cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7910/dvn/b74gn1/esh9f6
title: apygardos.1996.daug.vien1.5r.tab

description: :unav
DOI: 10.5281/zenodo.3667601
title: Data from: Dynamic simulations of microbial communitiesunder perturbations: opportunities formicrobiome engineering

description: Input and output files of the application of our MDPbiomeGEM system (available at https://github.com/beatrizgj/MDPbiomeGEM) to different microbial communities, in particular from human gut microbiome and soil microbiome. Each case study (i.e. microbial community) has the following structure, corresponding to the different steps performed by our system MDPbiomeGEM:<br> 1. InputGEM: Genome-scale metabolic models (GEM) and medium composition<br> 2. OutputMMODES: simulated microbial community timeseries by MMODES<br> 3. MmodesToMDPbiome: transformation of output from MMODES in input to MDPbiome<br> 4. OutRobustClustering: identification of microbiome states<br> 5. OutputMDPbiome: output of MDPbiome, with prediction of microbiome state changes and interventions a. Human gut microbiome (BifFae): the microbes in this community are <em>Bifidobacterium adolescentis L2-32</em> (<em>i</em>Bif452) and <em>Faecalibacterium prausnitizii A2-135</em> (<em>i</em>Fap484), whose GEMs were taken from the original publication [El-Semman et al.,2014] (https://doi.org/10.1186/1752-0509-8-41). We upload here those GEMs adjusted to satisfy the scenario we model, that is described in our manuscript. b. Soil microbiome (Atrazine): the microbes in this community are <em>P. aurescens, H. stevensii</em>, <em>Halobacillus sp</em>. The GEMs used in this consortium were kindly provided by the authors of [Xu et al.,2019] (https://doi.org/10.1038/s41396-018-0288-5). Model construction was lead by Raphy Zarecki. With the exception of <em>P.aurescens TC1</em> [Ofaim et al.,2019] (http://dx.doi.org/10.1101/536011) they had not yet been made publicly available. With the permission of the authors of the original models [Xu et al.,2019], we upload here those GEMs adjusted to satisfy the scenario we model, that is described in our manuscript.
DOI: 10.5281/zenodo.3667602
title: Data from: Dynamic simulations of microbial communitiesunder perturbations: opportunities formicrobiome engineering

description: Input and output files of the application of our MDPbiomeGEM system (available at https://github.com/beatrizgj/MDPbiomeGEM) to different microbial communities, in particular from human gut microbiome and soil microbiome. Each case study (i.e. microbial community) has the following structure, corresponding to the different steps performed by our system MDPbiomeGEM:<br> 1. InputGEM: Genome-scale metabolic models (GEM) and medium composition<br> 2. OutputMMODES: simulated microbial community timeseries by MMODES<br> 3. MmodesToMDPbiome: transformation of output from MMODES in input to MDPbiome<br> 4. OutRobustClustering: identification of microbiome states<br> 5. OutputMDPbiome: output of MDPbiome, with prediction of microbiome state changes and interventions a. Human gut microbiome (BifFae): the microbes in this community are <em>Bifidobacterium adolescentis L2-32</em> (<em>i</em>Bif452) and <em>Faecalibacterium prausnitizii A2-135</em> (<em>i</em>Fap484), whose GEMs were taken from the original publication [El-Semman et al.,2014] (https://doi.org/10.1186/1752-0509-8-41). We upload here those GEMs adjusted to satisfy the scenario we model, that is described in our manuscript. b. Soil microbiome (Atrazine): the microbes in this community are <em>P. aurescens, H. stevensii</em>, <em>Halobacillus sp</em>. The GEMs used in this consortium were kindly provided by the authors of [Xu et al.,2019] (https://doi.org/10.1038/s41396-018-0288-5). Model construction was lead by Raphy Zarecki. With the exception of <em>P.aurescens TC1</em> [Ofaim et al.,2019] (http://dx.doi.org/10.1101/536011) they had not yet been made publicly available. With the permission of the authors of the original models [Xu et al.,2019], we upload here those GEMs adjusted to satisfy the scenario we model, that is described in our manuscript.
DOI: 10.25405/data.ncl.11855532
title: Hadrian’s Wall – Forts

description: Shapefile of all major Roman forts within 100km of the line of Hadrian’s Wall. Original material evidenced from Ordnance Survey 1:25,000 base map and J. Collingwood Bruce’s ‘The Handbook to the Roman Wall. (Breeze 2006). Attribute table includes information about fort type, location (easting and northing – national grid reference), modern name and latin name. GIS Vector data (point) created using ArcGIS ArcMap 10.5 in OSG36 (EPSG:27700) co-ordinate system. This dataset was created as part of the Hadrian’s Wall Community Archaeology Project, funded by the National Lottery Heritage Fund.<br>References1:25 000 Scale Colour Raster [TIFF geospatial data], Scale 1:25000, Ordnance Survey (GB), Using: EDINA Digimap Ordnance Survey Service, , Downloaded: 2017-10-24 15:07:52.57 Breeze, D.J. 2006. J. Collingwood Bruce’s Handbook to the Roman Wall. Society of Antiquaries of Newcastle upon Tyne. Collins, R. and Symonds, M. (eds.) Hadrian’s Wall 2009-2019: A Summary of Excavation and Research prepared for The Fourteenth Pilgrimage of Hadrian’s Wall, 20-28 July 2019. Cumberland and Westmorland Antiquarian and Archaeological Society and the Society of Antiquaries of Newcastle upon Tyne, Kendal, 19-26.
DOI: 10.25405/data.ncl.11855580
title: Hadrian’s Wall – The Cumbrian Coast

description: Shapefile of all sites (milefortlets and towers) that represent the Cumbrian Coastal system of the Hadrian’s Wall frontier. Original material evidenced from Ordnance Survey 1:25,000 base map, J. Collingwood Bruce’s ‘The Handbook to the Roman Wall. (Breeze 2006), Historic England’s ‘An Archaeological Map of Hadrian’s Wall’ (Historic England 2014) and evidence from recent archaeological investigation (Collins and Symonds 2019). Attribute table includes information about fort type, location (easting and northing – national grid reference), modern name and reference number within Cumbria coastal system. GIS Vector data (point) created using ArcGIS ArcMap 10.5 in OSG36 (EPSG:27700) co-ordinate system. This dataset was created as part of the Hadrian’s Wall Community Archaeology Project, funded by the National Lottery Heritage Fund.<br> References 1:25 000 Scale Colour Raster [TIFF geospatial data], Scale 1:25000, Ordnance Survey (GB), Using: EDINA Digimap Ordnance Survey Service, , Downloaded: 2017-10-24 15:07:52.57 Breeze, D.J. 2006. J. Collingwood Bruce’s Handbook to the Roman Wall. Society of Antiquaries of Newcastle upon Tyne. Collins, R. and Symonds, M. (eds.) Hadrian’s Wall 2009-2019: A Summary of Excavation and Research prepared for The Fourteenth Pilgrimage of Hadrian’s Wall, 20-28 July 2019. Cumberland and Westmorland Antiquarian and Archaeological Society and the Society of Antiquaries of Newcastle upon Tyne, Kendal, 19-26. Historic England, 2014. An Archaeological Map of Hadrian’s Wall. Revised Edition. London.
DOI: 10.21227/dnev-f754
title: H2Arm - BSP vs PID experiments

description: Many different methods have been proposed in the control and robotics literature for the control of robotics arms. Our main aim is to provide a complete software and hardware platform which allow the statistical replication of our results and the experimentation of other more or less sophisticated control strategies and algorithms.In our work, in order to validate our reproducible research platform and provide a template methodology for its usage, we have thoroughly compared in a reproducible way the performance of simple BSP and PID controls when applied to a light-weight, low accuracy and compliant open source robot arm (H2Arm). BSP significantly outperforms PID on this platform, but not w.r.t. to all metrics.The findings are interesting by themselves. They also show how easily statistically weak results can lead to qualitatively wrong conclusions, if you cherry-pick results.The present dataset contains: i) CAD design to 3D-print the H2Arm; ii) experimental data of comparison BSP vs PID; iii) all the code used to perform the experiments (both BSP and PID).
DOI: 10.21227/tfky-2z36
title: H2Arm - BSP vs PID experiments

description: Many different methods have been proposed in the control and robotics literature for the control of robotics arms. Our main aim is to provide a complete software and hardware platform which allow the statistical replication of our results and the experimentation of other more or less sophisticated control strategies and algorithms.In our work, in order to validate our reproducible research platform and provide a template methodology for its usage, we have thoroughly compared in a reproducible way the performance of simple BSP and PID controls when applied to a light-weight, low accuracy and compliant open source robot arm (H2Arm). BSP significantly outperforms PID on this platform, but not w.r.t. to all metrics.The findings are interesting by themselves. They also show how easily statistically weak results can lead to qualitatively wrong conclusions, if you cherry-pick results.The present dataset contains: i) CAD design to 3D-print the H2Arm; ii) experimental data of comparison BSP vs PID; iii) all the code used to perform the experiments (both BSP and PID).
DOI: 10.25405/data.ncl.11855592
title: Hadrian's Wall - Frontier system

description: Shapefile of all major features of Hadrian’s Wall frontier (curtain wall, wall ditch and vallum). Original material evidenced from Ordnance Survey 1:25,000 base map, Historic England’s ‘An Archaeological Map of Hadrian’s Wall’ (Historic England 2014) and evidence from recent archaeological investigation (Collins and Symonds 2019). Shapefile provides simple representation of frontier as a polyline following a possible line of Hadrian’s Wall. The position of the line represents the central point of each feature. The line depicting the Vallum follows the base of the Vallum ditch. Breaks in the polyline data may represent breaks in the original line of the wall or a lack of archaeological information. Attribute table includes information length of segment (in metres) and features type (curtain wall, wall ditch, Vallum). GIS Vector data (polyline) created using ArcGIS ArcMap 10.5 in OSG36 (EPSG:27700) co-ordinate system. This dataset was created as part of the Hadrian’s Wall Community Archaeology Project, funded by the National Lottery Heritage Fund.<br> References 1:25 000 Scale Colour Raster [TIFF geospatial data], Scale 1:25000, Ordnance Survey (GB), Using: EDINA Digimap Ordnance Survey Service, , Downloaded: 2017-10-24 15:07:52.57 Collins, R. and Symonds, M. (eds.) Hadrian’s Wall 2009-2019: A Summary of Excavation and Research prepared for The Fourteenth Pilgrimage of Hadrian’s Wall, 20-28 July 2019. Cumberland and Westmorland Antiquarian and Archaeological Society and the Society of Antiquaries of Newcastle upon Tyne, Kendal, 19-26. Historic England, 2014. An Archaeological Map of Hadrian’s Wall. Revised Edition. London.
DOI: 10.21965/ijhpr.2019.001
title: Volonté à payer pour un programme de gestion intégrée de l’obésité dans les Groupes de médecine de famille au Québec
DOI: 10.17909/t9-69wa-4q90
title: HST Program GO 14634

description: PI D. Grodent FUV HST observations of Jupiter and it's satellites during the Juno mission
DOI: 10.17632/4535zwnrt5.1
title: Data for: Genotype-by-environment interaction analysis of nutritional composition in newly-developed sweetpotato clones

description: The data set comprises of 9 nutritional traits among newly-developed clones across six environments in Ethiopia.
DOI: 10.21227/pw5y-8j37
title: PrivacyNet results: obfuscated face images

description: In recent years, the utilization of biometric information has become more and more common for various forms of identity verification and user authentication. However, as a consequence of the widespread use and storage of biometric information, concerns regarding sensitive information leakage and the protection of users' privacy have been raised. Recent research efforts targeted these concerns by proposing the Semi-Adversarial Networks (SAN) framework for imparting gender privacy to face images. The objective of SAN is to perturb face image data such that it cannot be reliably used by a gender classifier but can still be used by a face matcher for matching purposes. In this work, we propose a novel Generative Adversarial Networks-based SAN model, PrivacyNet, that is capable of imparting selective soft biometric privacy to {\em multiple} soft-biometric attributes such as gender, age, and race. While PrivacyNet is capable of perturbing different sources of soft biometric information reliably and simultaneously, it also allows users to choose to obfuscate specific attributes, while preserving others. The results from extensive experiments on five independent face image databases demonstrate the efficacy of our proposed model in imparting selective multi-attribute privacy to face images.
DOI: 10.21227/qgs2-np56
title: PrivacyNet results: obfuscated face images

description: In recent years, the utilization of biometric information has become more and more common for various forms of identity verification and user authentication. However, as a consequence of the widespread use and storage of biometric information, concerns regarding sensitive information leakage and the protection of users' privacy have been raised. Recent research efforts targeted these concerns by proposing the Semi-Adversarial Networks (SAN) framework for imparting gender privacy to face images. The objective of SAN is to perturb face image data such that it cannot be reliably used by a gender classifier but can still be used by a face matcher for matching purposes. In this work, we propose a novel Generative Adversarial Networks-based SAN model, PrivacyNet, that is capable of imparting selective soft biometric privacy to {\em multiple} soft-biometric attributes such as gender, age, and race. While PrivacyNet is capable of perturbing different sources of soft biometric information reliably and simultaneously, it also allows users to choose to obfuscate specific attributes, while preserving others. The results from extensive experiments on five independent face image databases demonstrate the efficacy of our proposed model in imparting selective multi-attribute privacy to face images.
DOI: 10.5281/zenodo.3667685
title: Mooring observations

description: Mooring observations. Please see "Determination of tempo-spatial variability of the Indian Equatorial Intermediate Current" for details.
DOI: 10.5281/zenodo.3667686
title: Mooring observations

description: Mooring observations. Please see "Determination of tempo-spatial variability of the Indian Equatorial Intermediate Current" for details.
DOI: 10.21965/ijhpr.2020.e
title: Editorial: The obsession with precision in economic evaluations: have we gone too far?
DOI: 10.5519/qd.esbkfrag
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (744356 records)
DOI: 10.25386/genetics.11825673
title: Supplemental Material for Tsukamoto et al., 2020

description: Supplemental Tables, Supplemental Figures, Supplemental Figure Legends, Supplemental References, and Supplemental Files
DOI: 10.25386/genetics.11825673.v1
title: Supplemental Material for Tsukamoto et al., 2020

description: Supplemental Tables, Supplemental Figures, Supplemental Figure Legends, Supplemental References, and Supplemental Files
DOI: 10.34740/kaggle/dsv/1385074
title: COVID-19 cases by Continent
DOI: 10.25573/data.11856117
title: Hall et al data used in data from Boeschoten et al_Framework species approach .xlsx

description: Reforestation data set used in analysis for Journal of Sustainable Forestry manuscript with lead author Laura Boeschoten. Mature forest data containe in independent file.
DOI: 10.21227/jemc-n760
title: H2Arm - BSP vs PID experiments

description: Many different methods have been proposed in the control and robotics literature for the control of robotics arms. Our main aim is to provide a complete software and hardware platform which allow the statistical replication of our results and the experimentation of other more or less sophisticated control strategies and algorithms.In our work, in order to validate our reproducible research platform and provide a template methodology for its usage, we have thoroughly compared in a reproducible way the performance of simple BSP and PID controls when applied to a light-weight, low accuracy and compliant open source robot arm (H2Arm). BSP significantly outperforms PID on this platform, but not w.r.t. to all metrics.The findings are interesting by themselves. They also show how easily statistically weak results can lead to qualitatively wrong conclusions, if you cherry-pick results.The present dataset contains: i) CAD design to 3D-print the H2Arm; ii) experimental data of comparison BSP vs PID; iii) all the code used to perform the experiments (both BSP and PID).
DOI: 10.5281/zenodo.3667835
title: jtsiva/BLE_Data: Initial basic data set

description: Includes an older data set collected with Moto G5 Plus devices running Android 7.0. These tests evaluated BLE scanning and advertising behavior. The new data set concerns connection-oriented BLE communication and includes arrival times (jitter), operation latency, start up time, and total payload transfer latency for a collection of 9 Android devices: Moto G5 Plus with Android 7.0 Moto G5 Plus with Android 8.1 Moto G5 Plus with Lineage OS 15.1 (Android 8.1) Samsung J320V with Android 7.1.1 Samsung Galaxy A50 with Android 9.0 Samsung Galaxy J3 with Android 9.0 Samsung Galaxy S8 with Android 9.0 Google Pixel 2 with Android 10 Raspberry Pi Model 3 B with Android Things Data was collected using the Android applications found here The data is incomplete in that the phone vendor, bluetooth vendor and version are not included. This information will be included in a future release.
DOI: 10.5281/zenodo.3668100
title: Supplementary Data to "Disparate regulation of Smad3 phosphorylation and collagen gene transcription by full-length IL-33"

description: These are Supplementary Figures for the article "Disparate regulation of Smad3 phosphorylation and collagen transcription by full-length IL-33"
DOI: 10.5281/zenodo.3668101
title: Supplementary Data to "Disparate regulation of Smad3 phosphorylation and collagen gene transcription by full-length IL-33"

description: These are Supplementary Figures for the article "Disparate regulation of Smad3 phosphorylation and collagen transcription by full-length IL-33"
DOI: 10.25387/g3.11575059
title: Supplemental Material for Schmidt et al., 2020

description: Supplementary Information and Data<br>
DOI: 10.25387/g3.11575059.v1
title: Supplemental Material for Schmidt et al., 2020

description: Supplementary Information and Data<br>
DOI: 10.15468/5vwszl
title: New species of Leporinus (Characiformes: Anostomidae) from the highlands of the Guiana Shield in Venezuela

description: This dataset contains the digitized treatments in Plazi based on the original journal article Birindelli, José L. O., Britski, Heraldo A., Provenzano, Francisco (2019): New species of Leporinus (Characiformes: Anostomidae) from the highlands of the Guiana Shield in Venezuela. Neotropical Ichthyology 17 (2): 1-6, DOI: 10.1590/1982-0224-20190022
DOI: 10.25573/data.11856180
title: Hall et al data used in data from Boeschoten et al_Framework species approach_2 of 2

description: Mature forest data set used in Journal of Sustainable Forestry manuscript by Boeschoten et al. Reforestation data set uploaded separately.
DOI: 10.17632/n4zwn47zw8.1
title: Data for: Do Impact Assessments Influence Transport Plans? The Case of Sweden

description: Correlation matrices
DOI: 10.21227/8s2x-7k03
title: Experimental Data for Metacognitive Skills

description: The increasing student’s enrollment, the cost of education and the need to make learning resources accessible any time at anywhere are propelling educational institutions to develop a pedagogical framework to support in-class activities with online learning courses. Moreover, the increasing deployment of skilled-based courses on online learning environment occasioned by the advancement of smart and wireless technology both in formal and informal paradigm means that there is a need to develop effective ways to support online learning paradigm.The transformation system brought by advancement in smart technologies can provide a learning environment that can support online learning process. The self-regulated learning process has been identified as one of the effective ways of supporting online process. The smart learning environment can be designed and developed to support self-regulated learning process. The metacognitive skills such as goal setting, time management, helpseeking, task strategy and self-evaluation can be developed in smart learning environment to provide skills enhancement tosupport online learning process. Earlier, we developed artificial neural network (ANN)-based learning agent to support the development of smart learning environment support online learning.However, to test our approach, there is a lack of dataset to implement the applicability of our methodology. In this guiding note, we present the process of generating the dataset, the trainingand testing process how, and the trained weights of the dataset can be used for predicting students’ learning style that can trigger recommendations based on the behaviour of the student inonline learning environment. This dataset is randomly generated simulating the response of students; there are possibilities that using the dataset in similar purposes in future work could resultin different outcomes and this we consider a limitation of the dataset.
DOI: 10.21227/gqy9-ge13
title: Experimental Data for Metacognitive Skills

description: The increasing student’s enrollment, the cost of education and the need to make learning resources accessible any time at anywhere are propelling educational institutions to develop a pedagogical framework to support in-class activities with online learning courses. Moreover, the increasing deployment of skilled-based courses on online learning environment occasioned by the advancement of smart and wireless technology both in formal and informal paradigm means that there is a need to develop effective ways to support online learning paradigm.The transformation system brought by advancement in smart technologies can provide a learning environment that can support online learning process. The self-regulated learning process has been identified as one of the effective ways of supporting online process. The smart learning environment can be designed and developed to support self-regulated learning process. The metacognitive skills such as goal setting, time management, helpseeking, task strategy and self-evaluation can be developed in smart learning environment to provide skills enhancement tosupport online learning process. Earlier, we developed artificial neural network (ANN)-based learning agent to support the development of smart learning environment support online learning.However, to test our approach, there is a lack of dataset to implement the applicability of our methodology. In this guiding note, we present the process of generating the dataset, the trainingand testing process how, and the trained weights of the dataset can be used for predicting students’ learning style that can trigger recommendations based on the behaviour of the student inonline learning environment. This dataset is randomly generated simulating the response of students; there are possibilities that using the dataset in similar purposes in future work could resultin different outcomes and this we consider a limitation of the dataset.
DOI: 10.5519/qd.qydksnkv
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (120218 records)
DOI: 10.5519/qd.59jr9rve
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (2854 records)