cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.22701370
title: VC4_HSPhageB

description: VC4_HSPhageB
DOI: 10.6084/m9.figshare.22701370.v1
title: VC4_HSPhageB

description: VC4_HSPhageB
DOI: 10.6084/m9.figshare.22701373
title: VC4_HSPhageC

description: VC4_HSPhageC
DOI: 10.6084/m9.figshare.22701373.v1
title: VC4_HSPhageC

description: VC4_HSPhageC
DOI: 10.6084/m9.figshare.22701376
title: VC4_HSPhageD

description: VC4_HSPhageD
DOI: 10.6084/m9.figshare.22701376.v1
title: VC4_HSPhageD

description: VC4_HSPhageD
DOI: 10.57841/casdc.0000541
title: 远场语音识别数据集
DOI: 10.57841/casdc.0000542
title: 更新后的MISP2021挑战赛的音视频语音识别语料库
DOI: 10.5281/zenodo.7868375
title: IODP Expedition 354 Core drilling summary

description: Report includes detailed drilling data for each core: pump(s) used, mud pumped, strokes, shear pins/pressure, bit size/rotation, weight on bit, top drive torque, rate of penetration, core jams, winch and wirelines, core catcher/shoe and barrel, and whether core orientation, drillover, formation temperature, tracers, liners were used.
DOI: 10.5281/zenodo.7868374
title: IODP Expedition 354 Core drilling summary

description: Report includes detailed drilling data for each core: pump(s) used, mud pumped, strokes, shear pins/pressure, bit size/rotation, weight on bit, top drive torque, rate of penetration, core jams, winch and wirelines, core catcher/shoe and barrel, and whether core orientation, drillover, formation temperature, tracers, liners were used.
DOI: 10.5281/zenodo.7868377
title: IODP Expedition 354 Gas safety report

description: This composite report returns data from two different gas chromatograph configurations (GC3 and NGA). Each row combines data from several measurements made on the same sample at the same time for a particular headspace or vacutainer sample. If data do not exist for a particular expedition, the column does not appear. Gas samples were measured by gas chromatography and either flame ionization detection (GC-FID) or thermal conductivity detection (GC-TCD). Reported analytes may include methane, ethane, ethene, propane, propene, n-butane, i-butane, n-pentane, i-pentane, n-hexane, i-hexane, n-heptane, i-heptane, nitrogen, oxygen, carbon monoxide, carbon dioxide, and hydrogen sulfide. When data are available, methane to (ethane + ethene) ratio (C<sub>1</sub>/C<sub>2</sub>�ratio) is reported. To identify individual samples and tests, see each separate analysis (GC3, NGAFID, and/or NGATCD).
DOI: 10.5281/zenodo.7868376
title: IODP Expedition 354 Gas safety report

description: This composite report returns data from two different gas chromatograph configurations (GC3 and NGA). Each row combines data from several measurements made on the same sample at the same time for a particular headspace or vacutainer sample. If data do not exist for a particular expedition, the column does not appear. Gas samples were measured by gas chromatography and either flame ionization detection (GC-FID) or thermal conductivity detection (GC-TCD). Reported analytes may include methane, ethane, ethene, propane, propene, n-butane, i-butane, n-pentane, i-pentane, n-hexane, i-hexane, n-heptane, i-heptane, nitrogen, oxygen, carbon monoxide, carbon dioxide, and hydrogen sulfide. When data are available, methane to (ethane + ethene) ratio (C<sub>1</sub>/C<sub>2</sub>�ratio) is reported. To identify individual samples and tests, see each separate analysis (GC3, NGAFID, and/or NGATCD).
DOI: 10.5281/zenodo.7868380
title: IODP Expedition 354 Bulk Density (GRA)

description: Gamma ray attenuation (GRA) data were acquired using a Cs-137 collimated source and a sodium iodide (thallium), or NaI(Tl), scintillation detector. The signal was calibrated using water and aluminum standards to provide a proxy for bulk density. This measurement was performed by a sensor mounted on either the Whole-Round Multisensor Logger (WRMSL) or the Special Task Multisensor Logger (STMSL); which track was used for a given data set is indicated in the data.
DOI: 10.5281/zenodo.7868379
title: IODP Expedition 354 Bulk Density (GRA)

description: Gamma ray attenuation (GRA) data were acquired using a Cs-137 collimated source and a sodium iodide (thallium), or NaI(Tl), scintillation detector. The signal was calibrated using water and aluminum standards to provide a proxy for bulk density. This measurement was performed by a sensor mounted on either the Whole-Round Multisensor Logger (WRMSL) or the Special Task Multisensor Logger (STMSL); which track was used for a given data set is indicated in the data.
DOI: 10.35078/60esje
title: Replication Data for: Ficha epidemiológica de localidade: Distrito: Diamantina | Município: Inimutaba

description: Levantamento Triatomínico com operações de campo ocorridas em Minas Gerais pela Superintendência de Campanhas de Saúde Pública (SUCAM) e FUNASA (Fundação Nacional de Saúde), no período de 1979 a 1999, objetivando a busca da presença de vetores da doença de Chagas ou de vestígios dessa presença. A partir dela são orientadas as operações com inseticida (pt) ) Triatomine survey with field operations carried out in Minas Gerais by the Public Health Campaigns Superintendence (SUCAM) and FUNASA (National Health Foundation), from 1979 to 1999, aiming to search for the presence of Chagas disease vectors or traces of that presence. From it, operations with insecticide are guided (en) Encuesta de triatominos con operaciones de campo realizada en Minas Gerais por la Superintendencia de Campañas de Salud Pública (SUCAM) y FUNASA (Fundación Nacional de Salud), de 1979 a 1999, con el objetivo de buscar la presencia de vectores de la enfermedad de Chagas o rastros de esa presencia. Desde ella se orientan las operaciones con insecticida (es)
DOI: 10.5281/zenodo.7868385
title: IODP Expedition 354 Hole drilling summary

description: Report includes detailed drilling data: BHA length, bit parameters, and primary depth measurements.
DOI: 10.5281/zenodo.7868384
title: IODP Expedition 354 Hole drilling summary

description: Report includes detailed drilling data: BHA length, bit parameters, and primary depth measurements.
DOI: 10.5281/zenodo.7868387
title: IODP Expedition 354 Hole summary

description: Report lists hole data: location, water depth, drilling, coring system, and core recovery.
DOI: 10.5281/zenodo.7868386
title: IODP Expedition 354 Hole summary

description: Report lists hole data: location, water depth, drilling, coring system, and core recovery.
DOI: 10.5281/zenodo.7868395
title: IODP Expedition 354 Ion chromatography

description: Cation and anion concentration in interstitial water samples was measured by ion chromatography using a conductivity detector. Cation analytes include calcium, magnesium, potassium, and sodium. Anion analytes include chloride, sulfate, and bromide.
DOI: 10.5281/zenodo.7868394
title: IODP Expedition 354 Ion chromatography

description: Cation and anion concentration in interstitial water samples was measured by ion chromatography using a conductivity detector. Cation analytes include calcium, magnesium, potassium, and sodium. Anion analytes include chloride, sulfate, and bromide.
DOI: 10.6084/m9.figshare.22701322
title: Diretrizes para Apoiar a Adoção da OPLA-Tool para Usuários Novatos

description: A técnica de Linha de Produto de Software (LPS) tem como objetivo aumentar a qualidade e reúso de software através do uso de artefatos já desenvolvidos, como a Product Line Architecture (PLA). No entanto, obter uma PLA otimizada é um desafio para arquitetos de software. Para resolver esse problema, foi proposta a abordagem MOA4PLA, que visa otimizar automaticamente uma PLA. A ferramenta OPLA-Tool v2.0 foi desenvolvida para apoiar essa abordagem usando um algoritmo genético multi-objetivo. No entanto, a configuração dos parâmetros desse algoritmo pode ser difícil para usuários iniciantes, e estudos mostraram que diferentes configurações afetam os resultados produzidos pela ferramenta. Para apoiar usuários novatos ou inexperientes na utilização da OPLA-Tool v2.0, o objetivo principal desse projeto foi propor diretrizes para sua utilização. Três diferentes LPSs foram usadas nesse projeto: AGM, BET e MM. Sendo utilizadas 4 variações da PLA da AGM e MM e a PLA da BET como entrada dos experimentos. Após os experimentos utilizando diferentes tipos de configurações obteve-se as soluções e elas foram avaliadas usando dois indicadores de qualidade: Hypervolume e Distância Euclidiana. A partir dos valores obtidos nos indicadores, foi feita uma análise buscando as melhores configurações para cada PLA. Durante essa análise foram identificados quatro padrões envolvendo características das PLAs a serem otimizadas e o propósito de obter soluções mais diversificadas ou a solução de melhor balanceamento entre os objetivos de otimização. Por fim, usando os padrões identificados, cinco diretrizes foram propostas para orientar os usuários novatos, as quais precisam ser validadas em estudos futuros.
DOI: 10.6084/m9.figshare.22701322.v1
title: Diretrizes para Apoiar a Adoção da OPLA-Tool para Usuários Novatos

description: A técnica de Linha de Produto de Software (LPS) tem como objetivo aumentar a qualidade e reúso de software através do uso de artefatos já desenvolvidos, como a Product Line Architecture (PLA). No entanto, obter uma PLA otimizada é um desafio para arquitetos de software. Para resolver esse problema, foi proposta a abordagem MOA4PLA, que visa otimizar automaticamente uma PLA. A ferramenta OPLA-Tool v2.0 foi desenvolvida para apoiar essa abordagem usando um algoritmo genético multi-objetivo. No entanto, a configuração dos parâmetros desse algoritmo pode ser difícil para usuários iniciantes, e estudos mostraram que diferentes configurações afetam os resultados produzidos pela ferramenta. Para apoiar usuários novatos ou inexperientes na utilização da OPLA-Tool v2.0, o objetivo principal desse projeto foi propor diretrizes para sua utilização. Três diferentes LPSs foram usadas nesse projeto: AGM, BET e MM. Sendo utilizadas 4 variações da PLA da AGM e MM e a PLA da BET como entrada dos experimentos. Após os experimentos utilizando diferentes tipos de configurações obteve-se as soluções e elas foram avaliadas usando dois indicadores de qualidade: Hypervolume e Distância Euclidiana. A partir dos valores obtidos nos indicadores, foi feita uma análise buscando as melhores configurações para cada PLA. Durante essa análise foram identificados quatro padrões envolvendo características das PLAs a serem otimizadas e o propósito de obter soluções mais diversificadas ou a solução de melhor balanceamento entre os objetivos de otimização. Por fim, usando os padrões identificados, cinco diretrizes foram propostas para orientar os usuários novatos, as quais precisam ser validadas em estudos futuros.
DOI: 10.5281/zenodo.7868399
title: IODP Expedition 354 Interstitial water composite report

description: This composite report includes data from numerous interstitial water (IW) analyses (e.g., ICP-LIQUIDS and IONCHROM) in a variable set of columns. Each row combines the data from several tests (groups of measurements made on the same sample at the same time) for a particular IW sample. If data do not exist for a particular expedition, the column does not appear. To identify individual samples and tests, see each separate analysis (ALKALINITY, ICP-LIQUIDS, IONCHROM, SALINITY, SPEC, TITRATOR, and potentially others). Major and minor elements were determined by inductively coupled plasma-atomic emission spectrocsopy (ICP-LIQUIDS). Cations and anions were measured by ion chromatography (IONCHROM). Complex ions were measured by colorimetric measurements using a UV-VIS spectrometer (SPEC). Alkalinity and chloride was measured by titration (ALKALINITY and TITRATOR). Dissolved carbon (DC), dissolved inorganic carbon (DIC), and dissolved organic carbon (DOC) were measured by combustion in a total organic carbon (TOC) analyzer. Salinity was measured by refractometer (SALINITY). pH was measured by ion-selective electrode as part of the alkalinity procedure and is reported in the ALKALINITY analysis.
DOI: 10.5281/zenodo.7868398
title: IODP Expedition 354 Interstitial water composite report

description: This composite report includes data from numerous interstitial water (IW) analyses (e.g., ICP-LIQUIDS and IONCHROM) in a variable set of columns. Each row combines the data from several tests (groups of measurements made on the same sample at the same time) for a particular IW sample. If data do not exist for a particular expedition, the column does not appear. To identify individual samples and tests, see each separate analysis (ALKALINITY, ICP-LIQUIDS, IONCHROM, SALINITY, SPEC, TITRATOR, and potentially others). Major and minor elements were determined by inductively coupled plasma-atomic emission spectrocsopy (ICP-LIQUIDS). Cations and anions were measured by ion chromatography (IONCHROM). Complex ions were measured by colorimetric measurements using a UV-VIS spectrometer (SPEC). Alkalinity and chloride was measured by titration (ALKALINITY and TITRATOR). Dissolved carbon (DC), dissolved inorganic carbon (DIC), and dissolved organic carbon (DOC) were measured by combustion in a total organic carbon (TOC) analyzer. Salinity was measured by refractometer (SALINITY). pH was measured by ion-selective electrode as part of the alkalinity procedure and is reported in the ALKALINITY analysis.
DOI: 10.25934/pr00008756
title: Evaluation of Assessment of insulin utilization in people with type 1 diabetes mellitus using glucose excursions, insulin delivery metrics, and combined with nutrition and exercise information from the T1-DEXI dataset

description: In the US, approximately 1.5 million people have Type 1 Diabetes, and are treated with insulin, either in form of Multiple Insulin Dose Therapy (MDI) or Continuous Subcutaneous Insulin Infusion (CSII). The majority of these are using Continuous Glucose Monitoring (CGM). Understanding the dosing behavior of people with Type 1 Diabetes is important both for individual assessment and for population studies.

Most studies of dosing behavior have been based on self-reported behavior, either retrospectively or based on patient logbooks. Some studies have applied algorithms to identify specific activities, but the strength of the various approaches is not thoroughly evaluated.

The study will evaluate multiple approaches to determine dosing behavior from the literature or not yet published. Such evaluation will strengthen research into patient behavior in real world studies by allowing for rational selection of methodologies. This will allow for a better understanding of patient insulin dosing behavior and various approaches to enhance therapy.





1.	Peyrot M, Barnett AH, Meneghini LF, Schumm-Draeger PM. Insulin Adherence Behaviours and Barriers in the Multinational Global Attitudes of Patients and Physicians in Insulin Therapy Study. Diabet Med. 2012;29(5):682-689. doi: 10.1111/j.1464-5491.2012.03605.x.
2.	Osborn CY, Gonzalez JS. Measuring insulin adherence among adults with type 2 diabetes. J Behav Med. 2016 Aug;39(4):633-41. doi: 10.1007/s10865-016-9741-y.
3.	Morris AD, Boyle DI, McMahon AD, Greene SA, MacDonald TM, Newton RW. Adherence to insulin treatment, glycaemic control, and ketoacidosis in insulin-dependent diabetes mellitus. The DARTS/MEMO Collaboration. Diabetes Audit and Research in Tayside Scotland. Medicines Monitoring Unit. Lancet. 1997 Nov 22;350(9090):1505-10. doi: 10.1016/s0140-6736(97)06234-x.
4.	Currie CJ, Peyrot M, Morgan CL, et al.: The impact of treatment noncompliance on mortality in people with type 2 diabetes. Diabetes Care 2012;35:1279–1284.
5.	Currie CJ, Peyrot M, Morgan CL, et al.: The impact of treatment non-compliance on mortality in people with type 1 diabetes. J Diabetes Complications 2013;27:219–223.
DOI: 10.6084/m9.figshare.21777998
title: global1k.trees

description: 1,000 trees from the posterior sample after running MN-thorney.xml via BEAST v1.10.5 (ThorneyTreeLikelihood v0.1.1), https://github.com/beast-dev/beast-mcmc/releases/tag/v1.10.5pre_thorney_v0.1.1
DOI: 10.6084/m9.figshare.21777998.v4
title: global1k.trees

description: 1,000 trees from the posterior sample after running MN-thorney.xml via BEAST v1.10.5 (ThorneyTreeLikelihood v0.1.1), https://github.com/beast-dev/beast-mcmc/releases/tag/v1.10.5pre_thorney_v0.1.1
DOI: 10.6084/m9.figshare.21777998.v3
title: global1k.trees

description: 1,000 trees from the posterior sample after running MN-thorney.xml via BEAST v1.10.5 (ThorneyTreeLikelihood v0.1.1), https://github.com/beast-dev/beast-mcmc/releases/tag/v1.10.5pre_thorney_v0.1.1
DOI: 10.5281/zenodo.7440441
title: Strategy-based motor learning decreases the post-movement β power

description: This publication contains the following data, in connection with the manuscript entitled <em>"Strategy-based motor learning decreases the post-movement </em><em>β</em><em> power"</em>, by <em>Korka et al. (2023)</em>: 1- raw EEG and kinematics data files (for these, please see Version 1); 2 - task and stimulation files; 3 - analysis files. <strong>Task and stimulation files</strong>: - 2 experimental file formats, each corresponding to one condition: .exp - each .exp file calls on different scenarios (.sce) files: “train1” and “train2” correspond to task instructions regarding the experimental set-up and the general task (please see instructions in the <em>Supplementary Material</em> associated with the paper). “COMPENSATE_DEMO” and “IGNORE_DEMO” correspond to demonstrating the rotation in each condition; the rotation is constantly switched on for the duration of these training blocks. “COMPENSATE_TRAIN” and “IGNORE_TRAIN” correspond to training for the actual experiment including a probabilistic rotation (i.e., same as in the experimental blocks, but fewer trials). Finally, “COMPENSATE” and “IGNORE” scenarios represent the actual experimental blocks for each condition. <strong>Analysis files</strong> -main analysis files: ecEEG_kinematics – processes the relevant kinematics parameters by reading in the raw kinematics datafiles. ecEEG_eeg_preproc – reads in and preprocesses EEG raw data files; main steps steps include: filtering, segmenting the data into epochs (and matching each epoch with the kinematics data storing movement parameters), rejection of epochs containing artifacts based on visual inspection, rejection of channels containing extreme amplitudes, Independent Component Analysis (ICA) for detecting eye- and muscle-related artifacts, interpolation of channels, automated trial rejection after ICA. ecEEG_eeg_freq and ecEEG_eeg_freq_baseline – computes time-frequency analyses for the PMBR data and baseline (inter-trial-interval) data, respectively + re-references data to a common average. ecEEG_eeg_freq_2 – excludes marked trials based on task performance, kinematics parameters, and EEG rejection criteria + organizes the data from all participants into a unique structure (for PMBR and ITI data, in turn). ecEEG_change_plots – produces the EEG figures displayed in the manuscript + exports data for statistics. - auxiliary analysis files: ecEEG_ica – if called on, computes the ICA VMB_neighbours – defines neighbors for each channel, which are necessary for interpolation ecEEG_trialreject – automated trial rejection based on kurtosis topoplotbasic_ml2012_varchans – called on for creating the topographic plots when visualizing the ICA components ecEEG_eeg_trial_rej_15thresh – used to determine the number of trials in which participants did not follow the task instructions (see manuscript for details)
DOI: 10.5281/zenodo.7440442
title: Strategy-based motor learning decreases the post-movement β power

description: This publication contains the following data, in connection with the manuscript entitled <em>"Strategy-based motor learning decreases the post-movement </em><em>β</em><em> power"</em>, by <em>Korka et al. (2023)</em>: 1- raw EEG and kinematics data files; Structure of the data and other relevant details: <strong>Raw EEG files</strong>: - file format: .eeg = main EEG data file; .vhdr and .vmrk = adiditional header and marker files - 3 files of each type/participant, for each of the three days - data represent a continuous recording of that session (i.e., contains all trials from both conditions, including from practice blocks) - markers on EEG recording: Practice blocks: 8 – target onset; 16 – go signal; 32 – movement onset; 64 – movement offset; 128 – return circle Experimental blocks – IGNORE: 100 – target onset; 101 – go signal; 102 – movement onset; 103 – movement offset; 104 – return circle Experimental blocks – COMPENSATE: 200 – target onset; 201 – go signal; 202 – movement onset; 203 – movement offset; 204 – return circle <strong>Raw kinematics files</strong>: - 3 data files for each condition ("Compensate" and "Ignore"), each corresponding to one session (and one participant) - each data file contains 5 blocks of trials belonging to the same condition (see manuscript body for details).
DOI: 10.5281/zenodo.7868424
title: Strategy-based motor learning decreases the post-movement β power

description: This publication contains the following data, in connection with the manuscript entitled <em>"Strategy-based motor learning decreases the post-movement </em><em>β</em><em> power"</em>, by <em>Korka et al. (2023)</em>: 1- raw EEG and kinematics data files (for these, please see Version 1); 2 - task and stimulation files; 3 - analysis files. <strong>Task and stimulation files</strong>: - 2 experimental file formats, each corresponding to one condition: .exp - each .exp file calls on different scenarios (.sce) files: “train1” and “train2” correspond to task instructions regarding the experimental set-up and the general task (please see instructions in the <em>Supplementary Material</em> associated with the paper). “COMPENSATE_DEMO” and “IGNORE_DEMO” correspond to demonstrating the rotation in each condition; the rotation is constantly switched on for the duration of these training blocks. “COMPENSATE_TRAIN” and “IGNORE_TRAIN” correspond to training for the actual experiment including a probabilistic rotation (i.e., same as in the experimental blocks, but fewer trials). Finally, “COMPENSATE” and “IGNORE” scenarios represent the actual experimental blocks for each condition. <strong>Analysis files</strong> -main analysis files: ecEEG_kinematics – processes the relevant kinematics parameters by reading in the raw kinematics datafiles. ecEEG_eeg_preproc – reads in and preprocesses EEG raw data files; main steps steps include: filtering, segmenting the data into epochs (and matching each epoch with the kinematics data storing movement parameters), rejection of epochs containing artifacts based on visual inspection, rejection of channels containing extreme amplitudes, Independent Component Analysis (ICA) for detecting eye- and muscle-related artifacts, interpolation of channels, automated trial rejection after ICA. ecEEG_eeg_freq and ecEEG_eeg_freq_baseline – computes time-frequency analyses for the PMBR data and baseline (inter-trial-interval) data, respectively + re-references data to a common average. ecEEG_eeg_freq_2 – excludes marked trials based on task performance, kinematics parameters, and EEG rejection criteria + organizes the data from all participants into a unique structure (for PMBR and ITI data, in turn). ecEEG_change_plots – produces the EEG figures displayed in the manuscript + exports data for statistics. - auxiliary analysis files: ecEEG_ica – if called on, computes the ICA VMB_neighbours – defines neighbors for each channel, which are necessary for interpolation ecEEG_trialreject – automated trial rejection based on kurtosis topoplotbasic_ml2012_varchans – called on for creating the topographic plots when visualizing the ICA components ecEEG_eeg_trial_rej_15thresh – used to determine the number of trials in which participants did not follow the task instructions (see manuscript for details)