FASTQ_QIIME2_PICRUST2_FILES

description: Fish oil+cpt-11 and corn oil+cpt-11 study raw fastq files, qiime2 bioinformatics analysis results and the data used to perform picrust2.
DOI: 10.6084/m9.figshare.13607204
title: FISH_OIL+CPT-11_STUDY_FASTQ_QIIME2_PICRUST2_FILES

description: Fish oil+cpt-11 and corn oil+cpt-11 study raw fastq files, qiime2 bioinformatics analysis results and the data used to perform picrust2.
DOI: 10.6084/m9.figshare.13607204.v3
title: FISH_OIL+CPT-11_STUDY_FASTQ_QIIME2_PICRUST2_FILES

description: Fish oil+cpt-11 and corn oil+cpt-11 study raw fastq files, qiime2 bioinformatics analysis results and the data used to perform picrust2.
DOI: 10.6084/m9.figshare.13607204.v2
title: FISH_OIL+CPT-11_STUDY_FASTQ_QIIME2_PICRUST2_FILES

description: Fish oil+cpt-11 and corn oil+cpt-11 study raw fastq files, qiime2 bioinformatics analysis results and the data used to perform picrust2.
DOI: 10.21227/h0ja-c631
title: Module-based explainable predictions of energy consumption (MEPEC)

description: Energy consumption varies significantly across different areas and is dependent on various factors like weather, type of the consumer, appliances used, etc. If predictable, the data could be used to improvise energy production and develop cost reduction strategies. However, the complicated nature of the dependent parameters makes it difficult to predict the consumption. This submission attempts to address this problem based on historical consumption readings available for a set of consumers along with other related data. The consumers, represented by the meter_id in the dataset, are clustered based on the dwelling type and the number of bedrooms of the representative household. We use linear regression as the primary method to approximate the relationship between the average monthly temperatures observed and the corresponding energy consumption readings for each of the clusters separately and predict the future consumption patterns of the consumers accordingly. The predictions are then explained in natural language based on the features of the consumer (cluster dependent) to make the predictions more comprehensible. These explanations are devised using a template with the predicted data.
DOI: 10.5441/001/1.23n2m412/1
title: Black kites of different age and sex show similar avoidance responses to wind turbines during migration

description: Santos CD, Ferraz R, Muñoz A-R, Onrubia A, Wikelski M. 2021. Black kites of different age and sex show similar avoidance responses to wind turbines during migration. Roy Soc Open Sci. 8(1). doi:10.1098/rsos.201933
description: Populations of soaring birds are often impacted by wind-power generation. Sex and age bias in turbine collisions can exacerbate these impacts through demographic changes that can lead to population decline or collapse. While several studies have reported sex and age differences in the number of soaring birds killed by turbines, it remains unclear if they result from different abundances or group-specific turbine avoidance behaviours, the latter having severer consequences. We investigated sex and age effects on turbine avoidance behaviour of black kites (Milvus migrans) during migration near the Strait of Gibraltar. We tracked the movements of 135 individuals with GPS data loggers in an area with high density of turbines and then modelled the effect of proximity of turbines on bird utilization distribution (UD). Both sexes and age classes showed similar patterns of displacement, with reduced UD values in the proximity of turbines and a clear peak at 700–850 m away, probably marking the distance at which most birds turn direction to avoid approaching the turbines further. The consistency of these patterns indicates that displacement range can be used as an accurate proxy for collision risk and habitat loss, and should be incorporated in environmental impact assessment studies.
DOI: 10.5441/001/1.23n2m412/2
title: Black kites of different age and sex show similar avoidance responses to wind turbines during migration-reference-data

description: Santos CD, Ferraz R, Muñoz A-R, Onrubia A, Wikelski M. 2021. Black kites of different age and sex show similar avoidance responses to wind turbines during migration. Roy Soc Open Sci. 8(1). doi:10.1098/rsos.201933
description: Populations of soaring birds are often impacted by wind-power generation. Sex and age bias in turbine collisions can exacerbate these impacts through demographic changes that can lead to population decline or collapse. While several studies have reported sex and age differences in the number of soaring birds killed by turbines, it remains unclear if they result from different abundances or group-specific turbine avoidance behaviours, the latter having severer consequences. We investigated sex and age effects on turbine avoidance behaviour of black kites (Milvus migrans) during migration near the Strait of Gibraltar. We tracked the movements of 135 individuals with GPS data loggers in an area with high density of turbines and then modelled the effect of proximity of turbines on bird utilization distribution (UD). Both sexes and age classes showed similar patterns of displacement, with reduced UD values in the proximity of turbines and a clear peak at 700–850 m away, probably marking the distance at which most birds turn direction to avoid approaching the turbines further. The consistency of these patterns indicates that displacement range can be used as an accurate proxy for collision risk and habitat loss, and should be incorporated in environmental impact assessment studies.
DOI: 10.5441/001/1.q23p1t84/1
title: Black Kites at the Strait of Gibraltar (data from Marques et al. 2019)

description: Marques AT, Santos CD, Hanssen F, Muñoz A-R, Onrubia A, Wikelski M, Moreira F, Palmeirim JM, Silva JP. 2020. Wind farm turbines cause functional habitat loss for migratory soaring birds. J Anim Ecol. 89(1):93–103. https://doi.org/10.1111/1365-2656.12961
description: (1) Wind energy production has expanded to meet climate change mitigation goals, but negative impacts of wind turbines have been reported on wildlife. Soaring birds are among the most affected groups with alarming fatality rates by collision with wind turbines and an escalating occupation of their migratory corridors. These birds have been described as changing their flight trajectories to avoid wind turbines, but this behaviour may lead to functional habitat loss, as suitable soaring areas in the proximity of wind turbines will likely be underused. (2) We modelled the displacement effect of wind turbines on black kites (Milvus migrans) tracked by GPS. We also evaluated the impact of this effect at the scale of the landscape by estimating how much suitable soaring area was lost to wind turbines. (3) We used state‐of‐art tracking devices to monitor the movements of 130 black kites in an area populated by wind turbines, at the migratory bottleneck of the Strait of Gibraltar. Landscape use by birds was mapped from GPS data using dynamic Brownian bridge movement models and generalized additive mixed modelling was used to estimate the effect of wind turbine proximity on bird use while accounting for orographic and thermal uplift availability. (4) We found that areas up to approximately 674 m away from the turbines were less used than expected given their uplift potential. Within that distance threshold, bird use decreased with the proximity to wind turbines. We estimated that the footprint of wind turbines affected 3‐14% of the areas suitable for soaring in our study area. (5) We present evidence that the impacts of wind energy industry on soaring birds are greater than previously acknowledged. In addition to the commonly reported fatalities, the avoidance of turbines by soaring birds causes habitat losses in their movement corridors. Authorities should recognize this further impact of wind energy production and establish new regulations that protect soaring habitat. We also showed that soaring habitat for birds can be modelled at a fine scale using publicly available data. Such an approach can be used to plan low‐impact placement of turbines in new wind energy developments.
DOI: 10.5441/001/1.q23p1t84/2
title: Black Kites at the Strait of Gibraltar (data from Marques et al. 2019)-reference-data

description: Marques AT, Santos CD, Hanssen F, Muñoz A-R, Onrubia A, Wikelski M, Moreira F, Palmeirim JM, Silva JP. 2020. Wind farm turbines cause functional habitat loss for migratory soaring birds. J Anim Ecol. 89(1):93–103. https://doi.org/10.1111/1365-2656.12961
description: (1) Wind energy production has expanded to meet climate change mitigation goals, but negative impacts of wind turbines have been reported on wildlife. Soaring birds are among the most affected groups with alarming fatality rates by collision with wind turbines and an escalating occupation of their migratory corridors. These birds have been described as changing their flight trajectories to avoid wind turbines, but this behaviour may lead to functional habitat loss, as suitable soaring areas in the proximity of wind turbines will likely be underused. (2) We modelled the displacement effect of wind turbines on black kites (Milvus migrans) tracked by GPS. We also evaluated the impact of this effect at the scale of the landscape by estimating how much suitable soaring area was lost to wind turbines. (3) We used state‐of‐art tracking devices to monitor the movements of 130 black kites in an area populated by wind turbines, at the migratory bottleneck of the Strait of Gibraltar. Landscape use by birds was mapped from GPS data using dynamic Brownian bridge movement models and generalized additive mixed modelling was used to estimate the effect of wind turbine proximity on bird use while accounting for orographic and thermal uplift availability. (4) We found that areas up to approximately 674 m away from the turbines were less used than expected given their uplift potential. Within that distance threshold, bird use decreased with the proximity to wind turbines. We estimated that the footprint of wind turbines affected 3‐14% of the areas suitable for soaring in our study area. (5) We present evidence that the impacts of wind energy industry on soaring birds are greater than previously acknowledged. In addition to the commonly reported fatalities, the avoidance of turbines by soaring birds causes habitat losses in their movement corridors. Authorities should recognize this further impact of wind energy production and establish new regulations that protect soaring habitat. We also showed that soaring habitat for birds can be modelled at a fine scale using publicly available data. Such an approach can be used to plan low‐impact placement of turbines in new wind energy developments.
DOI: 10.34740/kaggle/dsv/1875070
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.21227/wzsh-zx91
title: overpass3d: a trajectory dataset for road junction mapping

description: This dataset consists of GPS trajectories of vehicles at fifteen road junctions and maps of road junctions with annotations of slope sections.
DOI: 10.6084/m9.figshare.13515224
title: Fertility data for male candidate genes

description: Data is sourced from the referenced publication, or via correspondence with authors.
DOI: 10.6084/m9.figshare.13515224.v3
title: Fertility data for male candidate genes

description: Data is sourced from the referenced publication, or via correspondence with authors.
DOI: 10.6084/m9.figshare.13515224.v2
title: Fertility data for male candidate genes

description: Data is sourced from the referenced publication, or via correspondence with authors.
DOI: 10.5281/zenodo.4460346
title: Replication package for: "Exchange Rate Exposure and Firm Dynamics"

description: This package contains all the code necessary to reproduce figures and tables in Salomao and Varela (forthcoming). "Exchange Rate Exposure and Firm Dynamics" Review of Economic Studies. Instructions are also given about accessing the data.
DOI: 10.5517/ccdc.csd.cc24m0ld
title: CCDC 1984483: Experimental Crystal Structure Determination

description: Related Article: Abhay K. Patel, Rajendra N. Jadeja, Ray.J. Butcher, Manoj K. Kesharwani, Johannes Kästner, Mohd. Muddassir|2021|Polyhedron|195|114969|doi:10.1016/j.poly.2020.114969
DOI: 10.5517/ccdc.csd.cc24m0mf
title: CCDC 1984484: Experimental Crystal Structure Determination

description: Related Article: Abhay K. Patel, Rajendra N. Jadeja, Ray.J. Butcher, Manoj K. Kesharwani, Johannes Kästner, Mohd. Muddassir|2021|Polyhedron|195|114969|doi:10.1016/j.poly.2020.114969
DOI: 10.5517/ccdc.csd.cc25d9d9
title: CCDC 2007820: Experimental Crystal Structure Determination

description: Related Article: Xiao Ma, Ling Jia, Baozhu Yang, Jipeng Li, Wei Huang, Dayu Wu, Wai-Yeung Wong|2021|J.Mater.Chem.C|9|727|doi:10.1039/D0TC04234J
DOI: 10.5517/ccdc.csd.cc25d9fb
title: CCDC 2007821: Experimental Crystal Structure Determination

description: Related Article: Xiao Ma, Ling Jia, Baozhu Yang, Jipeng Li, Wei Huang, Dayu Wu, Wai-Yeung Wong|2021|J.Mater.Chem.C|9|727|doi:10.1039/D0TC04234J
DOI: 10.5517/ccdc.csd.cc26llhx
title: CCDC 2043659: Experimental Crystal Structure Determination

description: Related Article: Lin Cheng, Ying Wang, Yi-Ming Zhao, Dan-Dan Yang, Shuang-Shuang Gong, Zhong-Yi Liu, Ying Wang, Kai-Yao Wang|2021|Inorg.Chem.Commun.|124|108391|doi:10.1016/j.inoche.2020.108391
DOI: 10.5517/ccdc.csd.cc26lljy
title: CCDC 2043660: Experimental Crystal Structure Determination

description: Related Article: Lin Cheng, Ying Wang, Yi-Ming Zhao, Dan-Dan Yang, Shuang-Shuang Gong, Zhong-Yi Liu, Ying Wang, Kai-Yao Wang|2021|Inorg.Chem.Commun.|124|108391|doi:10.1016/j.inoche.2020.108391
DOI: 10.5281/zenodo.4460161
title: M1_1

description: dataset M1_1 for OMERO
DOI: 10.5281/zenodo.4460160
title: M1_1

description: dataset M1_1 for OMERO
DOI: 10.5519/qd.wmh8zlw0
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4706577 records)
DOI: 10.5281/zenodo.4460354
title: A 30m Topographic Wetness Index Dataset for the Continental United States

description: The topographic wetness index was computed at a 30m resolution for the continental United States. This dataset was computed using the triangular multiple flow direction algorithm for computing upslope areas (see https://doi.org/10.1029/2006WR005128). We used the Shuttle Radar Topography Mission (SRTM; https://doi.org/10.1029/2005RG000183) digital elevation model (DEM) for all computations. This analysis was conducted in the R programming language using the System for Automated Geoscientific Analyses (SAGA; https://doi.org/10.5194/gmd-8-1991-2015) for back end computations. This dataset is in the NAD83(NSRS2007) / Conus Albers projection system, EPSG 5072.
DOI: 10.25345/c5150j
title: MassIVE MSV000086758 - Analysis of RiPP gene cluster from marine sponges
title: The aim of the project is the study of the widely distributed ribosomally synthesized and post-translationally modified peptides gene cluster found in diverse genus of marine sponges from different geographical locations
DOI: 10.5281/zenodo.3858032
title: A Dataset of Pull Requests and A Trained Random Forest Model for predicting Pull Request Acceptance

description: A Curated Dataset of 470,925 pull requests for 3349 popular NPM packages, description of the variables, code snippet for creating a Random Forest model for predicting pull request acceptance, and a pre-trained Random Forest model (in R). The dataset is for the ESEM-2020 paper: "Impact of Technical and Social Factors on Pull Request Quality for the NPM Ecosystem" (https://arxiv.org/abs/2007.04816). Citation: <pre>@inproceedings{dey2020effect, title={Effect of technical and social factors on pull request quality for the npm ecosystem}, author={Dey, Tapajit and Mockus, Audris}, booktitle={Proceedings of the 14th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)}, pages={1--11}, year={2020} }</pre>