cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24377/ljmu.d.00000157
title: Barriers and facilitators to access for alcohol treatment in Liverpool.

description: Background Timely intervention for people with alcohol dependence in primary care is needed. Primary care services have a key role in supporting adults with alcohol dependence and require appropriate provision of services. Objective To examine the perceptions of both primary care practitioners and adults with alcohol dependence regarding service provision and to describe help seeking behaviours for adults with alcohol dependence. Design and setting Qualitative study consisting of semi-structured interviews with adults with alcohol dependence, healthcare professionals and staff members of specialist alcohol services who had previous or current experience in the management, treatment, or referral of adults with alcohol dependence in Northwest England. Method Interviews were conducted with ten adults with alcohol dependence and 15 staff. Data were analysed thematically, applying principles of constant comparison. Results Three themes were identified following inductive thematic analysis. The first theme, point of access relates to current service provision being reactive rather than preventative, the stigma associated with alcohol dependence and a person’s preparedness to change. The second theme identified was treatment process and pathways that highlights difficulties of engagement, mental health support, direct access and person-centred support. The third theme was follow-up care and discusses the opportunities and threats of transitional support or aftercare for alcohol dependence, signposting and peer support. Conclusion There are clear opportunities to support adults with alcohol dependence in primary care and the need to increase provision for timely intervention for alcohol related issues in primary care.
description: Qualitative Interviews
DOI: 10.25418/crick.23542332
title: Fig1_behavior_Vir_D18

description: Behavior data for Fig. 1E, G and fig. S1B-K in Ammari, Monaca et al. (2023)<b>variable names ======</b>ID = mouse IDrec_day = recording daylick_t = licking/grooming time (s)lick_f = licking/grooming frequency (number of episodes during 15min assay)sniff_t = sniffing time (s)sniff_f = sniffing frequency (number of episodes during 15min assay)nest_t = time spent in nest (s)retr_1 = latency to retrieve first pup (s)retr_2 = latency to retrieve second pup (s)self_t = self-grooming time (s) self_f = self-grooming frequency (number of episodes during 15min assay)build_t = nest building time (s)crouch_t = time spent crouching above pups (s)crouch_f = crouching frequency (number of episodes during 15min assay)sniff_min = sniffing frequency (per min)lick_min = licking/grooming frequency (per min)self_min = self-grooming frequency (per min)crouch_min = crouching frequency (per min)
DOI: 10.25418/crick.23542332.v1
title: Fig1_behavior_Vir_D18

description: Behavior data for Fig. 1E, G and fig. S1B-K in Ammari, Monaca et al. (2023)<b>variable names ======</b>ID = mouse IDrec_day = recording daylick_t = licking/grooming time (s)lick_f = licking/grooming frequency (number of episodes during 15min assay)sniff_t = sniffing time (s)sniff_f = sniffing frequency (number of episodes during 15min assay)nest_t = time spent in nest (s)retr_1 = latency to retrieve first pup (s)retr_2 = latency to retrieve second pup (s)self_t = self-grooming time (s) self_f = self-grooming frequency (number of episodes during 15min assay)build_t = nest building time (s)crouch_t = time spent crouching above pups (s)crouch_f = crouching frequency (number of episodes during 15min assay)sniff_min = sniffing frequency (per min)lick_min = licking/grooming frequency (per min)self_min = self-grooming frequency (per min)crouch_min = crouching frequency (per min)
DOI: 10.6084/m9.figshare.24183078.v1
title: Suppl Data1.xlsx Supplementary materials to Ozawa et al. (2023) [DOI: 10.3390/ijms241713590]

description: Suppl Data1.xlsx Supplementary materials to Ozawa et al. (2023) [DOI: 10.3390/ijms241713590]
DOI: 10.6084/m9.figshare.24183078
title: Suppl Data1.xlsx Supplementary materials to Ozawa et al. (2023) [DOI: 10.3390/ijms241713590]

description: Suppl Data1.xlsx Supplementary materials to Ozawa et al. (2023) [DOI: 10.3390/ijms241713590]
DOI: 10.5281/zenodo.7689069
title: Data for: Processability of Mg-Gd powder via friction extrusion

description: This dataset contains measurement data, machine logs as well as microstructure and overview images for the publication "Processability of Mg-Gd powder via friction extrusion".
DOI: 10.5281/zenodo.8370155
title: Ocular Injuries Caused by Motorcycle Accidents ‎

description: Data set
DOI: 10.5281/zenodo.8369880
title: Late Pleistocene evolution of tides and tidal dissipation: dissipation data

description: These .mat files contain the dissipation files calculated in Wilmes et al. (2023) Late Pleistocene evolution of tides and tidal dissipation. For details on how dissipation was calculated and the simulation details see the paper. Each file contains following variables: time_(i or g) = time in ka BP lat & lon = latitude and longitude vectors const = tidal constituents (M2, S2, K1, O1) diss_all = dissipation time slices for all runs (33 for Gowan, 62 for ICE-6G); dimensions: lon/lat/time/const mz_all = land mask time slices for all runs; dimensions: lon/lat/time hz_all = bathymetries time slices for all runs; dimensions: lon/lat/time dtot_all / dshelf_all/ ddeep_all = globally integrated dissipation values for all time slices for global dissipation / open dissipation / shelf dissipation; dimensions: time/const
DOI: 10.25625/m7ruly/irdhan
title: Tastic AI - Opensource - AI tools.tab

description: :unav
DOI: 10.25625/m7ruly
title: TasticAI - 4000 artificial intelligence tools categorized

description: This AI Tools Dataset is a comprehensive collection of 4,000 distinct artificial intelligence (AI) tools and software applications meticulously categorized into 50 unique categories. This dataset has been curated with the intention of supporting research and innovation in the field of artificial intelligence across various domains. It serves as a valuable resource for universities, researchers, and data scientists interested in exploring the diverse landscape of AI technologies. COUNT | LINE ----------------------------------------------------- 319 | AI writing tools 201 | AI Productivity tools 182 | AI day-to-day assistant 163 | AI tools for education 153 | AI image generator 148 | AI tools for developer 136 | Social media AI tools 121 | AI startup tools 115 | AI Customer Service tools 113 | AI design tool 110 | AI image editing tool 107 | AI SEO tools 106 | AI HR tools 100 | No code AI tools 98 | AI tools for research 92 | AI Prompt Generator 91 | AI coding tools 90 | AI email assistant 90 | AI tools for art 88 | AI summarizer tools 86 | AI music generator 86 | AI sales tools 86 | AI video Generator tools 76 | AI search engine 74 | AI finance tools 71 | AI efficiency tools 65 | AI Avatar Generators 59 | AI storytelling tools 58 | AI Text To Speech tools 54 | AI transcriber tools 53 | AI video editing tools 50 | AI healthcare tools 49 | AI 3D Model Generator 43 | AI travel tools 42 | AI audio editing tools 41 | AI Spreadsheet Tools 37 | AI experiments Tools 34 | AI Gaming tools 32 | AI e-commerce tools 32 | Fun AI Tools 31 | AI legal assistant 31 | AI presentation tools 27 | AI Dating tools 25 | AI SQL generator tools 25 | AI tools for gift ideas 22 | AI for Real Estate 20 | AI Fitness tools 18 | AI Fashion tools 16 | AI logo generator 12 | AI tools for memory 11 | AI Paraphraser tools 10 | AI Religious Tools ----------------------------------------------------- 3999 | TOTAL LINES 52 | DISTINCT LINES
DOI: 10.6084/m9.figshare.12090432
title: Bagworm decorations are an anti-predatory structure

description: Length and width data of <i>Eumeta crameri </i>caterpillar and their decorative bags.
DOI: 10.6084/m9.figshare.12090432.v1
title: Bagworm decorations are an anti-predatory structure

description: Length and width data of <i>Eumeta crameri </i>caterpillar and their decorative bags.
DOI: 10.5281/zenodo.8370204
title: Starting material: Reclaimed_carbonfiber

description: Table of reclaimed reclaimed carbonfiber used as starting material.
DOI: 10.5281/zenodo.8370203
title: Starting material: Reclaimed_carbonfiber

description: Table of reclaimed reclaimed carbonfiber used as starting material.
DOI: 10.6084/m9.figshare.24175233
title: HISAT2 indexes for chm13v2

description: HISAT2 indexes for chm13v2 {Kim, 2019}Kim, D., Paggi, J.M., Park, C. <i>et al.</i> Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. <i>Nat Biotechnol</i> <b>37</b>, 907–915 (2019).
DOI: 10.6084/m9.figshare.24175233.v2
title: HISAT2 indexes for chm13v2

description: HISAT2 indexes for chm13v2 {Kim, 2019}Kim, D., Paggi, J.M., Park, C. <i>et al.</i> Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. <i>Nat Biotechnol</i> <b>37</b>, 907–915 (2019).
DOI: 10.6084/m9.figshare.24175233.v1
title: HISAT2 indexes for chm13v2

description: HISAT2 indexes for chm13v2 {Kim, 2019}Kim, D., Paggi, J.M., Park, C. <i>et al.</i> Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. <i>Nat Biotechnol</i> <b>37</b>, 907–915 (2019).
DOI: 10.34740/kaggle/dsv/6519296
title: 2021 World Population (updated daily)
DOI: 10.34740/kaggle/dsv/6519295
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5281/zenodo.8370218
title: Starting_material_thermoplastic_matrix

description: Information in tables about the starting materials used in the project.
DOI: 10.5281/zenodo.8370217
title: Starting_material_thermoplastic_matrix

description: Information in tables about the starting materials used in the project.
DOI: 10.6084/m9.figshare.24015531
title: Supplementary Material for: Challenges in Pharmacovigilance: Variability in the Criteria for Determining Drug-Associated Acute Kidney Injury in Retrospective, Observational Studies

description: <b><i>Background:</i></b> Drug-associated acute kidney injury (D-AKI) accounts for 19–26% of acute kidney injury (AKI) events in hospitalized patients and results in outcomes similar to patients with AKI from other etiologies. Diagnosing D-AKI is complex and various criteria have been used. <b><i>Summary:</i></b> To highlight the variability in D-AKI determination, a review was conducted between January 2017 and December 2022 using PubMed. Search terms included adaptations of “drug associated kidney injury” to identify a sampling of literature discussing definitions and criteria for D-AKI evaluation. The search yielded 291 articles that were uploaded to Rayyan, a software tool used to screen and select studies. Retrospective, observational electronic health record (EHR) studies conducted in hospitalized patients were included. The final sample contained 16 studies for data extraction, representing mostly adult populations (<i>n</i> = 13, 81.3%) in noncritical or unspecified inpatient settings (<i>n</i> = 12, 75%). Nine studies (56.3%) utilized the recommended Kidney Disease: Improving Global Outcome guidelines (KDIGO) criteria to define AKI. Baseline creatinine or laboratory criteria for kidney function were provided in 10 studies (62.5%). Eleven studies (68.8%) established a temporal sequence assessment linking nephrotoxin drug exposure to an AKI event, but these criteria were inconsistent among studies using time frames as soon as 3 months prior to AKI. <b><i>Conclusion:</i></b> This review highlights the substantial variability in D-AKI criteria in select studies. Minimum expectations about what should be reported and criteria for the elements reported are needed to assure transparency, consistency, and standardization of pharmacovigilance strategies.
DOI: 10.6084/m9.figshare.24015531.v2
title: Supplementary Material for: Challenges in Pharmacovigilance: Variability in the Criteria for Determining Drug-Associated Acute Kidney Injury in Retrospective, Observational Studies

description: <b><i>Background:</i></b> Drug-associated acute kidney injury (D-AKI) accounts for 19–26% of acute kidney injury (AKI) events in hospitalized patients and results in outcomes similar to patients with AKI from other etiologies. Diagnosing D-AKI is complex and various criteria have been used. <b><i>Summary:</i></b> To highlight the variability in D-AKI determination, a review was conducted between January 2017 and December 2022 using PubMed. Search terms included adaptations of “drug associated kidney injury” to identify a sampling of literature discussing definitions and criteria for D-AKI evaluation. The search yielded 291 articles that were uploaded to Rayyan, a software tool used to screen and select studies. Retrospective, observational electronic health record (EHR) studies conducted in hospitalized patients were included. The final sample contained 16 studies for data extraction, representing mostly adult populations (<i>n</i> = 13, 81.3%) in noncritical or unspecified inpatient settings (<i>n</i> = 12, 75%). Nine studies (56.3%) utilized the recommended Kidney Disease: Improving Global Outcome guidelines (KDIGO) criteria to define AKI. Baseline creatinine or laboratory criteria for kidney function were provided in 10 studies (62.5%). Eleven studies (68.8%) established a temporal sequence assessment linking nephrotoxin drug exposure to an AKI event, but these criteria were inconsistent among studies using time frames as soon as 3 months prior to AKI. <b><i>Conclusion:</i></b> This review highlights the substantial variability in D-AKI criteria in select studies. Minimum expectations about what should be reported and criteria for the elements reported are needed to assure transparency, consistency, and standardization of pharmacovigilance strategies.
DOI: 10.6084/m9.figshare.24015531.v1
title: Supplementary Material for: Challenges in Pharmacovigilance: Variability in the Criteria for Determining Drug-Associated Acute Kidney Injury in Retrospective, Observational Studies

description: <b><i>Background:</i></b> Drug-associated acute kidney injury (D-AKI) accounts for 19–26% of acute kidney injury (AKI) events in hospitalized patients and results in outcomes similar to patients with AKI from other etiologies. Diagnosing D-AKI is complex and various criteria have been used. <b><i>Summary:</i></b> To highlight the variability in D-AKI determination, a review was conducted between January 2017 and December 2022 using PubMed. Search terms included adaptations of “drug associated kidney injury” to identify a sampling of literature discussing definitions and criteria for D-AKI evaluation. The search yielded 291 articles that were uploaded to Rayyan, a software tool used to screen and select studies. Retrospective, observational electronic health record (EHR) studies conducted in hospitalized patients were included. The final sample contained 16 studies for data extraction, representing mostly adult populations (<i>n</i> = 13, 81.3%) in noncritical or unspecified inpatient settings (<i>n</i> = 12, 75%). Nine studies (56.3%) utilized the recommended Kidney Disease: Improving Global Outcome guidelines (KDIGO) criteria to define AKI. Baseline creatinine or laboratory criteria for kidney function were provided in 10 studies (62.5%). Eleven studies (68.8%) established a temporal sequence assessment linking nephrotoxin drug exposure to an AKI event, but these criteria were inconsistent among studies using time frames as soon as 3 months prior to AKI. <b><i>Conclusion:</i></b> This review highlights the substantial variability in D-AKI criteria in select studies. Minimum expectations about what should be reported and criteria for the elements reported are needed to assure transparency, consistency, and standardization of pharmacovigilance strategies.
DOI: 10.6084/m9.figshare.19794709
title: Detection of corrosion on steel structures using an artificial neural network

description: Image based-corrosion detection has become a widespread practice for steel structures, but fine-tuning their model parameters is time-consuming. Alternatively, convolutional neural networks (CNNs) can also be trained fast and automatically, but they demand a huge training dataset. In this paper, a corrosion detection approach based on an artificial neural network (ANN) whose training dataset size is less than 0.1% of that of typical CNNs is introduced. The input layer of the proposed ANN consists of textural and color properties. In the present work, different color spaces and textural properties are examined for their impact on the robustness of the ANN. Results reveal that the best color channels can be achieved by combining <i>CIE L*u*v*</i> and <i>YUV</i> color spaces. Moreover, <i>energy</i> is selected as the best texture feature with respect to the ANN robustness. The proposed ANN outperforms an available image processing algorithm from the perspective of both speed and accuracy. In conclusion, this ANN can be used for actual applications after a fast and straightforward training step.
DOI: 10.6084/m9.figshare.19794709.v1
title: Detection of corrosion on steel structures using an artificial neural network

description: Image based-corrosion detection has become a widespread practice for steel structures, but fine-tuning their model parameters is time-consuming. Alternatively, convolutional neural networks (CNNs) can also be trained fast and automatically, but they demand a huge training dataset. In this paper, a corrosion detection approach based on an artificial neural network (ANN) whose training dataset size is less than 0.1% of that of typical CNNs is introduced. The input layer of the proposed ANN consists of textural and color properties. In the present work, different color spaces and textural properties are examined for their impact on the robustness of the ANN. Results reveal that the best color channels can be achieved by combining <i>CIE L*u*v*</i> and <i>YUV</i> color spaces. Moreover, <i>energy</i> is selected as the best texture feature with respect to the ANN robustness. The proposed ANN outperforms an available image processing algorithm from the perspective of both speed and accuracy. In conclusion, this ANN can be used for actual applications after a fast and straightforward training step.
DOI: 10.6084/m9.figshare.24183198
title: CRdataset

description: Identification of resistance loci common to three, two and single rust species in tetraploid wheat by GWAS and multi-location trials
DOI: 10.5519/qd.h238uxus
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (5301930 records)
DOI: 10.5519/qd.so3hb6oe
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5281/zenodo.8370228
title: Recycled_Material_printability

description: Information regarding the printability of the tested materials including gcode files, printability reports, and lists of tested filaments.
DOI: 10.5281/zenodo.8370227
title: Recycled_Material_printability

description: Information regarding the printability of the tested materials including gcode files, printability reports, and lists of tested filaments.
DOI: 10.5281/zenodo.8370200
title: Kinetics of N2 Release from Diazo Compounds: A Combined Machine Learning-Density Functional Theory Study

description: Total potential (E) and Thermal correction to Gibbs Free Energies obtained using SMD/M06-2X/def2-TZVP//SMD/M06-2X/6-31G(d) level of theory in dichloroethane and Cartesian coordinates for all of the calculated structures. dataset Python Machine Learning Script
DOI: 10.5281/zenodo.8370199
title: Kinetics of N2 Release from Diazo Compounds: A Combined Machine Learning-Density Functional Theory Study

description: Total potential (E) and Thermal correction to Gibbs Free Energies obtained using SMD/M06-2X/def2-TZVP//SMD/M06-2X/6-31G(d) level of theory in dichloroethane and Cartesian coordinates for all of the calculated structures. dataset Python Machine Learning Script
DOI: 10.5281/zenodo.8370246
title: demonstrator_printing_process

description: A demonstrator case of the printing process.
DOI: 10.5281/zenodo.8370245
title: demonstrator_printing_process

description: A demonstrator case of the printing process.
DOI: 10.6084/m9.figshare.23750736
title: Statistical Process Monitoring of Artificial Neural Networks

description: The rapid advancement of models based on artificial intelligence demands innovative monitoring techniques which can operate in real time with low computational costs. In machine learning, especially if we consider artificial neural networks (ANNs), the models are often trained in a supervised manner. Consequently, the learned relationship between the input and the output must remain valid during the model’s deployment. If this stationarity assumption holds, we can conclude that the ANN provides accurate predictions. Otherwise, the retraining or rebuilding of the model is required. We propose considering the latent feature representation of the data (called “embedding”) generated by the ANN to determine the time when the data stream starts being nonstationary. In particular, we monitor embeddings by applying multivariate control charts based on the data depth calculation and normalized ranks. The performance of the introduced method is compared with benchmark approaches for various ANN architectures and different underlying data formats.
DOI: 10.6084/m9.figshare.23750736.v2
title: Statistical Process Monitoring of Artificial Neural Networks

description: The rapid advancement of models based on artificial intelligence demands innovative monitoring techniques which can operate in real time with low computational costs. In machine learning, especially if we consider artificial neural networks (ANNs), the models are often trained in a supervised manner. Consequently, the learned relationship between the input and the output must remain valid during the model’s deployment. If this stationarity assumption holds, we can conclude that the ANN provides accurate predictions. Otherwise, the retraining or rebuilding of the model is required. We propose considering the latent feature representation of the data (called “embedding”) generated by the ANN to determine the time when the data stream starts being nonstationary. In particular, we monitor embeddings by applying multivariate control charts based on the data depth calculation and normalized ranks. The performance of the introduced method is compared with benchmark approaches for various ANN architectures and different underlying data formats.
DOI: 10.6084/m9.figshare.23750736.v1
title: Statistical process monitoring of artificial neural networks

description: The rapid advancement of models based on artificial intelligence demands innovative monitoring techniques which can operate in real time with low computational costs. In machine learning, especially if we consider artificial neural networks (ANNs), the models are often trained in a supervised manner. Consequently, the learned relationship between the input and the output must remain valid during the model’s deployment. If this stationarity assumption holds, we can conclude that the ANN provides accurate predictions. Otherwise, the retraining or rebuilding of the model is required. We propose considering the latent feature representation of the data (called “embedding”) generated by the ANN to determine the time when the data stream starts being nonstationary. In particular, we monitor embeddings by applying multivariate control charts based on the data depth calculation and normalized ranks. The performance of the introduced method is compared with benchmark approaches for various ANN architectures and different underlying data formats.