cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4013769
title: Sudanese Jibbah in UK Institutions

description: This dataset was created by Elvira Thomas during the summer of 2020 as part of her Junior Research Associate Project funded by the University of Sussex. The project is attached to the “Making African Connections” (MAC) Project of which Sussex University and the Royal Engineers Museum are partners. The project, which was titled “Plundered artefacts and the Colonial Gaze: exhibiting Sudan at the Royal Engineers Museum after Covid-19”, investigates museum display in UK institutions of artefacts which were plundered by colonial British forces at the end of the 19<sup>th</sup> Century. It takes an object-focus on Mahdist <em>jibbah</em>, tunics worn by Ansar (followers of al Mahdi) in conflicts during the Mahdist war in Sudan at the end of the 19<sup>th</sup> C. Many of these <em>jibbah</em> were plundered after the Battle of Omdurman (1898), taken back to Britain and dispersed into private collections and cultural institutions across the UK. Five of these Mahdist <em>jibbah</em> are now held at the Royal Engineers Museum. In order to explore how these important cultural artefacts of Sudanese heritage have been historically framed in triumphalist narratives of empire in UK institutions, the project combined a case study of the museum display at the Royal Engineers Museum with a wider investigation of Mahdist <em>jibbah</em> held in institutions across the UK. This dataset was created as part of the wider investigation of <em>jibbah</em> held in UK institutions. Due to the outbreak of the Covid-19 pandemic which restricted close contact with the <em>jibbah</em> held at REM, this part of the project became a crucial element of research. There were three main goals for this investigation: to produce an estimate of the amount of Sudanese <em>jibbah</em> existing in UK cultural institutions to locate, record and publish a list of these artefacts to make them knowable to ‘source’ communities or anyone searching for Sudanese heritage in UK institutions to analyse digital catalogue entries (accessibility, data prioritisation and linguistic analysis) of the <em>jibbah</em> held in UK institutions and consider to what extent legacies of colonialism are encountered in the digital display of plundered artefacts held in the UK
DOI: 10.6084/m9.figshare.2068683
title: Priming in a shape task but not in a category task under CFS (Hesselmann et al. 2016)

description: Data from the JoV 2016 paper and "read me"<br>
DOI: 10.6084/m9.figshare.2068695.v2
title: AFS_data_analyse_170323 -labview 2016

description: analyse software
DOI: 10.6084/m9.figshare.2068665
title: Tf values, word frequency values for gathering idf values, and evaluation data of the paper submitted to PLOS ONE, titled "Identifying Topics in Microblogs Using Wikipedia"

description: This data provides the topics identified by our approach BOUN-TI, on the data collected from Twitter while the 2012 U.S.A. presidential debates were holding.The dataset also provides tf values of words in a Wikipedia snapshot, and the values required to gain idf values of words. Word frequency distribution of an interval of Twitter english public stream tweets' is provided.
DOI: 10.6084/m9.figshare.2068650
title: PHEME rumour scheme dataset: journalism use case

description: This is a dataset collected and annotated within the journalism use case of the PHEME FP7 project. These rumours are associated with 9 different breaking news. It was created for the analysis of social media rumours, and contains Twitter conversations which are initiated by a rumourous tweet; the conversations include tweets responding to those rumourous tweets. These tweets have been annotated for support, certainty, and evidentiality.<br>The dataset contains 330 conversational threads (297 in English, and 33 in German), with a folder for each thread.<br>License: The annotations are provided under a CC-BY license, while Twitter retains the ownership and rights of the content of the tweets.
DOI: 10.6084/m9.figshare.2068683.v1
title: Darcy et al 2016 - data files

description: Data from the JoV paper and "read me"<br>
DOI: 10.6084/m9.figshare.2068647
title: Mapping the global geographic potential of Zika virus spread

description: The study estimated the global distribution of a mosquito-borne Zika virus based on advanced Ecological Niche Modeling. We presented here a digital copy of the the figure presented in our manuscript. This digital copy provide information for areas at risk and examine possible transmission scenario in 5 * 5 km pixel. The data can be open by any GIS software and is available for public community and International Health Organization to guide the surveillance priorities and control programs worldwide. We refer to factors that driven the outbreak in Brazil and other parts of the world. <br>
DOI: 10.6084/m9.figshare.2068665.v1
title: data.tar.gz

description: This data provides the topics identified by our approach BOUN-TI, on the data collected from Twitter while the 2012 U.S.A. presidential debates were holding.<br>The dataset also provides tf values of words in a Wikipedia snapshot, and the values required to gain idf values of words. Word frequency distribution of an interval of Twitter english public stream tweets' is provided.
DOI: 10.6084/m9.figshare.2068677.v1
title: MAMEM EEG SSVEP Dataset I (256 channels, 11 subjects, 5 frequencies)

description: EEG signals with 256 channels captured from 11 subjects executing a SSVEP-based experimental protocol. Five different frequencies (6.66, 7.50, 8.57, 10.00 and 12.00 Hz) have been used for the visual stimulation, and the EGI 300 Geodesic EEG System (GES 300), using a 256-channel HydroCel Geodesic Sensor Net (HCGSN) and a sampling rate of 250 Hz has been used for capturing the signals. Check https://github.com/MAMEM/ssvep-eeg-processing-toolbox for the processing toolbox.
DOI: 10.6084/m9.figshare.2068683.v3
title: Priming in a shape task but not in a category task under CFS (Hesselmann et al. 2016)

description: Data from the JoV 2016 paper and "read me"<br>
DOI: 10.6084/m9.figshare.2068644
title: Dermacentor occidentalis 16S rRNA OTU table

description: OTU table (from 515F/806R 16S rRNA gene sequences) derived from individual Dermacentor occidentalis ticks via Illumina sequencing. OTU table was filtered to remove samples with less than 150 sequences and singleton OTUs and was rarefied to an even sampling depth of 150.
DOI: 10.6084/m9.figshare.2068644.v1
title: Dermacentor occidentalis 16S rRNA OTU table

description: OTU table (from 515F/806R 16S rRNA gene sequences) derived from individual Dermacentor occidentalis ticks via Illumina sequencing. OTU table was filtered to remove samples with less than 150 sequences and singleton OTUs and was rarefied to an even sampling depth of 150.
DOI: 10.6084/m9.figshare.2068677.v2
title: MAMEM EEG SSVEP Dataset I (256 channels, 11 subjects, 5 frequencies presented in isolation)

description: EEG signals with 256 channels captured from 11 subjects executing a SSVEP-based experimental protocol. Five different frequencies (6.66, 7.50, 8.57, 10.00 and 12.00 Hz) have been used for the visual stimulation, and the EGI 300 Geodesic EEG System (GES 300), using a 256-channel HydroCel Geodesic Sensor Net (HCGSN) and a sampling rate of 250 Hz has been used for capturing the signals. Check <b>https://www.youtube.com/watch?v=8lGBVvCX5d8&feature=youtu.be</b> for a video demonstrating one trial.Check <b>https://github.com/MAMEM/ssvep-eeg-processing-toolbox </b>for the processing toolbox.Check <b>http://arxiv.org/abs/1602.00904 </b>for the technical report.
DOI: 10.6084/m9.figshare.2068683.v2
title: Darcy et al 2016 - data files

description: Data from the JoV paper and "read me"<br>
DOI: 10.6084/m9.figshare.2068626
title: A dataset for the test-retest reliability assessment of EEG & ERP quantities

description: This data package contains single-trial ERPs from 4 participants, each tested in 10 sessions on 10 different days. Biosemi data in BDF format were recorded from 128 electrodes at 512 Hz. Stimuli were images of faces and noise textures presented for 82 ms without a mask. Participants were asked to indicate whether they saw a face or a texture as fast and accurately as possible using key ‘1’ and ‘2’ on the numerical pad. They performed 11 blocks of 100 trials. Sessions took place at 9 am or 11 am, at the same time every day. Participants were asked not to consume alcohol the night before, and to complete sleep and mood questionnaires.<br>Because of battery issues some sessions do not have 1,100 trials: P2 session 1 = 1084 trialsP3 session 6 = 1088 trialsP3 session 6 = 1092 trials<br>The raw EEG files, in BDF format, are stored in zip archives: - ft10_p1.zip- ft10_p2.zip- ft10_p3.zip- ft10_p4.zip<br>`ft10_behaviour.zip` = mat files of behavioural results.`Glasgow_Biosemi_132.ced` = electrode locations`ft10_preprocessing_example.m` = script using EEGLAB`ft10_ptb.zip` = folder to run the experiment using the PTB in Matlab<br>`Participant Information ft10.xlsx` = demographics, questionnaires, eye tests<br><b>This dataset is not associated with any publication.</b> We collected the data but haven't analysed them yet. The references below provide examples of test-retest analyses we did with other datasets.
DOI: 10.6084/m9.figshare.2068629
title: Exp1_PreRat_Brightness.sav

description: These dataset corresponds to the article, "Music for a brighter world: Brightness judgment bias by musical emotion" by Joydeep Bhattacharya & Job P Lindsen that is published in the journal, PLoS ONE <br>
DOI: 10.6084/m9.figshare.2068629.v1
title: Exp1_PreRat_Brightness.sav

description: These dataset corresponds to the article, "Music for a brighter world: Brightness judgment bias by musical emotion" by Joydeep Bhattacharya & Job P Lindsen that is published in the journal, PLoS ONE <br>
DOI: 10.6084/m9.figshare.2068677.v3
title: MAMEM EEG SSVEP Dataset I (256 channels, 11 subjects, 5 frequencies presented in isolation)

description: EEG signals with 256 channels captured from 11 subjects executing a SSVEP-based experimental protocol. Five different frequencies (6.66, 7.50, 8.57, 10.00 and 12.00 Hz) have been used for the visual stimulation, and the EGI 300 Geodesic EEG System (GES 300), using a 256-channel HydroCel Geodesic Sensor Net (HCGSN) and a sampling rate of 250 Hz has been used for capturing the signals. Check <b>https://www.youtube.com/watch?v=8lGBVvCX5d8&feature=youtu.be</b> for a video demonstrating one trial.Check <b>https://github.com/MAMEM/ssvep-eeg-processing-toolbox </b>for the processing toolbox.Check <b>http://arxiv.org/abs/1602.00904 </b>for the technical report.
DOI: 10.6084/m9.figshare.2068614
title: GRRA-times-Sparc

description: This file contains the execution times for all the allocator, generating code for the Sparc architecture.
DOI: 10.6084/m9.figshare.2068617
title: GRRA-ICs-Sparc

description: This file contains the confidence intervals for observations of the GRRA allocator, generating code for the Sparc architecture.
DOI: 10.6084/m9.figshare.2068620
title: GRRA-ICs-x86

description: This file contains the confidence intervals for observations of the GRRA allocator, generating code for the x86 architecture.
DOI: 10.6084/m9.figshare.2068677.v4
title: MAMEM EEG SSVEP Dataset I (256 channels, 11 subjects, 5 frequencies presented in isolation)

description: EEG signals with 256 channels captured from 11 subjects executing a SSVEP-based experimental protocol. Five different frequencies (6.66, 7.50, 8.57, 10.00 and 12.00 Hz) have been used for the visual stimulation, and the EGI 300 Geodesic EEG System (GES 300), using a 256-channel HydroCel Geodesic Sensor Net (HCGSN) and a sampling rate of 250 Hz has been used for capturing the signals. Check <b>https://www.youtube.com/watch?v=8lGBVvCX5d8&feature=youtu.be</b> for a video demonstrating one trial.Check <b>https://github.com/MAMEM/ssvep-eeg-processing-toolbox </b>for the processing toolbox.Check <b>http://arxiv.org/abs/1602.00904 </b>for the technical report.
DOI: 10.6084/m9.figshare.2068611
title: GRRA-times-x86

description: This file contains the execution times for all the allocator, generating code for the x86 architecture.<br>
DOI: 10.6084/m9.figshare.2068614.v1
title: GRRA-times-Sparc

description: This file contains the execution times for all the allocator, generating code for the Sparc architecture.
DOI: 10.6084/m9.figshare.2068617.v1
title: GRRA-ICs-Sparc

description: This file contains the confidence intervals for observations of the GRRA allocator, generating code for the Sparc architecture.
DOI: 10.6084/m9.figshare.2068608
title: Statistical Learning of Neuronal Functional Connectivity

description: Identifying the network structure of a neuron ensemble beyond the standard measure of pairwise correlations is critical for understanding how information is transferred within such a neural population. However, the spike train data pose significant challenges to conventional statistical methods due to not only the complexity, massive size, and large scale, but also high dimensionality. In this article, we propose a novel “structural information enhanced” (SIE) regularization method for estimating the conditional intensities under the generalized linear model (GLM) framework to better capture the functional connectivity among neurons. We study the consistency of parameter estimation of the proposed method. A new “accelerated full gradient update” algorithm is developed to efficiently handle the complex penalty in the SIE-GLM for large sparse datasets applicable to spike train data. Simulation results indicate that our proposed method outperforms existing approaches. An application of the proposed method to a real spike train dataset, obtained from the prelimbic region of the prefrontal cortex of adult male rats when performing a T-maze based delayed-alternation task of working memory, provides some insight into the neuronal network in that region.
DOI: 10.6084/m9.figshare.2068611.v1
title: GRRA-times-x86

description: This file contains the execution times for all the allocator, generating code for the x86 architecture.<br>
DOI: 10.6084/m9.figshare.2068620.v2
title: GRRA-ICs-x86

description: This file contains the confidence intervals for observations of the GRRA allocator, generating code for the x86 architecture.
DOI: 10.6084/m9.figshare.2068608.v1
title: Statistical Learning of Neuronal Functional Connectivity

description: Identifying the network structure of a neuron ensemble beyond the standard measure of pairwise correlations is critical for understanding how information is transferred within such a neural population. However, the spike train data pose significant challenges to conventional statistical methods due to not only the complexity, massive size, and large scale, but also high dimensionality. In this article, we propose a novel “structural information enhanced” (SIE) regularization method for estimating the conditional intensities under the generalized linear model (GLM) framework to better capture the functional connectivity among neurons. We study the consistency of parameter estimation of the proposed method. A new “accelerated full gradient update” algorithm is developed to efficiently handle the complex penalty in the SIE-GLM for large sparse datasets applicable to spike train data. Simulation results indicate that our proposed method outperforms existing approaches. An application of the proposed method to a real spike train dataset, obtained from the prelimbic region of the prefrontal cortex of adult male rats when performing a T-maze based delayed-alternation task of working memory, provides some insight into the neuronal network in that region.
DOI: 10.6084/m9.figshare.2068620.v1
title: New draft item

description: This file contains the confidence intervals for observations of the GRRA allocator, generating code for the x86 architecture.
DOI: 10.6084/m9.figshare.2068602
title: A Bootstrap Metropolis–Hastings Algorithm for Bayesian Analysis of Big Data

description: Markov chain Monte Carlo (MCMC) methods have proven to be a very powerful tool for analyzing data of complex structures. However, their computer-intensive nature, which typically require a large number of iterations and a complete scan of the full dataset for each iteration, precludes their use for big data analysis. In this article, we propose the so-called bootstrap Metropolis–Hastings (BMH) algorithm that provides a general framework for how to tame powerful MCMC methods to be used for big data analysis, that is, to replace the full data log-likelihood by a Monte Carlo average of the log-likelihoods that are calculated in parallel from multiple bootstrap samples. The BMH algorithm possesses an embarrassingly parallel structure and avoids repeated scans of the full dataset in iterations, and is thus feasible for big data problems. Compared to the popular divide-and-combine method, BMH can be generally more efficient as it can asymptotically integrate the whole data information into a single simulation run. The BMH algorithm is very flexible. Like the Metropolis–Hastings algorithm, it can serve as a basic building block for developing advanced MCMC algorithms that are feasible for big data problems. This is illustrated in the article by the tempering BMH algorithm, which can be viewed as a combination of parallel tempering and the BMH algorithm. BMH can also be used for model selection and optimization by combining with reversible jump MCMC and simulated annealing, respectively. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.2068590
title: Diatom community samples

description: 1- Diatom sampling and analysesDiatoms were sampled as part of the national biomonitoringprogram for rivers. The sampling procedure followed the French standard (Afnor, 2007). Diatoms were collected once per year, during the low-flow period. Benthic diatoms were collected from at least five stones from the lotic parts of the sampling sites. The upper surfaces of the stones were scrubbed with a toothbrush to collect the biofilms in which diatoms live. Then the sample was fixed in 4% formaldehyde. In laboratory, the diatom valves were cleanedusing 40% H2O2 and HCl. Clean valves were mounted in a resin (Naphrax©). At least 400 valves from each sample were counted and identified using a light microscope (1000× magnification) according to European (European Committee for Standardization, 2004) and French (Afnor, 2007) standards. The abundances of all observed taxa were expressed as relative counts. Identifications were carried out using Krammer and Lange-Bertalot (1986–1991) to species and sub-species level.<br>2- Physical and chemical sample analysesPhysical and chemical analyses were carried-out at the same sampling sites every month. Dissolved oxygen and conductivity were measured in the field. For NO3−, NH4 +, Kjeldahl nitrogen (TKN), PO43−, Na+, Ca2+, Cl−, K+, Mg2+, SO42−, Biological-Oxygen-Demand (BOD), Chemical-Oxygen-Demand (COD), water samples were collected and analysed in the laboratory according to standard procedures (APHA, 1995).
DOI: 10.6084/m9.figshare.2068590.v2
title: Diatom community samples

description: 1- Diatom sampling and analysesDiatoms were sampled as part of the national biomonitoringprogram for rivers. The sampling procedure followed the French standard (Afnor, 2007). Diatoms were collected once per year, during the low-flow period. Benthic diatoms were collected from at least five stones from the lotic parts of the sampling sites. The upper surfaces of the stones were scrubbed with a toothbrush to collect the biofilms in which diatoms live. Then the sample was fixed in 4% formaldehyde. In laboratory, the diatom valves were cleanedusing 40% H2O2 and HCl. Clean valves were mounted in a resin (Naphrax©). At least 400 valves from each sample were counted and identified using a light microscope (1000× magnification) according to European (European Committee for Standardization, 2004) and French (Afnor, 2007) standards. The abundances of all observed taxa were expressed as relative counts. Identifications were carried out using Krammer and Lange-Bertalot (1986–1991) to species and sub-species level.<br>2- Physical and chemical sample analysesPhysical and chemical analyses were carried-out at the same sampling sites every month. Dissolved oxygen and conductivity were measured in the field. For NO3−, NH4 +, Kjeldahl nitrogen (TKN), PO43−, Na+, Ca2+, Cl−, K+, Mg2+, SO42−, Biological-Oxygen-Demand (BOD), Chemical-Oxygen-Demand (COD), water samples were collected and analysed in the laboratory according to standard procedures (APHA, 1995).
DOI: 10.6084/m9.figshare.2068578
title: Dealing with discordance: a novel approach for analysing U–Pb detrital zircon datasets

description: R code, in .R format
DOI: 10.6084/m9.figshare.2068578.v1
title: Dealing with discordance: a novel approach for analysing U–Pb detrital zircon datasets

description: R code, in .R format
DOI: 10.6084/m9.figshare.2068590.v1
title: diatom database

description: 1- Diatom sampling and analysesDiatoms were sampled as part of the national biomonitoringprogram for rivers. The sampling procedure followed the French standard (Afnor, 2007). Diatoms were collected once per year, during the low-flow period. Benthic diatoms were collected from at least five stones from the lotic parts of the sampling sites. The upper surfaces of the stones were scrubbed with a toothbrush to collect the biofilms in which diatoms live. Then the sample was fixed in 4% formaldehyde. In laboratory, the diatom valves were cleanedusing 40% H2O2 and HCl. Clean valves were mounted in a resin (Naphrax©). At least 400 valves from each sample were counted and identified using a light microscope (1000× magnification) according to European (European Committee for Standardization, 2004) and French (Afnor, 2007) standards. The abundances of all observed taxa were expressed as relative counts. Identifications were carried out using Krammer and Lange-Bertalot (1986–1991) to species and sub-species level.<br>2- Physical and chemical sample analysesPhysical and chemical analyses were carried-out at the same sampling sites every month. Dissolved oxygen and conductivity were measured in the field. For NO3−, NH4 +, Kjeldahl nitrogen (TKN), PO43−, Na+, Ca2+, Cl−, K+, Mg2+, SO42−, Biological-Oxygen-Demand (BOD), Chemical-Oxygen-Demand (COD), water samples were collected and analysed in the laboratory according to standard procedures (APHA, 1995).
DOI: 10.6084/m9.figshare.2068563
title: Raphael's <i>Acts of the Apostles</i> Tapestries for Leo X: Sight, Sound, and Space in the Sistine Chapel

description: The Sistine Chapel in the Vatican was the intended site for tapestries depicting the lives of Saints Peter and Paul that were commissioned in 1515 by Leo X, designed by Raphael in Rome, and woven in the Brussels workshop of Pieter van Aelst. The contribution these tapestries made to papal majesty has long been acknowledged, and various schemes for their hanging within the chapel have been proposed. Their effect not only on the preexisting paintings but also on the spoken and sung rituals that took place in the Sistine Chapel is significant as well.