cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5255/ukda-sn-855239
title: Combining Work and Care- How Do Workplace Support and Technologies Contribute to Sustainable Care Arrangements, 2017-2021

description: Family carers, central to sustainable care, are mostly of working age and employed full-time. Their rising numbers include many mobile workers. Incompatibility between family care and paid work is a known risk to sustainable care, but better evidence is needed of the support needed to promote wellbeing among working carers and those they support. This team’s research on Combining Work and Care: How do workplace support and technologies contribute to sustainable care arrangements is designed to inform policy and practice on the planning and resourcing of care by generating new insights into sustainable care and wellbeing through comparison of developments in the UK and other countries. Previous UK research on this topic has mostly focused on flexible working arrangements and organisational case studies. Little is known about how care leave (on which the UK has not legislated) might be introduced, or about the voluntary initiatives already being implemented by employers. This research focused on under-researched aspects of the support needed to sustain the wellbeing of ‘working carers’: measurement of impact; the role and potential of schemes designed to improve workplace support; the impact and characteristics of statutory care leave in other countries and of their voluntary, employer-led, equivalents in the UK. The main research questions were: 1. What support do working carers need to fulfill both their work and caring responsibilities? What are their highest priorities for such support? 2. What constitutes good workplace support for carers in employment? How does it enhance carers’ ability to integrate their paid employment and caring roles? 3. What is the impact of this support on carers, employers, care users and care workers? What are its costs and benefits for employers and for different types of employee? 4. What are the key features of established/emerging carer ‘workplace standard’, ‘employer recognition’ and ‘benchmarking’ schemes; what do they contribute to wellbeing and sustainability of care arrangements? 5. What are the characteristics, impact, uptake and outcomes of UK employers’ voluntary care leave schemes, and of statutory paid care leave schemes in other countries? 6. What aspects of worker and carer roles are amenable to improved co-ordination of care; in what ways and how can technology enhance the quality of care relationships, or produce wellbeing outcomes? 7. Which (if any) available technologies do working carers use, and how (if at all) do they assemble or modify these to provide the support they need? 8. Can technological innovations offer improved support for working carers reduce stress/overload or disconnection/ fragmentation in caring situations?
DOI: 10.5281/zenodo.5667174
title: Long time-series ecological niche modelling using archaeological settlement data.

description: <strong>CR_settlement_niche_[N]_[Yr]_[BC/AD].tif</strong> Ecological niche models in GeoTIFF format generated with the MaxEnt software based using prehistoric settlement evidence as training data and environmental layers (elevation, mean annual precipitation, mean annual temperature, landscape water balance, soil types) as background data. Raster values represent the probability of presence of a settlement.<br> <strong>N</strong> - chronological ordering<br> <strong>Yr, BC/AD</strong> - calendar years BC or AD <strong>CR_settlement_niche_combined.tif</strong> All models combined by averaging. <strong>CR_settlement_archeo.zip</strong> Archaeological data used to train the MaxEnt models in ESRI SHP format with the following fields: <strong>Site_Type:</strong> Cemetery or Settlement <strong>Archeo_Dat:</strong> Archaeological dating (culture or period) <strong>Source:</strong> Source dataset (AMCR or LONGWOOD) AMCR: Archeologická mapa České republiky – Archaeological Map of the Czech Republic. Retrieved from https://digiarchiv.aiscr.cz/. LONGWOOD: Kolář, J., Tkáč, P., Macek, M., & Szabó, P. (2016). Archaeology and Historical Ecology: the Archaeological Database of the LONGWOOD ERC Project. Archäologisches Korrespondenzblatt 46/4, 539-554. <strong>Yrs_BP_Avg:</strong> Average dating in calendar years BP (based on the archaeological dating) <strong>Yrs_BP_Unc:</strong> Temporal uncertainty of the dating (half of the culture or period's duration) <strong>Loc_Accur:</strong> Spatial accuracy derived from the recorded degree of the accuracy of location (radius in meters around the center point)
DOI: 10.5281/zenodo.5667173
title: Long time-series ecological niche modelling using archaeological settlement data.

description: <strong>CR_settlement_niche_[N]_[Yr]_[BC/AD].tif</strong> Ecological niche models in GeoTIFF format generated with the MaxEnt software based using prehistoric settlement evidence as training data and environmental layers (elevation, mean annual precipitation, mean annual temperature, landscape water balance, soil types) as background data. Raster values represent the probability of presence of a settlement.<br> <strong>N</strong> - chronological ordering<br> <strong>Yr, BC/AD</strong> - calendar years BC or AD <strong>CR_settlement_niche_combined.tif</strong> All models combined by averaging. <strong>CR_settlement_archeo.zip</strong> Archaeological data used to train the MaxEnt models in ESRI SHP format with the following fields: <strong>Site_Type:</strong> Cemetery or Settlement <strong>Archeo_Dat:</strong> Archaeological dating (culture or period) <strong>Source:</strong> Source dataset (AMCR or LONGWOOD) AMCR: Archeologická mapa České republiky – Archaeological Map of the Czech Republic. Retrieved from https://digiarchiv.aiscr.cz/. LONGWOOD: Kolář, J., Tkáč, P., Macek, M., & Szabó, P. (2016). Archaeology and Historical Ecology: the Archaeological Database of the LONGWOOD ERC Project. Archäologisches Korrespondenzblatt 46/4, 539-554. <strong>Yrs_BP_Avg:</strong> Average dating in calendar years BP (based on the archaeological dating) <strong>Yrs_BP_Unc:</strong> Temporal uncertainty of the dating (half of the culture or period's duration) <strong>Loc_Accur:</strong> Spatial accuracy derived from the recorded degree of the accuracy of location (radius in meters around the center point)
DOI: 10.34740/kaggle/dsv/2795821
title: 2021 World Population (updated daily)
DOI: 10.15468/u4ae2a
title: Taxonomic and nomenclatural notes on Philippine ferns. IV. Asplenium pahli (Polypodiales, Aspleniineae, Aspleniaceae), a new species from Leyte, Philippines

description: This dataset contains the digitized treatments in Plazi based on the original journal article Salgado, Arthur Edward (2021): Taxonomic and nomenclatural notes on Philippine ferns. IV. Asplenium pahli (Polypodiales, Aspleniineae, Aspleniaceae), a new species from Leyte, Philippines. Phytotaxa 524 (2): 71-84, DOI: 10.11646/phytotaxa.524.2.1
DOI: 10.25721/qw2t-qp47
title: 27_54 - RCP Châtillonnais; enquête ethnomusicologique. Dactylogramme des transcriptions de la collection « 67-28; RCP Chât (CMD-MA-JR) »
DOI: 10.25721/rbe1-k269
title: 27_49 - RCP Châtillonnais; enquête ethnomusicologique. Carnet de description des enregistremets; « Rcp Châtillonnais 3e campagne B Ethnomusicologie CMD-MA 1-5 juin 1967 »
DOI: 10.25721/pfyv-zc14
title: 27_20 - Carnet de terrain CMD : « III e campagne B CMD »
DOI: 10.25721/rnyv-4s46
title: 27_14 - « CNRS-ATP; RCP-Chat – Ethnomus – IIIe campagne B; Journal de route JR; juin 67 (« Instruments d'écorce »)
DOI: 10.25721/vq2t-p614
title: 27_13 - « CNRS-ATP; RCP-Chat – Ethnomus – IIIe campagne; Journal de route JR; avril-mai 1967 (« Musique »)
DOI: 10.25721/8am4-5b79
title: 27_12 - « CNRS-ATP; RCP-Chat – Ethnomus – IIIe campagne; Journal de route JR; avril-mai 1967 (« Mais »)
DOI: 10.5519/qd.cepyqoa7
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (133 records)
DOI: 10.25721/wnbe-s082
title: 27_56 - RCP Châtillonnais; enquête ethnomusicologique. Dactylogramme des transcriptions de la collection « 67-47; RCP Chât (CMD-MA-JR) »; fin
DOI: 10.25721/288b-br10
title: 27_55 - RCP Châtillonnais; enquête ethnomusicologique. Dactylogramme des transcriptions de la collection « 67-47; RCP Chât (CMD-MA-JR) »; début
DOI: 10.25721/11jd-4j66
title: 27_50 - RCP Châtillonnais; enquête ethnomusicologique. Carnet de description des enregistremets; « Rcp Châtillonnais IVe campagne (14 juillet) CMD-MA »
DOI: 10.25721/hf2r-7p68
title: 27_26 - Carnet de terrain MPA : « RCP Châtillonnais; 4e campagne; M.P.A.; 11-16 juillet 1967 »
DOI: 10.25721/2hgq-w171
title: 27_25 - Carnet de terrain MPA : « RCP Châtillonnais; 4e campagne; M.P.A.; 11-16 juillet 1967 »
DOI: 10.25721/fmw8-dd18
title: 27_21 - Carnet de terrain CMD : « RCP Châtillonnais; 4e campagne; 1-16.07.67; CMD »
DOI: 10.25721/krdg-xh02
title: 27_15 - « CNRS-ATP; RCP-Chat – Ethnomus – IVe campagne; Journal de route JR; juillet 67 (« 14 juillet »)
DOI: 10.25721/cr04-fb42
title: 27_57 - RCP Châtillonnais; enquête ethnomusicologique. Dactylogramme des transcriptions de la collection « 67-70; Châtillonnais (original et doubles relus et corrigés) »
DOI: 10.25721/3tyc-2p36
title: 27_51 - RCP Châtillonnais; enquête ethnomusicologique. Carnet de description des enregistremets; « Rcp Châtillonnais Ve campagne Ethnomusicologie CMD-MA »
DOI: 10.25721/j8rk-n144
title: 27_22 - Carnet de terrain CMD : « Rcp Châtillonnais; 5e campagne (nov. 67); CMD »
DOI: 10.25721/15xn-zz56
title: 27_16 - « CNRS-ATP; RCP-Chat – Ethnomus – V; Journal de route JR; nov-déc 67 (« musiciens; 11 novembre; Noël »)
DOI: 10.6084/m9.figshare.16945276.v1
title: IMD Radar Network

description: IMD Radar Network data with lat/lon and range
DOI: 10.25721/2ztt-9y58
title: 31.2_01 - Enquête : carnet de terrain
DOI: 10.25721/vj15-f068
title: 31.2_02 - Enquête; inventaire des enregistrements : « Mission CNRS-ATP Ethnomusicologie Antilles II Ste Lucie; Haïti; juillet 1976 »
DOI: 10.25721/gyvh-7r39
title: 31.2_03 - Enquête : nostes ms de terrain
DOI: 10.5281/zenodo.5667384
title: Accipiter melanoleucus SPAdes preassembly

description: .
DOI: 10.5281/zenodo.5667383
title: Accipiter melanoleucus SPAdes preassembly

description: .
DOI: 10.6084/m9.figshare.14544645
title: Disappearance of coarse woody debris, following a land-clearing event

description: 'preProcessed.Rdata' holds an R object of the data used to assess the disappearance of coarse woody debris.R script 'analysis.R' analyses 'preProcessed.Rdata'. R script 'functions.R' contains functions called by 'analysis.R'. Image 'medianAnnualRain_mm.tif' holds rainfall information for Queensland.
DOI: 10.6084/m9.figshare.14544645.v1
title: Disappearance of coarse woody debris, following a land-clearing event

description: 'preProcessed.Rdata' holds an R object of the data used to assess the disappearance of coarse woody debris.R script 'analysis.R' analyses 'preProcessed.Rdata'. R script 'functions.R' contains functions called by 'analysis.R'.
DOI: 10.25721/vmpf-yx56
title: 31.3_01 - Organisation : correspondance; 1976 et 1978-1981
DOI: 10.25721/4rms-g907
title: 31.3_02 - Organisation : programmation; notes ms; 1978
DOI: 10.25721/01m0-af58
title: 31.3_03 - Contributions de G. Collomb : rapport de mission à Marie-Galante (juin 1978) et intervention au colloque de la SEF
DOI: 10.6084/m9.figshare.14967357
title: Table S1.xlsx

description: Primer sequences of genes RTqPCR and analysis results of the NormFinder program.<br>
DOI: 10.25721/f86g-5x22
title: 31.3_04 - Enquête : notes ms de terrain (journal) et notations musicales; nov-déc. 1978
DOI: 10.6084/m9.figshare.14967357.v4
title: Table S1.xlsx

description: Primer sequences of genes RTqPCR and analysis results of the NormFinder program.<br>
DOI: 10.25721/k78n-xb22
title: 31.3_05 - Enquête : relevés des phonogrammes enregistrés à Marie-Galante (nov. 1978)
DOI: 10.21227/e2yd-k649
title: Crime Video Dataset for Indian Scenarios

description: The crime rate is increasing at a high rate in India. Terrorist attacks like Mumbai 26/11, Pulwama attack, Pune German Beckary attacks have created terrific fear amongst Indian Society. Video analytics plays a significant role in detecting and predicting such suspicioushuman activities using deep learning models It will help in reducing the increasing crime rate by preventing treacherous actions.Video analyticsanalyzes the video content and adds brains to eyes i.e. analytics to the camera. It extracts contents from the video by monitoring the video in real-time. There is a huge advancement in human action recognitions and prediction-related research but still, state of art algorithms needs improvement in classifying it. The main objective here is to create a dataset for Indian scenarios for different crime actions like Fighting, Abuse, Arrest, Arson Burglary. It can then be used to train a deep learning model to detect and predict criminal actions reducing terrorism in India.
DOI: 10.6084/m9.figshare.12656165.v30
title: COVID-19 Case Reports in China

description: Chinese prefectural level governments started to report daily confirmed COVID-19 cases online, starting from January 2020. The disclosures may contain the mobility, potential exposure scenario, epidemiological characteristics, and other useful information of individual cases. We organized a group of content coders since early March 2020, kept monitoring the information updates, manually extracted useful information from the public disclosures, and compiled these datasets.We welcome any form of collaborations with us and non-commercial reuse of our dataset. We highly encourage interested parties to examine the data, report errors in our coding, and help us to keep the data updated.The detailed data description can be found on SSRN preprint server https://dx.doi.org/10.2139/ssrn.3705815.
DOI: 10.25377/sussex.14345732
title: Research data for paper: Racist way of victim blaming in the aftermath of the Grenfell Tower fire

description: This dataset for a forthcoming publication involves racist tweets against Grenfell Tower fire victims. We aimed to understand how tweeters used racist language to delegitimise the victims and their supporters.<br>Data was collected in January 2021, using the Advanced Search option within Twitter and by downloading tweets using tweepy.org, developer.twitter.com, and kaggle.com. The data was processed using Pandas Package (pandas.pydata.org).<br><br>The dataset consists of four files of tweet ID numbers of racist Twitter posts against Grenfell Tower victims, survivors and bereaved families. In total we collected 26,653 tweets that involved #Grenfell #GrenfellTower #GrenfellTowerfire hashtags in four different time periods (see below for more information), and separated the ones that used hostile and racist language against Grenfell Tower fire victims and bereaved families. Tweets were also analysed which were in themselves not using racist language, but where replies included racist language.This dataset represents 267 of the 416 tweets that were analysed, in the following documents:<br><br>1. ‘2017_Tweets_Grenfelll Public Inquiry began’ represents data that were collected between September 7 and 21 in 2017. This centred on the formal opening of the Grenfell Tower Inquiry on 14th September 2017. We collected 93 racist tweets out of 6,348 Grenfell Tower fire-related tweets from this period. <br><br>2. ‘2018_Tweets_Petition’ represents tweets from the second data collection time period that was during the time of the petition that Grenfell supporters created to demand a debate in Parliament to include survivors and bereaved families in the inquiry process. The petition began on 14th May 2018 and it ended on 30th May 2018. We collected 112 racist tweets out of 7,578 Grenfell Tower fire-related tweets between those dates. <br><br>3. 2019_Tweets_Inquiry Report’ has tweets that were collected between 23rd October and 7th November in 2019. This included the date of the first inquiry report on 30th October. We collected 47 racist tweets out of 11,718 Grenfell Tower fire-related tweets during this period.<br><br>4. ‘2020_Tweets_COVID cancelled SW’ involves the last data collection time period that happened between 7th and 31st March 2020. This included the first Silent Walk that was cancelled because of the COVID-19 outbreak. We collected 33 (13 tweets with hashtags) racist tweets out of 1,010 Grenfell Tower fire-related tweets.<br>The Readme file includes further details.<br>The files include TweetIDs as captured in January 2021. These can be rehydrated using resources such as Twarc (https://github.com/DocNow/twarc) or Hydrator (https://github.com/DocNow/hydrator) in order to retrieve the Tweets as they currently appear on Twitter. Tweets which have been deleted since data capture will not be retrieved.<br>
DOI: 10.25377/sussex.14345732.v1
title: Research data for paper: Racist way of victim blaming in the aftermath of the Grenfell Tower fire

description: This dataset for a forthcoming publication involves racist tweets against Grenfell Tower fire victims. We aimed to understand how tweeters used racist language to delegitimise the victims and their supporters.<br>Data was collected in January 2021, using the Advanced Search option within Twitter and by downloading tweets using tweepy.org, developer.twitter.com, and kaggle.com. The data was processed using Pandas Package (pandas.pydata.org).<br><br>The dataset consists of four files of tweet ID numbers of racist Twitter posts against Grenfell Tower victims, survivors and bereaved families. In total we collected 26,653 tweets that involved #Grenfell #GrenfellTower #GrenfellTowerfire hashtags in four different time periods (see below for more information), and separated the ones that used hostile and racist language against Grenfell Tower fire victims and bereaved families. Tweets were also analysed which were in themselves not using racist language, but where replies included racist language.This dataset represents 267 of the 416 tweets that were analysed, in the following documents:<br><br>1. ‘2017_Tweets_Grenfelll Public Inquiry began’ represents data that were collected between September 7 and 21 in 2017. This centred on the formal opening of the Grenfell Tower Inquiry on 14th September 2017. We collected 93 racist tweets out of 6,348 Grenfell Tower fire-related tweets from this period. <br><br>2. ‘2018_Tweets_Petition’ represents tweets from the second data collection time period that was during the time of the petition that Grenfell supporters created to demand a debate in Parliament to include survivors and bereaved families in the inquiry process. The petition began on 14th May 2018 and it ended on 30th May 2018. We collected 112 racist tweets out of 7,578 Grenfell Tower fire-related tweets between those dates. <br><br>3. 2019_Tweets_Inquiry Report’ has tweets that were collected between 23rd October and 7th November in 2019. This included the date of the first inquiry report on 30th October. We collected 47 racist tweets out of 11,718 Grenfell Tower fire-related tweets during this period.<br><br>4. ‘2020_Tweets_COVID cancelled SW’ involves the last data collection time period that happened between 7th and 31st March 2020. This included the first Silent Walk that was cancelled because of the COVID-19 outbreak. We collected 33 (13 tweets with hashtags) racist tweets out of 1,010 Grenfell Tower fire-related tweets.<br>The Readme file includes further details.<br>The files include TweetIDs as captured in January 2021. These can be rehydrated using resources such as Twarc (https://github.com/DocNow/twarc) or Hydrator (https://github.com/DocNow/hydrator) in order to retrieve the Tweets as they currently appear on Twitter. Tweets which have been deleted since data capture will not be retrieved.<br>
DOI: 10.5517/ccdc.csd.cc26n7mr
title: CCDC 2045244: Experimental Crystal Structure Determination

description: Related Article: Zhaojin Wang, Juewen Zhao, Mohd Muddassir, Rongfeng Guan, Silu Tao|2021|Inorg.Chem.|60|4705|doi:10.1021/acs.inorgchem.0c03664
DOI: 10.5517/ccdc.csd.cc26n7ns
title: CCDC 2045245: Experimental Crystal Structure Determination

description: Related Article: Zhaojin Wang, Juewen Zhao, Mohd Muddassir, Rongfeng Guan, Silu Tao|2021|Inorg.Chem.|60|4705|doi:10.1021/acs.inorgchem.0c03664
DOI: 10.17903/fk2/df25lr
title: De-industrialization and biographical transformations. Life histories of industrial workers in Lavrion

description: This dissertation deals with the process of de-industrialization of the Lavrio area in the early 1990s, examining how social developments and changes are recorded in the life stories of affected industrial workers. The purpose of this work is to examine how the rapid changes brought about by the de-industrialization wave are recruited and experienced by industrial workers as well as by the crisis management techniques they employ. The study adopts the directions of interpretative research tradition and qualitative methodology in social research. In particular, the biographical approach is adopted. The study was based on empirical material collected in 1994 during a six-month field study in Lavrio.The types and sources of material vary. Includes all available demographics, prior studies on Lavrion, publications in the local and Athenian press, interviews with representatives of local agencies. At the core of the research process, however, was the conduct and processing of 17 narrative biographies with dismissed industrial male and female workers.
DOI: 10.21227/4tyf-4109
title: Forex Market Movements

description: The data was collected and represents the historical data of 6 currency pairs measured in 4-hour intervals: 4 major pairs such as GBP/USD, EUR/USD, USD/CHF, USD/JPY and 2 minor pairs such as EUR/GBP and GBP/JPY.The data set covers the period from 01-2000 to 07-2020 Three files exists: the sequence dataset, the label dataset, and the legenda.The legenda reports the follwoing information:BBSignal, BBBreakoutSignal, LabelStrategy:
DOI: 10.5281/zenodo.5667539
title: Aratinga auricapillus SPAdes preassembly

description: .
DOI: 10.5281/zenodo.5667538
title: Aratinga auricapillus SPAdes preassembly

description: .
DOI: 10.18434/mds2-1468
title: Radio Frequency Measurements for Selected Manufacturing and Industrial Environments
DOI: 10.18434/mds2-1508
title: Computer Code for Tennesse Eastman Industrial Wireless Systems Performance Evaluation
DOI: 10.18434/mds2-1509
title: Computer Code for Industrial Wireless Measurement Analysis and Scenario Generation
DOI: 10.18434/mds2-1868
title: Intel Edison wireless latency and reliability computing code
DOI: 10.18434/mds2-1941
title: Model of the Wireless Factory Work-cell using the Systems Modeling Language
DOI: 10.18434/m32010
title: Translator from Extended SysML to Physical Interaction and Signal Flow Simulation Platforms

description: The translator implements the SysML Extension for Physical Interaction and Signal Flow Simulation (SysPhS). It can generate Modelica or Simulink/Simscape files from SysML models extended according to the SysPhS specification. This dataset binary and source code for the translator, as well as a set of SysML models that can be translated into simulation models.Disclaimer: Certain commercial equipment, instruments, or materials are identified in this dataset to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.