cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3060945
title: Supplementary material for the Systematic Literature Review (SLR) on software-testing education

description: Supplementary material for the Systematic Literature Review (SLR) on software-testing education
DOI: 10.7910/dvn/psksoe/kgxz4y
title: train_lmdb_beauty_augmented_smart.tar.gz

description: Training dataset of streetview images, serialized in lmdb format
DOI: 10.7910/dvn/psksoe/ueug5l
title: validation_lmdb_beauty_augmented_smart.tar.gz

description: Streetview validation set serialized in lmdb format
DOI: 10.14469/hpc/5662
title: Characterisation data for compound 5c

description: Characterisation data for compound 5c
DOI: 10.14469/hpc/5658
title: Characterisation data for compound 4h

description: Characterisation data for compound 4h
DOI: 10.7910/dvn/atxytp/pqsuu5
title: caffenet_deploy_1.prototxt

description: Model_definition_Caffe
DOI: 10.14469/hpc/5668
title: NMR characterisation data for compound 9c

description: NMR characterisation data for compound 9c
DOI: 10.7910/dvn/atxytp/fdppn2
title: caffe_model_beauty_city_smartAugment_fixed_iter_20000.caffemodel

description: Model_weights_at_peak_performance
DOI: 10.7910/dvn/atxytp/lq7yxz
title: citySmart1.binaryproto

description: Mean_image_generated_from_augmented_dataset
DOI: 10.14469/hpc/5663
title: Characterisation data for compound 5d

description: Characterisation data for compound 5d
DOI: 10.14469/hpc/5659
title: Characterisation data for compound 4i

description: Characterisation data for compound 4i
DOI: 10.5286/isis.e.rb1790346-6
title: Sine 2020 Data Collection
DOI: 10.14469/hpc/5664
title: Characterisation data for compound 5e

description: Characterisation data for compound 5e
DOI: 10.18112/openneuro.ds001894.v1.1.0
title: Longitudinal Brain Correlates of Multisensory Lexical Processing in Children
DOI: 10.14469/hpc/5665
title: Characterisation data for compound 5f

description: Characterisation data for compound 5f
DOI: 10.7910/dvn/bnjecp/hqqxq5
title: generator.prototxt

description: Generator description file for Caffe
DOI: 10.7910/dvn/bnjecp/twrbdh
title: generator_81000.caffemodel

description: Generator Model Weights
DOI: 10.5281/zenodo.3065488
title: Data for: Extensive coral mortality and critical habitat loss following dredging and their association with remotely-sensed sediment plumes

description: This work describes impacts to coral reefs surrounding the 2013-2015 dredging of the Port of Miami based on data collected before, during, and after dredging by Dial Cordy and Associates (DCA) on behalf of Great Lakes Dredge and Dock Company, the dredging contractors for the U.S. Army Corps of Engineers (USACE) and for the Port of Miami (Miami-Dade County). A front page for this repository can be accessed at jrcunning.github.io/pom-dredge containing rendered R Markdown detailing all analyses conducted as part of this work.
DOI: 10.6084/m9.figshare.13090214
title: Betaherpesvirus PCR, rabies virus RT-PCR and rabies virus neutralising antibody test results for Peruvian bats

description: This data record consists of a single dataset: <b>BHV_VBRV_testing_all_samples.xlsx</b>.The dataset includes Betaherpesvirus polymerase chain reaction (PCR), Rabies virus reverse transcription (RT)-PCR and Rabies virus neutralising antibody test results for Peruvian bats (predominantly <i>Desmodus rotundus</i>). The PCR samples (saliva, fecal and blood samples), were collected between 2015 and 2018.The Metadata tab provides descriptions of all the variables in the “BHV_PCR_VBRV_Set” tab.<br><b>Ethical approval</b>: All capture and sampling of bats was approved by the Research Ethics Committee of the University of Glasgow School of Medical, Veterinary and Life Sciences (Ref081/15) and by the University of Georgia Animal Care and Use Committee (A2014 04-016-Y3-A5). Field collections were authorized by the Peruvian government (RD‐009‐2015‐SERFOR‐DGGSPFFS, RD‐264‐2015 SERFOR‐DGGSPFFS, RD‐142‐2015‐ SERFOR‐DGGSPFFS, RD‐054‐2016‐SERFOR‐DGGSPFFS).<br><b>Study aims and methodology</b>: Rabies is a viral zoonosis transmitted by vampire bats across Latin America. Substantial public health and agricultural burdens remain, despite decades of bats culls and livestock vaccinations. Virally vectored vaccines that spread autonomously through bat populations are a theoretically appealing solution to managing rabies in its reservoir host. In this study, the authors investigated the biological and epidemiological suitability of newly discovered betaherpesvirus (DrBHV) to act as a vaccine vector. Specifically, the three main objectives of the study were: (1) to quantify BHV prevalence across populations and demographic groups of vampire bats in Peru, in comparison to that of VBRV, (2) to use phylogenetic analyses of DrBHV and other newly described bat BHVs to investigate the host specificity of DrBHV among sympatric bat species, and (3) to use deep sequencing to evaluate the potential for DrBHV super-infections in individual bats and obtain a whole genome sequence to evaluate the structural similarity of DrBHV to already developed BHV vectors.The following procedures are described in more detail in the published article: sample collection, nucleic acid extraction, amplification and sequencing of betaherpesviruses, molecular confirmation of bat species identities, amplification and sequencing of vampire bat rabies virus, detection of rabies virus neutralising antibodies, statistical analysis, phylogenetic inference, whole genome sequencing and bioinformatics analysis.<br>
DOI: 10.6084/m9.figshare.13090214.v1
title: Betaherpesvirus PCR, rabies virus RT-PCR and rabies virus neutralising antibody test results for Peruvian bats

description: This data record consists of a single dataset: <b>BHV_VBRV_testing_all_samples.xlsx</b>.The dataset includes Betaherpesvirus polymerase chain reaction (PCR), Rabies virus reverse transcription (RT)-PCR and Rabies virus neutralising antibody test results for Peruvian bats (predominantly <i>Desmodus rotundus</i>). The PCR samples (saliva, fecal and blood samples), were collected between 2015 and 2018.The Metadata tab provides descriptions of all the variables in the “BHV_PCR_VBRV_Set” tab.<br><b>Ethical approval</b>: All capture and sampling of bats was approved by the Research Ethics Committee of the University of Glasgow School of Medical, Veterinary and Life Sciences (Ref081/15) and by the University of Georgia Animal Care and Use Committee (A2014 04-016-Y3-A5). Field collections were authorized by the Peruvian government (RD‐009‐2015‐SERFOR‐DGGSPFFS, RD‐264‐2015 SERFOR‐DGGSPFFS, RD‐142‐2015‐ SERFOR‐DGGSPFFS, RD‐054‐2016‐SERFOR‐DGGSPFFS).<br><b>Study aims and methodology</b>: Rabies is a viral zoonosis transmitted by vampire bats across Latin America. Substantial public health and agricultural burdens remain, despite decades of bats culls and livestock vaccinations. Virally vectored vaccines that spread autonomously through bat populations are a theoretically appealing solution to managing rabies in its reservoir host. In this study, the authors investigated the biological and epidemiological suitability of newly discovered betaherpesvirus (DrBHV) to act as a vaccine vector. Specifically, the three main objectives of the study were: (1) to quantify BHV prevalence across populations and demographic groups of vampire bats in Peru, in comparison to that of VBRV, (2) to use phylogenetic analyses of DrBHV and other newly described bat BHVs to investigate the host specificity of DrBHV among sympatric bat species, and (3) to use deep sequencing to evaluate the potential for DrBHV super-infections in individual bats and obtain a whole genome sequence to evaluate the structural similarity of DrBHV to already developed BHV vectors.The following procedures are described in more detail in the published article: sample collection, nucleic acid extraction, amplification and sequencing of betaherpesviruses, molecular confirmation of bat species identities, amplification and sequencing of vampire bat rabies virus, detection of rabies virus neutralising antibodies, statistical analysis, phylogenetic inference, whole genome sequencing and bioinformatics analysis.<br>
DOI: 10.5281/zenodo.4246525
title: Greek Smart House Nanogrid Dataset

description: The dataset refers to measurements and data collected from and for the CERTH Smart House nanogrid infrastructure in Thessaloniki, Greece. This infrastructure is a living lab that belongs to the Centre for Research and Technology-Hellas (www.certh.gr) and has been designed, deployed and operated by the Information Technology Institute (www.iti.gr). Mainly energy-related aspects are included in the datasets uploaded which are in the form of .csv files, covering Electricity Energy Consumption, Generation, and Storage, as well as Weather and Electricity Price information from external APIs (a local weather station has just been installed and will be included in future versions).
description: The data are in their raw format as extracted from the physical installation including missing values, outliers, etc. The initial building infrastructure has been funded by Greece and the European Regional Development Fund of the European Union (NSRF 2007-2013) through the GSRT/KRIPIS action, whereas from that point onwards the infrastructure has been also co-funded by the Greek General Secreteriat of Research and Technology through the 3DMicroGrid project of the ERANETMED initiative (identification: ERANETMED-energy-11-286).
DOI: 10.5281/zenodo.4246524
title: Greek Smart House Nanogrid Dataset

description: The dataset refers to measurements and data collected from and for the CERTH Smart House nanogrid infrastructure in Thessaloniki, Greece. This infrastructure is a living lab that belongs to the Centre for Research and Technology-Hellas (www.certh.gr) and has been designed, deployed and operated by the Information Technology Institute (www.iti.gr). Mainly energy-related aspects are included in the datasets uploaded which are in the form of .csv files, covering Electricity Energy Consumption, Generation, and Storage, as well as Weather and Electricity Price information from external APIs (a local weather station has just been installed and will be included in future versions).
description: The data are in their raw format as extracted from the physical installation including missing values, outliers, etc. The initial building infrastructure has been funded by Greece and the European Regional Development Fund of the European Union (NSRF 2007-2013) through the GSRT/KRIPIS action, whereas from that point onwards the infrastructure has been also co-funded by the Greek General Secreteriat of Research and Technology through the 3DMicroGrid project of the ERANETMED initiative (identification: ERANETMED-energy-11-286).
DOI: 10.6084/m9.figshare.12174864
title: Atopic dermatitis MendelVar example

description: MendelVar annotation results for sub-significant loci Paternoster et al. (2015) atopic dermatitis GWAS. Detailed description of the output files in the "Results" section of MendelVar tutorial at:https://www.notion.so/mendelvar/MendelVar-tutorial-ab91d2a6acb846f2b9f2978fcd942dd5<br><br>
DOI: 10.6084/m9.figshare.13154504.v1
title: East Asian HDL GWAS MendelVar example

description: MendelVar annotation results for Kanai et al. (2019) HDL GWAS.Detailed description of the output files in the "Results" section of MendelVar tutorial at:https://www.notion.so/mendelvar/MendelVar-tutorial-ab91d2a6acb846f2b9f2978fcd942dd5<br>
DOI: 10.5517/ccdc.csd.cc23630g
title: CCDC 1942274: Experimental Crystal Structure Determination

description: Related Article: Anton  V. Lukashenko, Vitaly A. Osyanin, Dmitry V. Osipov, Yuri   N. Klimochkin, Victor B. Rybakov|2019|CSD Communication|||
description: Related Article: A.V.Lukashenko, D.V.Osipov, V.A.Osyanin, Yu.N.Klimochkin|2020|Chemistry of Heterocyclic Compounds|56|529|doi:10.1007/s10593-020-02695-4
DOI: 10.5517/ccdc.csd.cc236gh9
title: CCDC 1942630: Experimental Crystal Structure Determination

description: Related Article: Anton  V. Lukashenko, Vitaly A. Osyanin, Dmitry V. Osipov, Yuri   N. Klimochkin, Victor B. Rybakov|2019|CSD Communication|||
description: Related Article: A.V.Lukashenko, D.V.Osipov, V.A.Osyanin, Yu.N.Klimochkin|2020|Chemistry of Heterocyclic Compounds|56|529|doi:10.1007/s10593-020-02695-4
DOI: 10.5517/ccdc.csd.cc2582bw
title: CCDC 2003757: Experimental Crystal Structure Determination

description: Related Article: Yong Xuefeng, Huang Jian-Qiang, Ho Chun-Yu|2020|Youji Huaxue|40|3327|doi:10.6023/cjoc202005050
DOI: 10.5517/ccdc.csd.cc258p16
title: CCDC 2004337: Experimental Crystal Structure Determination

description: Related Article: Yong Xuefeng, Huang Jian-Qiang, Ho Chun-Yu|2020|Youji Huaxue|40|3327|doi:10.6023/cjoc202005050
description: Related Article: Xuefeng Yong|2020|CSD Communication|||
DOI: 10.6084/m9.figshare.13154561
title: European HDL GWAS MendelVar example

description: MendelVar annotation results for Teslovich et al. (2010) HDL GWAS.Detailed description of the output files in the "Results" section of MendelVar tutorial at:https://www.notion.so/mendelvar/MendelVar-tutorial-ab91d2a6acb846f2b9f2978fcd942dd5<br>
DOI: 10.6084/m9.figshare.13154561.v2
title: European HDL GWAS MendelVar example

description: MendelVar annotation results for Teslovich et al. (2010) HDL GWAS.Detailed description of the output files in the "Results" section of MendelVar tutorial at:https://www.notion.so/mendelvar/MendelVar-tutorial-ab91d2a6acb846f2b9f2978fcd942dd5<br>
DOI: 10.6084/m9.figshare.13154561.v1
title: European HDL GWAS MendelVar example

description: MendelVar annotation results for Teslovich et al. (2010) HDL GWAS.Detailed description of the output files in the "Results" section of MendelVar tutorial at:https://www.notion.so/mendelvar/MendelVar-tutorial-ab91d2a6acb846f2b9f2978fcd942dd5<br>
DOI: 10.5281/zenodo.4246178
title: Top IMDb 250 TV Shows dataset

description: Dataset of the Top 250 IMDb TV Shows and its episodes, ranked by users (collected on Nov. 4 2020).
DOI: 10.5281/zenodo.4246177
title: Top IMDb 250 TV Shows dataset

description: Dataset of the Top 250 IMDb TV Shows and its episodes, ranked by users (collected on Nov. 4 2020).
DOI: 10.21227/1bf3-ja24
title: Model14

description: We have followed the three-step approach to tackle this challenge.Data preparation – We started with the aggregation of the energy consumption data at a daily level (originally given at a half-hourly interval). Missing values were observed in temperature metadata and consumption data, which we imputed using daily level linear interpolation.Next was feature engineering which was essential to bring out the best accuracy in the model.There are mainly three features we have tried to work with:a) Average weather data – used as is (we have used avg. weather data only)b) Time-Series features created using date1. Month (1,2,3,…..12) and2. Weekday (Mon, Tue,……Sun)3. WeekofYear(1,2,…52)c) Cyclic encoding of periodic features for the month and weekday1. Month gets mapped to Month_x = sin(2*pi*month/12) and Month_y=cos(2*pi*month/12)2. Weekday gets mapped to weekday_x = sin(2*pi*weekday/6) and weekday_y=cos(2*pi*weekday /6).Post multiple iterations, we settled with the XgBoost model since it performed the best among all. We trained the model on individual meter_ID and forecasted energy consumption at a daily level. The outcome was then rolled-up, to a monthly level to get our final submission.For the customer having one month of data, we performed fuzzy clustering of time series using December daily consumption data then daily consumption data for the whole year for missing months were imputed using the cluster's average consumption and the probability of meter-id to fall in each cluster and took summation.
DOI: 10.6084/m9.figshare.13193780
title: Bank regulation; a structural equation modelling of productivity change post 2008 crisis.

description: To examine the impact of the recent regulation post-2008 crisis and the progress in regulatory reforms, we utilise the fifth update of the World Bank - Bank Regulation and Supervision Survey (BRSS 2019) (Anginer<i> et al.</i>, 2019) which cover years 2011 to 2016. We complement this analysis by a comparative examination of the previous surveys covering the years 1999 to 2010. Such an extended panel on banks regulation and supervision, along with performance and productivity measures, is not a standard analysis in the literature, and it enables comparison and scenario-frontier analysis that strengthens evidence and outcome. The plausibility of our evidence is further established through implanting a Structural Equation Modelling (SEM and GSEM) method to analyse association and impact of regulation with/on banks productivity over the relative merits of stages and endogeneity. The implementation of SEM and GSEM contributes through several advantages for its competitiveness in handling complex and multi-layered models, and that enables the employment of a series of nested models and sequential chi-square difference tests. <br>
DOI: 10.6084/m9.figshare.13193780.v2
title: Bank regulation; a structural equation modelling of productivity change post 2008 crisis.

description: To examine the impact of the recent regulation post-2008 crisis and the progress in regulatory reforms, we utilise the fifth update of the World Bank - Bank Regulation and Supervision Survey (BRSS 2019) (Anginer<i> et al.</i>, 2019) which cover years 2011 to 2016. We complement this analysis by a comparative examination of the previous surveys covering the years 1999 to 2010. Such an extended panel on banks regulation and supervision, along with performance and productivity measures, is not a standard analysis in the literature, and it enables comparison and scenario-frontier analysis that strengthens evidence and outcome. The plausibility of our evidence is further established through implanting a Structural Equation Modelling (SEM and GSEM) method to analyse association and impact of regulation with/on banks productivity over the relative merits of stages and endogeneity. The implementation of SEM and GSEM contributes through several advantages for its competitiveness in handling complex and multi-layered models, and that enables the employment of a series of nested models and sequential chi-square difference tests. <br>
DOI: 10.6084/m9.figshare.13193780.v1
title: Bank regulation; a structural equation modelling of productivity change post 2008 crisis.

description: To examine the impact of the recent regulation post-2008 crisis and the progress in regulatory reforms, we utilise the fifth update of the World Bank - Bank Regulation and Supervision Survey (BRSS 2019) (Anginer<i> et al.</i>, 2019) which cover years 2011 to 2016. We complement this analysis by a comparative examination of the previous surveys covering the years 1999 to 2010. Such an extended panel on banks regulation and supervision, along with performance and productivity measures, is not a standard analysis in the literature, and it enables comparison and scenario-frontier analysis that strengthens evidence and outcome. The plausibility of our evidence is further established through implanting a Structural Equation Modelling (SEM and GSEM) method to analyse association and impact of regulation with/on banks productivity over the relative merits of stages and endogeneity. The implementation of SEM and GSEM contributes through several advantages for its competitiveness in handling complex and multi-layered models, and that enables the employment of a series of nested models and sequential chi-square difference tests. <br>
DOI: 10.6084/m9.figshare.13154504
title: East Asian HDL GWAS MendelVar example

description: MendelVar annotation results for Kanai et al. (2019) HDL GWAS.Detailed description of the output files in the "Results" section of MendelVar tutorial at:https://www.notion.so/mendelvar/MendelVar-tutorial-ab91d2a6acb846f2b9f2978fcd942dd5<br>
DOI: 10.6084/m9.figshare.13154504.v3
title: East Asian HDL GWAS MendelVar example

description: MendelVar annotation results for Kanai et al. (2019) HDL GWAS.Detailed description of the output files in the "Results" section of MendelVar tutorial at:https://www.notion.so/mendelvar/MendelVar-tutorial-ab91d2a6acb846f2b9f2978fcd942dd5<br>
DOI: 10.6084/m9.figshare.13154504.v2
title: East Asian HDL GWAS MendelVar example

description: MendelVar annotation results for Kanai et al. (2019) HDL GWAS.Detailed description of the output files in the "Results" section of MendelVar tutorial at:https://www.notion.so/mendelvar/MendelVar-tutorial-ab91d2a6acb846f2b9f2978fcd942dd5<br>
DOI: 10.6084/m9.figshare.13154525
title: East Asian LDL GWAS MendelVar example

description: MendelVar annotation results for Kanai et al. (2019) LDL GWAS.Detailed description of the output files in the "Results" section of MendelVar tutorial at:https://www.notion.so/mendelvar/MendelVar-tutorial-ab91d2a6acb846f2b9f2978fcd942dd5
DOI: 10.6084/m9.figshare.13154525.v2
title: East Asian LDL GWAS MendelVar example

description: MendelVar annotation results for Kanai et al. (2019) LDL GWAS.Detailed description of the output files in the "Results" section of MendelVar tutorial at:https://www.notion.so/mendelvar/MendelVar-tutorial-ab91d2a6acb846f2b9f2978fcd942dd5
DOI: 10.6084/m9.figshare.13154525.v1
title: East Asian LDL GWAS MendelVar example

description: MendelVar annotation results for Kanai et al. (2019) LDL GWAS.Detailed description of the output files in the "Results" section of MendelVar tutorial at:https://www.notion.so/mendelvar/MendelVar-tutorial-ab91d2a6acb846f2b9f2978fcd942dd5
DOI: 10.21227/s2dw-vt13
title: Dyn mean

description: Dyn mean
DOI: 10.21227/mdp9-n317
title: Dyn median

description: Dyn median
DOI: 10.6084/m9.figshare.13154567
title: European LDL GWAS MendelVar example

description: MendelVar annotation results for Teslovich et al. (2010) LDL GWAS.Detailed description of the output files in the "Results" section of MendelVar tutorial at:https://www.notion.so/mendelvar/MendelVar-tutorial-ab91d2a6acb846f2b9f2978fcd942dd5
DOI: 10.6084/m9.figshare.13154567.v2
title: European LDL GWAS MendelVar example

description: MendelVar annotation results for Teslovich et al. (2010) LDL GWAS.Detailed description of the output files in the "Results" section of MendelVar tutorial at:https://www.notion.so/mendelvar/MendelVar-tutorial-ab91d2a6acb846f2b9f2978fcd942dd5
DOI: 10.6084/m9.figshare.13154567.v1
title: European LDL GWAS MendelVar example

description: MendelVar annotation results for Teslovich et al. (2010) LDL GWAS.Detailed description of the output files in the "Results" section of MendelVar tutorial at:https://www.notion.so/mendelvar/MendelVar-tutorial-ab91d2a6acb846f2b9f2978fcd942dd5