cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6821784
title: WiPerf Vehicular Wi-Fi Performance Monitoring Dataset

description: WiPerf is a Wi-Fi performance monitoring system that can measure quality-of-service (i.e., throughput) and collect channel state and GPS information from vehicles using Wi-Fi for Vehicle-to-Infrastructure (V2I) and Vehicle-to-Everything (V2X) communications. The dataset contains data collected on June 14<sup>th</sup>, 2022, from a real-world vehicular setup. For the setup we mounted two TP-Link Talon AD7200 routers on top of two vehicles, i.e., a 2006 Renault Megane Break and a 2002 Honda Jazz. One of the cars remained static while the other was driven around the stationary node for 10 laps at Rua D. Frei Vicente da Soledade e Castro in Porto,<br> Portugal. We collected 802.11n, 802.11ac, and 802.11ad data simultaneously using the three radio interfaces of the Talon AD7200 routers. The dataset also includes throughput and CPU load data collected using <em>iperf3 </em>and the <em>sar </em>application from the <em>sysstat </em>Linux utility package. These measurements were taken in a lab environment using the same Talon AD7200 routers.
DOI: 10.5281/zenodo.6821792
title: WiPerf Vehicular Wi-Fi Performance Monitoring Dataset

description: WiPerf is a Wi-Fi performance monitoring system that can measure quality-of-service (i.e., throughput) and collect channel state and GPS information from vehicles using Wi-Fi for Vehicle-to-Infrastructure (V2I) and Vehicle-to-Everything (V2X) communications. The dataset contains data collected on June 14<sup>th</sup>, 2022, from a real-world vehicular setup. For the setup we mounted two TP-Link Talon AD7200 routers on top of two vehicles, i.e., a 2006 Renault Megane Break and a 2002 Honda Jazz. One of the cars remained static while the other was driven around the stationary node for 10 laps at Rua D. Frei Vicente da Soledade e Castro in Porto,<br> Portugal. We collected 802.11n, 802.11ac, and 802.11ad data simultaneously using the three radio interfaces of the Talon AD7200 routers. The dataset also includes throughput and CPU load data collected using <em>iperf3 </em>and the <em>sar </em>application from the <em>sysstat </em>Linux utility package. These measurements were taken in a lab environment using the same Talon AD7200 routers.
DOI: 10.5281/zenodo.6761916
title: WiPerf Vehicular Wi-Fi Performance Monitoring Dataset

description: WiPerf is a Wi-Fi performance monitoring system that can measure quality-of-service (i.e., throughput) and collect channel state and GPS information from vehicles using Wi-Fi for Vehicle-to-Infrastructure (V2I) and Vehicle-to-Everything (V2X) communications. The dataset contains data collected on June 14<sup>th</sup>, 2022, from a real-world vehicular setup. For the setup we mounted two TP-Link Talon AD7200 routers on top of two vehicles, i.e., a 2006 Renault Megane Break and a 2002 Honda Jazz. One of the cars remained static while the other was driven around the stationary node for 10 laps at Rua D. Frei Vicente da Soledade e Castro in Porto,<br> Portugal. We collected 802.11n, 802.11ac, and 802.11ad data simultaneously using the three radio interfaces of the Talon AD7200 routers. The dataset also includes throughput and CPU load data collected using <em>iperf3 </em>and the <em>sar </em>application from the <em>sysstat </em>Linux utility package. These measurements were taken in a lab environment using the same Talon AD7200 routers.
DOI: 10.6084/m9.figshare.20288772
title: Supplemental Table 2: Raw echo data separated by genotype and sex.

description: Raw echo data separated by sex and genotype for the associated project.
DOI: 10.6084/m9.figshare.20288772.v1
title: Supplemental Table 2: Raw echo data separated by genotype and sex.

description: Raw echo data separated by sex and genotype for the associated project.
DOI: 10.6084/m9.figshare.20288760
title: Supplemental Table 1. Primer sequences used for RT-qPCR analysis.

description: qPCR primer sets used for associated paper.
DOI: 10.5281/zenodo.6821796
title: DATASET: Environmental analysis of servicing centralised and decentralised wastewater treatment for population living in neighbourhoods

description: DATASET: Article: Environmental analysis of servicing centralised and decentralised wastewater treatment for population living in neighbourhoods Journal of Water Process Engineering, Volume 37, October 2020, 101469 https://doi.org/10.1016/j.jwpe.2020.101469
DOI: 10.5281/zenodo.6821795
title: DATASET: Environmental analysis of servicing centralised and decentralised wastewater treatment for population living in neighbourhoods

description: DATASET: Article: Environmental analysis of servicing centralised and decentralised wastewater treatment for population living in neighbourhoods Journal of Water Process Engineering, Volume 37, October 2020, 101469 https://doi.org/10.1016/j.jwpe.2020.101469
DOI: 10.5281/zenodo.6821810
title: DATASET: What is the best scale for implementing anaerobic digestion according to environmental and economic indicators?

description: DATASET: What is the best scale for implementing anaerobic digestion according to environmental and economic indicators? Journal of Water Process Engineering, Volume 35, June 2020, 101235 https://doi.org/10.1016/j.jwpe.2020.101235
DOI: 10.5281/zenodo.6821809
title: DATASET: What is the best scale for implementing anaerobic digestion according to environmental and economic indicators?

description: DATASET: What is the best scale for implementing anaerobic digestion according to environmental and economic indicators? Journal of Water Process Engineering, Volume 35, June 2020, 101235 https://doi.org/10.1016/j.jwpe.2020.101235
DOI: 10.5281/zenodo.6820681
title: An Empirical Evaluation of Competitive Programming AI: A Case Study of AlphaCode

description: <strong>Abstract</strong><br> AlphaCode, the code generation system by DeepMind, is an AI technology for assisting software developers in solving competitive programming problems using natural language problem descriptions.<br> However, there is no existing work comparing AlphaCode-generated codes with human codes.<br> In this paper, we conduct an empirical study to find code similarities and performance differences between AlphaCode-generated codes and human codes.<br> We collect 44 generated codes in C++ and Python languages from AlphaCode official website that solve 22 problems on Codeforces.<br> We then retrieve 31,736 human codes by using Codeforces API.<br> The results show that (i) the generated code from AlphaCode and the human code is not similar (i.e., the similarity of 0.26 for both C++ and Python), however, the code fragments in the generated code are comprised of various human codes (i.e., uniqueness of 3.30\% and 8.94\% for C++ and Python respectively) and<br> (ii) the generated code performs on par with or worse than the human code in terms of execution time and memory usage.<br> Moreover, AlphaCode employs excessive nested loops and unnecessary variable declarations (e.g., used \textit{long long} instead of \textit{int}), which causes a low performance regarding our manual investigation.
DOI: 10.5281/zenodo.6820680
title: An Empirical Evaluation of Competitive Programming AI: A Case Study of AlphaCode

description: <strong>Abstract</strong><br> AlphaCode, the code generation system by DeepMind, is an AI technology for assisting software developers in solving competitive programming problems using natural language problem descriptions.<br> However, there is no existing work comparing AlphaCode-generated codes with human codes.<br> In this paper, we conduct an empirical study to find code similarities and performance differences between AlphaCode-generated codes and human codes.<br> We collect 44 generated codes in C++ and Python languages from AlphaCode official website that solve 22 problems on Codeforces.<br> We then retrieve 31,736 human codes by using Codeforces API.<br> The results show that (i) the generated code from AlphaCode and the human code is not similar (i.e., the similarity of 0.26 for both C++ and Python), however, the code fragments in the generated code are comprised of various human codes (i.e., uniqueness of 3.30\% and 8.94\% for C++ and Python respectively) and<br> (ii) the generated code performs on par with or worse than the human code in terms of execution time and memory usage.<br> Moreover, AlphaCode employs excessive nested loops and unnecessary variable declarations (e.g., used \textit{long long} instead of \textit{int}), which causes a low performance regarding our manual investigation.
DOI: 10.5281/zenodo.6821220
title: Dataset Bottom-up approach in the assessment of environmental impacts and costs of an innovative anammox-based process for nitrogen removal

description: Dataset of Bottom-up approach in the assessment of environmental impacts and costs of an innovative anammox-based process for nitrogen removal J Environ Manage 2018 Nov 1;225:112-119.doi: 10.1016/j.jenvman.2018.07.070. Epub 2018 Jul 31.
DOI: 10.5281/zenodo.6821219
title: Dataset Bottom-up approach in the assessment of environmental impacts and costs of an innovative anammox-based process for nitrogen removal

description: Dataset of Bottom-up approach in the assessment of environmental impacts and costs of an innovative anammox-based process for nitrogen removal J Environ Manage 2018 Nov 1;225:112-119.doi: 10.1016/j.jenvman.2018.07.070. Epub 2018 Jul 31.
DOI: 10.14459/2022mp1661654
title: AI Generated Galaxy Images

description: Datasets of AI generated galaxy images based on simulations (TNG-Illustris), observations (COSMOS) and analytic expressions (Sérsic profiles)
DOI: 10.6084/m9.figshare.20290704
title: Supplemental table summarizing the clinical and evolutive features of 27 patients with lymphoid HES or CTCL

description: Clinical and evolutive features of 27 patients with lymphoid hypereosinophilic syndrome and/or cutaneous T-cell lymphoma
DOI: 10.6084/m9.figshare.20290704.v1
title: Supplemental table summarizing the clinical and evolutive features of 27 patients with lymphoid HES or CTCL

description: Clinical and evolutive features of 27 patients with lymphoid hypereosinophilic syndrome and/or cutaneous T-cell lymphoma
DOI: 10.5281/zenodo.6821824
title: DATASET: How decentralized treatment can contribute to the symbiosis between environmental protection and resource recovery

description: DATASET: How decentralized treatment can contribute to the symbiosis between environmental protection and resource recovery Science of The Total Environment, Volume 812, 15 March 2022, 151485 10.1016/j.scitotenv.2021.151485
DOI: 10.5281/zenodo.6821823
title: DATASET: How decentralized treatment can contribute to the symbiosis between environmental protection and resource recovery

description: DATASET: How decentralized treatment can contribute to the symbiosis between environmental protection and resource recovery Science of The Total Environment, Volume 812, 15 March 2022, 151485 10.1016/j.scitotenv.2021.151485
DOI: 10.26180/20291850
title: S1_File.docx

description: This supporting file S1 provides details of the search strategy used for our systematic review.
DOI: 10.26180/20291850.v1
title: S1_File.docx

description: This supporting file S1 provides details of the search strategy used for our systematic review.
DOI: 10.6084/m9.figshare.20291856
title: Table S1.xlsx

description: Table S1a: Statistical information of the proteome of A. ferrooxidans ATCC23270 (Genbank accession: GCA_000021485.1) including protein name, sequence, COG categories, protein length, corresponding structure name in DataS1 at https://doi.org/10.6084/m9.figshare.19093109.v1, structure confidence, G+C content. Table S1b: Functional prediction results of unannotated proteins in the proteome of Acidithiobacillus ferrooxidans ATCC23270 by DeepFRI. Table S1c: Comparison of the predictions made by RoseTTAFold and AlphaFold2 on a subset protein sequences (n=113) of Acidithiobacillus ferrooxidans ATCC23270 using TM-align. The comparison results show that the predicted models from both methods agree well with each other with average pairwise TM-score of 0.93 and average pairwise RMSD of 1.58. The outcoming models (.pdb) of both methods in this test are available at https://doi.org/10.6084/m9.figshare.19583692.v1
DOI: 10.6084/m9.figshare.20291856.v1
title: Table S1.xlsx

description: Table S1a: Statistical information of the proteome of A. ferrooxidans ATCC23270 (Genbank accession: GCA_000021485.1) including protein name, sequence, COG categories, protein length, corresponding structure name in DataS1 at https://doi.org/10.6084/m9.figshare.19093109.v1, structure confidence, G+C content. Table S1b: Functional prediction results of unannotated proteins in the proteome of Acidithiobacillus ferrooxidans ATCC23270 by DeepFRI. Table S1c: Comparison of the predictions made by RoseTTAFold and AlphaFold2 on a subset protein sequences (n=113) of Acidithiobacillus ferrooxidans ATCC23270 using TM-align. The comparison results show that the predicted models from both methods agree well with each other with average pairwise TM-score of 0.93 and average pairwise RMSD of 1.58. The outcoming models (.pdb) of both methods in this test are available at https://doi.org/10.6084/m9.figshare.19583692.v1
DOI: 10.26180/20291862
title: S2_File.pdf

description: This supporting file S2 listed reported benefits of HIS interventions on patient flow management.
DOI: 10.26180/20291862.v1
title: S2_File.pdf

description: This supporting file S2 listed reported benefits of HIS interventions on patient flow management.
DOI: 10.5281/zenodo.5798028
title: Lizards from warm and declining populations are born with extremely short telomeres

description: These two datasets report the information at the populational ("Population_Biogeography2017-2018.csv") and individual ("Telomere_Zootocavivipara_2015-2017.csv") levels. At populational level, we studied the covariation of multiple biogeographic measures to obtain an integrative index of population extinction risk. At individual level, we examined what factors best explained the variation in lizard telomere length. We also uploaded the R code ("DataAnalysis_Telomerelizards_AndreazDupoue.R") used to analyse these data, in which we detailed all variables.
DOI: 10.5281/zenodo.5798027
title: Lizards from warm and declining populations are born with extremely short telomeres

description: These two datasets report the information at the populational ("Population_Biogeography2017-2018.csv") and individual ("Telomere_Zootocavivipara_2015-2017.csv") levels. At populational level, we studied the covariation of multiple biogeographic measures to obtain an integrative index of population extinction risk. At individual level, we examined what factors best explained the variation in lizard telomere length. We also uploaded the R code ("DataAnalysis_Telomerelizards_AndreazDupoue.R") used to analyse these data, in which we detailed all variables.
DOI: 10.6084/m9.figshare.20290650
title: Relationship of multiple blood metals and osteoporosis in Chinese older women: An Aging and Longevity study

description: supplement table
DOI: 10.6084/m9.figshare.20290650.v1
title: Relationship of multiple blood metals and osteoporosis in Chinese older women: An Aging and Longevity study

description: supplement table
DOI: 10.21942/uva.20029991
title: Spatio-Temporal Relative Sea Level Curve (RSL)

description: <strong>DESCRIPTION: </strong> <br> The spatio-temporal relative sea level curve (RSL) was developed using the software SealEveL EquatioN solver - version 4 (SELEN4; https://zenodo.org/record/3520451) (Spada et al. 2019), the global ice sheet reconstruction (ICE-5G (VM2); https://pmip2.lsce.ipsl.fr/design/ice5g/) (Peltier 2004) and DEMSRE3a (https://zenodo.org/record/1637816). <br> <strong>MAPPED VALUES:</strong> <br> Relative sea level stand below present in meter. <br> <strong>SPATIAL RESOLUTION:</strong> <br> The spatial resolution of the dataset is 0.2 degrees (WGS84). <br> <strong>TEMPORAL RESOLUTION:</strong> <br> Relative sea level for every 500 years from 0 BP to 26000 BP. <br> The dataset is provided in two formats: <br> <strong>1. TILED IN ASCII (RSL_tiles.zip):</strong> <br> 53 tiles of 10x10 degrees per folder each representing the relative sea level for a 500 yr time period. <br> Directory naming: structure: //RSL..asc example: 10/010-020/RSL.005.asc description: Tile with relative sea level from 0 to 10 degrees latitude, and 10 to 20 degrees longitude, per 500 yr BP. <br> <strong>2. MOSAIC IN TIF (RSL_mosaic.zip):</strong> <br> 53 global maps each presenting the relative sea level for a 500 yr time period. <br> File naming: structure: RSL.tif example: RSL005.tif description: Relative sea level map per 500 yr BP. <br> <br> <br>
DOI: 10.21942/uva.20029991.v1
title: Spatio-Temporal Relative Sea Level Curve (RSL)

description: <strong>DESCRIPTION: </strong> <br> The spatio-temporal relative sea level curve (RSL) was developed using the software SealEveL EquatioN solver - version 4 (SELEN4; https://zenodo.org/record/3520451) (Spada et al. 2019), the global ice sheet reconstruction (ICE-5G (VM2); https://pmip2.lsce.ipsl.fr/design/ice5g/) (Peltier 2004) and DEMSRE3a (https://zenodo.org/record/1637816). <br> <strong>MAPPED VALUES:</strong> <br> Relative sea level stand below present in meter. <br> <strong>SPATIAL RESOLUTION:</strong> <br> The spatial resolution of the dataset is 0.2 degrees (WGS84). <br> <strong>TEMPORAL RESOLUTION:</strong> <br> Relative sea level for every 500 years from 0 BP to 26000 BP. <br> The dataset is provided in two formats: <br> <strong>1. TILED IN ASCII (RSL_tiles.zip):</strong> <br> 53 tiles of 10x10 degrees per folder each representing the relative sea level for a 500 yr time period. <br> Directory naming: structure: //RSL..asc example: 10/010-020/RSL.005.asc description: Tile with relative sea level from 0 to 10 degrees latitude, and 10 to 20 degrees longitude, per 500 yr BP. <br> <strong>2. MOSAIC IN TIF (RSL_mosaic.zip):</strong> <br> 53 global maps each presenting the relative sea level for a 500 yr time period. <br> File naming: structure: RSL.tif example: RSL005.tif description: Relative sea level map per 500 yr BP. <br> <br> <br>
DOI: 10.21942/uva.20029304
title: Historic coastline age raster (AGE 2021)

description: <strong>DESCRIPTION: </strong><br> Global paleo drowning map indicating the most recent 500 yr time period a pixel was land. The reconstruction was done using the global bathymetric model (GEBCO 2021) and a state-of-the-art global spatiotemporal sea level curve. The global sea level curve was generated using the software SELEN4 (10.5281/zenodo.3520451) and raster DEMSRE3a (10.5281/zenodo.1637816).<br> <strong>MAPPED CATEGORIES:</strong><br> from 0 to 260 in bins of 5 (e.g., 0, 5, 10, 15, 20, ..., 260; each bin corresponds to 500 yr) <br> 0 = 0 yr BP<br> 5 = 500 yr BP <br> 10 = 1000 yr BP<br> ...<br> ... <br> 100 = 10000 yr BP<br> ...<br> ...<br> 200 = 20000 yr BP <br> ...<br> ...<br> 260 = 26000 yr BP <br> <strong>MAPPED NA VALUES: </strong><br> No Data = pixel has always been Below (Past) Sea Level since 26000 BP.<br> <strong>SPATIAL RESOLUTION:</strong><br> The spatial resolution of the dataset is a factor 8 of the original GEBCO. <br> <strong>TEMPORAL RESOLUTION:</strong><br> Temporal mapping resolution is 500 yr. <br> <strong>DIRECTORY STRUCTURE:</strong> The dataset is provided in two formats:<br> <strong>1. TILED IN ASCII (AGE_tiles.zip):</strong><br> tiles of 10x10 degrees each representing the drowning age.<br> File naming:<br> structure: AGE--.asc <br> example: AGE-10-010-020.asc <br> description: Tile with drowning age from 0 to 10 degrees latitude, and 10 to 20 degrees longitude. <br> <strong>2. MOSAIC IN TIF (AGE_mosaic.tif):</strong><br> Global drowning map. <br> File naming: <br> structure: AGE.tif<br> example: AGE.tif<br> description: Global drowning map
DOI: 10.21942/uva.20029304.v1
title: Historic coastline age raster (AGE 2021)

description: <strong>DESCRIPTION: </strong><br> Global paleo drowning map indicating the most recent 500 yr time period a pixel was land. The reconstruction was done using the global bathymetric model (GEBCO 2021) and a state-of-the-art global spatiotemporal sea level curve. The global sea level curve was generated using the software SELEN4 (10.5281/zenodo.3520451) and raster DEMSRE3a (10.5281/zenodo.1637816).<br> <strong>MAPPED CATEGORIES:</strong><br> from 0 to 260 in bins of 5 (e.g., 0, 5, 10, 15, 20, ..., 260; each bin corresponds to 500 yr) <br> 0 = 0 yr BP<br> 5 = 500 yr BP <br> 10 = 1000 yr BP<br> ...<br> ... <br> 100 = 10000 yr BP<br> ...<br> ...<br> 200 = 20000 yr BP <br> ...<br> ...<br> 260 = 26000 yr BP <br> <strong>MAPPED NA VALUES: </strong><br> No Data = pixel has always been Below (Past) Sea Level since 26000 BP.<br> <strong>SPATIAL RESOLUTION:</strong><br> The spatial resolution of the dataset is a factor 8 of the original GEBCO. <br> <strong>TEMPORAL RESOLUTION:</strong><br> Temporal mapping resolution is 500 yr. <br> <strong>DIRECTORY STRUCTURE:</strong> The dataset is provided in two formats:<br> <strong>1. TILED IN ASCII (AGE_tiles.zip):</strong><br> tiles of 10x10 degrees each representing the drowning age.<br> File naming:<br> structure: AGE--.asc <br> example: AGE-10-010-020.asc <br> description: Tile with drowning age from 0 to 10 degrees latitude, and 10 to 20 degrees longitude. <br> <strong>2. MOSAIC IN TIF (AGE_mosaic.tif):</strong><br> Global drowning map. <br> File naming: <br> structure: AGE.tif<br> example: AGE.tif<br> description: Global drowning map
DOI: 10.21942/uva.20029220
title: Shelf sea extent rasters (SLW140 2021)

description: <strong>DESCRIPTION: </strong> <br> Global paleoreconstruction of shelf sea extent for every 500 yr time period using the global bathymetric model (GEBCO 2021) and a state-of-the-art global spatiotemporal sea level curve. The global sea level curve was generated using the software SELEN4 (10.5281/zenodo.3520451) and raster DEMSRE3a (10.5281/zenodo.1637816). <br> <strong>MAPPED CATEGORIES:</strong> <br> 0 = &gt; 0 m BPSL (Below Past Sea Level) 1 = Between 0 and -140 m BPSL <br> <strong>MAPPED NA VALUES: </strong> <br> No Data = &lt; -140 m BPSL <br> <strong>SPATIAL RESOLUTION:</strong> <br> The spatial resolution of the dataset is a factor 8 of the original GEBCO. <br> <strong>TEMPORAL RESOLUTION:</strong> <br> A reconstruction is made for every 500 years from 0 BP to 26000 BP. <br> The dataset is provided in two formats: <br> <strong>1. TILED IN ASCII (SLW_tiles.zip):</strong> <br> 53 tiles of 10x10 degrees per folder each representing shallow water for a 500 yr time period. <br> Directory naming: structure: //SLW..asc example: 10/010-020/SLW.005.asc description: Tile with shelf sea extent from 0 to 10 degrees latitude, and 10 to 20 degrees longitude, for 500 yr BP. <br> <strong>2. MOSAIC IN TIF (SLW_mosaic.zip):</strong> <br> 53 global maps each presenting shallow water for a 500 yr time period. <br> File naming: structure: SLW.tif example: SLW005.tif description: Global shelf sea extent map for 500 yr BP. <br> <br>
DOI: 10.26180/20291886
title: S3_File.xlsx

description: Included studies and main findings
DOI: 10.26180/20291886.v1
title: S3_File.xlsx

description: Included studies and main findings
DOI: 10.21942/uva.17218724
title: Spatial polygons for Sunda, Timor, and Caribbean regions.

description: <b>DESCRIPTION:</b><br><br> Spatial polygons for Sunda, Timor, and Caribbean regions.<br> These are used for drawing region-scale figures (Fig. S2)<br><br><b>CAUTION:</b><br><br> These are hand-drawn to roughly capture the tendencies around the focal regions.<br> <br><br>
DOI: 10.21942/uva.17218484
title: Resampled Bathymetry (GEBCO 2019)

description: <b>DESCRIPTION:</b><br><br> Modern global batymetric map. The original data is GEBCO_2019. The spatial resolution was reduced to 2-minutes grid level.<br><br><b>VALUES: </b><br><br> Average elevation in meters for land and ocean<br><br><b>SPATIAL RESOLUTION:</b><br><br> 120 arc seconds<br><br><b>FILE NAMING:</b><br> <br> File naming: BATHYMETRY.tif<br> <br><br>