cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/kcjt4v658x.2
title: Alzheimer's disease MRI images

description: The current dataset is about Alzheimer's disease (AD). It contains MRI images of 26 subjects, of which 10 subjects have AD, 10 subjects have Mild Cognitive impairment (MCI) and 4 subjects are normal controls. The images were collected from Firoozgar Hospital in Tehran, Iran.
DOI: 10.17632/hh3pgbw827
title: LINF_030006000

description: Leucine-rich repeat (LRR) domain-containing protein; Leishmania infantum (strain JPCM5)
DOI: 10.17632/hh3pgbw827.1
title: LINF_030006000

description: Leucine-rich repeat (LRR) domain-containing protein; Leishmania infantum (strain JPCM5)
DOI: 10.17632/5ytkx6y5yp.1
title: LINF_030005800

description: Cytochrome c oxidase assembly protein; Leishmania infantum (strain JPCM5)
DOI: 10.17632/j7b5622tbt.1
title: LINF_030005900

description: Hypothetical protein - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.17632/cbtj3wzys9.1
title: LINF_030005700

description: DNA primase large subunit; Leishmania infantum (strain JPCM5)
DOI: 10.17632/fvhw2zwt5d.1
title: Anti-Inflammatory Cytokine Stimulation of HMC3 Cells: Proteome Dataset

description: Excel spreadsheets that contain qualitative and quantitative data from processed mass spectrometry raw files, as well as associated biological processes, from the analysis of HMC3 cells stimulated with anti-inflammatory cytokines.
DOI: 10.17632/5p3563c95d.1
title: Structural data from Devonian suture zone in SW Iberia (Central Unit, Spain)

description: This dataset includes a collection of individual measurements of bedding, foliations and lineations from the so-called Central Unit and its surrounding rocks, located in southwest Iberia (Spain). Data were collected in 2019, 2020, 2021, 2022, and 2023. Planar and linear data are divided into several groups, depending on the type, time of formation, and ascription to tectonostratigraphic units.
Bedding is referred to as S0. Foliation and lineation measurements refer to the orientation of the local main foliation (Sn) and specific type of lineation observed in the field (crenulation, intersection, stretching, and mineral).
The “inHP” and “outHP” terms refer to the within or outside location of the data relative to the high-pressure rock belt defined by the Central Unit, respectively.
Plane measurements are given as dip direction and dip, while line measurements are given as plunge and trend. The structural dataset includes UTM coordinates (Zone 29, European Datum 1950) indicating the location of individual measurements.
DOI: 10.17632/ns4j779zvm.1
title: LINF_360051000

description: N-acetylglucosamine-1-phosphate transferase | ALG7
Leishmania infantum (strain JPCM5)
DOI: 10.15468/dl.drpzh4
title: Occurrence Download

description: A dataset containing 80 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Occurrence evidence",
 "TaxonKey is Hirundo Linnaeus, 1758",
 "Year 2020-2025"
 ]
}

 The dataset includes 80 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004435-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/b3d2p9fzg3.1
title: Differentially Expressed Features in HEI vs HEU in B-CELL IMMUNITY AND VACCINE INDUCED ANTIBODY PROTECTION REVEAL THE INEFFICACY OF CURRENT VACCINATION SCHEDULE IN INFANTS WITH PERINATAL HIV-INFECTION IN MOZAMBIQUE, AFRICA. by Cotugno N, et Al.

description: All differentially expressed features in children living with HIV compared to HIV exposed uninfected children for the paper entitled: B-CELL IMMUNITY AND VACCINE INDUCED ANTIBODY PROTECTION REVEAL THE INEFFICACY OF CURRENT VACCINATION SCHEDULE IN INFANTS WITH PERINATAL HIV-INFECTION IN MOZAMBIQUE, AFRICA by Cotugno N. et Al.
DOI: 10.17632/nm2rmn9wrp.1
title: Dataset for "Energy use in residential buildings for sustainable development: The fifth Solar Decathlon Europe revelations"

description: This research focuses on energy use in residential buildings for sustainable development based on the fifth Solar Decathlon Europe. The dataset includes 1.Preferred Reporting Items for Systematic Reviews and Meta-Analyses screening data, 2.The development of publications on Solar Decathlon research with time, 3.The keyword co-occurrence analysis of 230 related publications, 4.Global Solar Decathlon competitions information, 5.The fifth Solar Decathlon European rules, 6.The ranking of the fifth Solar Decathlon European, 7.The fifth Solar Decathlon European’s 16 teams information, 8.The competition site and 16 competing buildings figures, 9.The flowchart of the research paper, 10.16 teams’ concept and energy use technologies summary, 11.Sankey diagram of energy use technologies analysis, 12.ToAtal score of sub-contests in Energy Performance and Sustainability, 13.Workflow of multi-criteria decision analysis and 14.Annual solar radiation simulation source files. Solar Decathlon history data comes from https://www.solardecathlon.gov/. Solar Decathlon European information comes from https://sde21.eu/. Each building’s project manual and drawing come from https://building-competition.org/EU2021. All the photos were shot by the authors. At last, some revelations are obtained from onsite investigation, literature review, software simulation, qualitative analysis, quantitative analysis and comparative analysis. For more information, please refer to the publication “Energy use in residential buildings for sustainable development: The fifth Solar Decathlon Europe revelations”.
DOI: 10.17632/y98rgjmgyv.1
title: LINF_360051800

description: serine/threonine protein kinase
Leishmania infantum (strain JPCM5)
DOI: 10.17632/sr2vppg24f.1
title: Spatial Distribution and Long-term Persistence of Wolbachia-infected Aedes aegypti in the Mentari Court, Malaysia

description: The mosquito Aedes aegypti is the primary vector of dengue virus to humans. Wolbachia bacteria can prevent dengue virus transmission following transfer into this species. Ae. aegypti of both sexes carrying Wolbachia strain wAlbB were released in Mentari Court, Malaysia, since October 2017 for 20 weeks. This study aims to investigate the spatial distribution of Wolbachia-carrying mosquitoes in a high-rise residential site, and examine the nature of the composition of the mosquito population across the site. There are numerous findings; firstly, Wolbachia-infected mosquitoes showed a high infection frequency four years after release. Secondly, there were some minor effects of floor and block on Wolbachia frequency. Thirdly, there was no evidence that the Aedes albopictus has in-creased across the area. While the invasion of Wolbachia will vary from location to location, this study filled a knowledge gap on the invasion of such a strain in a high-rise residential area. This study can assist in planning field release strategies and the development of models that can forecast local success.
DOI: 10.17632/ypmgnp23rd.1
title: LINF_360053000

description: ubiquitin-protein ligase | HECT5
Leishmania infantum (strain JPCM5)
DOI: 10.17632/2t5pg25vkh.1
title: Supplementary Data 1.0: The use of a deep learning model in the histopathological diagnosis of actinic keratosis: A case control accuracy study

description: The dataset contains supplementary figures and tables for the original article: The use of a deep learning model in the histopathological diagnosis of actinic keratosis: A case control accuracy study

Abstract: 
Background Actinic Keratosis (AK) is a frequent dermatological diagnosis which contributes to a large proportion of routine dermatopathology. A current development in histopathology is in the digitization of specimens by creating whole slide images (WSI) with slide scanners. Deep Learning Models (DLM) have been introduced to radiology or pathology for image recognition but dermatopathology lacks available solutions. Objective Building on previous work about skin pathologies, this paper proposes a DLM following the U-Net architecture to detect AK in histopathological samples. Methods In total, 297 histopathological slides (269 with AK and 28 without AK) have been retrospectively selected. They were randomly assigned to training, validation and testing groups. Performance was evaluated by conducting a Case Control Accuracy Study on three levels of granularity. Results The DLM model achieved an overall accuracy of 99.13% on the WSI level, 99.02% on the patch level and an intersection over union (IoU) of 83.88%. Limitations The major limitations of this study were inclusion and digitization of AK from one laboratory and one scanner type. Conclusion The proposed DLM reliably recognizes AK in histopathological images, supporting the implementation of DLMs in dermatopathology practice. Given existing technical capabilities and advancements, DLMs could have a significant influence on dermatopathology routine in the future.
DOI: 10.17632/2t5pg25vkh.2
title: Supplementary Data 1.0: The use of a deep learning model in the histopathological diagnosis of actinic keratosis: A case control accuracy study

description: The dataset contains supplementary figures and tables for the original article: The use of a deep learning model in the histopathological diagnosis of actinic keratosis: A case control accuracy study

Abstract: 
Background Actinic Keratosis (AK) is a frequent dermatological diagnosis which contributes to a large proportion of routine dermatopathology. A current development in histopathology is in the digitization of specimens by creating whole slide images (WSI) with slide scanners. Deep Learning Models (DLM) have been introduced to radiology or pathology for image recognition but dermatopathology lacks available solutions. Objective Building on previous work about skin pathologies, this paper proposes a DLM following the U-Net architecture to detect AK in histopathological samples. Methods In total, 297 histopathological slides (269 with AK and 28 without AK) have been retrospectively selected. They were randomly assigned to training, validation and testing groups. Performance was evaluated by conducting a Case Control Accuracy Study on three levels of granularity. Results The DLM model achieved an overall accuracy of 99.13% on the WSI level, 99.02% on the patch level and an intersection over union (IoU) of 83.88%. Limitations The major limitations of this study were inclusion and digitization of AK from one laboratory and one scanner type. Conclusion The proposed DLM reliably recognizes AK in histopathological images, supporting the implementation of DLMs in dermatopathology practice. Given existing technical capabilities and advancements, DLMs could have a significant influence on dermatopathology routine in the future.
DOI: 10.17632/2t5pg25vkh.3
title: Supplementary Data 1.0: The use of a deep learning model in the histopathological diagnosis of actinic keratosis: A case control accuracy study

description: The dataset contains supplementary figures and tables for the original article: The use of a deep learning model in the histopathological diagnosis of actinic keratosis: A case control accuracy study

Abstract: 
Background Actinic Keratosis (AK) is a frequent dermatological diagnosis which contributes to a large proportion of routine dermatopathology. A current development in histopathology is in the digitization of specimens by creating whole slide images (WSI) with slide scanners. Deep Learning Models (DLM) have been introduced to radiology or pathology for image recognition but dermatopathology lacks available solutions. Objective Building on previous work about skin pathologies, this paper proposes a DLM following the U-Net architecture to detect AK in histopathological samples. Methods In total, 297 histopathological slides (269 with AK and 28 without AK) have been retrospectively selected. They were randomly assigned to training, validation and testing groups. Performance was evaluated by conducting a Case Control Accuracy Study on three levels of granularity. Results The DLM model achieved an overall accuracy of 99.13% on the WSI level, 99.02% on the patch level and an intersection over union (IoU) of 83.88%. Limitations The major limitations of this study were inclusion and digitization of AK from one laboratory and one scanner type. Conclusion The proposed DLM reliably recognizes AK in histopathological images, supporting the implementation of DLMs in dermatopathology practice. Given existing technical capabilities and advancements, DLMs could have a significant influence on dermatopathology routine in the future.
DOI: 10.17632/2t5pg25vkh.4
title: Supplementary Data: Development and Performance Evaluation of a Deep Learning Model for the Histopathological Diagnosis of Actinic Keratosis: A Diagnostic Case-Control-Accuracy Study

description: The dataset contains clinical data.

Abstract: 
Actinic keratosis (AK) is a precancerous skin lesion with the potential to progress into squamous cell carcinoma (SCC), with an overall prevalence of 14\%. Although AK is not routinely biopsied, it represents a significant portion of dermatopathology cases. Advances in histopathology now allow for the digitization of slides using whole slide imaging (WSI). While deep learning models (DLMs) have shown promise in various medical fields, their application in dermatopathology remains limited. Building on prior research, we developed a U-Net-based DLM to detect AK in histopathological slides. A total of 371 cases were used to develop the DLM, with two optimal classification thresholds identified for different tasks. In the test cohort, the DLM achieved an overall accuracy of 98.9\% at the patch level and an intersection over union (IoU) of 78.8\%. At the WSI level, the DLM reached an accuracy of 96.8\% and an IoU of 67.7\%. Additionally, 626 remaining cases were evaluated in a Diagnostic Case-Control-Accuracy Study, where the DLM's performance on AK slides and negative controls was compared to the gold standard, achieving an accuracy of 97.6\%. Our findings demonstrate the potential of DLMs to reliably detect AK in routine dermatopathology, suggesting their future impact as technology continues to advance.
DOI: 10.17632/n4ygnxwscb.1
title: LINF_360053300

description: DEAD/H RNA helicase | MAK5
Leishmania infantum (strain JPCM5)
DOI: 10.17632/smfb7kcv3r.1
title: LINF_360052400

description: SET9
Leishmania infantum (strain JPCM5)
DOI: 10.17632/wy9hvt9ggg.1
title: LINF_360052900

description: 19S proteasome regulatory subunit
Leishmania infantum (strain JPCM5)
DOI: 10.17632/mtw4rfx7jf.1
title: LINF_360052600

description: predicted C2 domain protein
Leishmania infantum (strain JPCM5)
DOI: 10.17632/vmft4d24h6.1
title: Crisis-Induced Environmental Action: Examining Youth Environmental Behaviors during Economic Crisis in Pakistan

description: This dataset arises from a cross-sectional survey conducted to explore how Generation Z's cognition of the post-COVID-19 economic recession influences their Pro-Environmental Behaviors (PEBs) across distinct spheres. The data was collected from respondents aged 18–26, residing in six major cities across Pakistan. The survey instrument measured variables related to emergency relevance, emergency coping, positive and negative environmental affective reactions, and self-reported PEBs in each sphere.
The dataset serves as the empirical foundation for testing an integrative model informed by Affective Events Theory, which examines the interplay of emergency cognition and affective reactions in driving environmental behavior during economic crises. The study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) for analysis, offering insights into differential behavioral patterns across the identified spheres and advancing understanding of the challenges and opportunities for environmental action during economic downturns.This resource is valuable for researchers and policymakers interested in behavioral responses to intersecting economic and environmental challenges.
DOI: 10.17632/w35ktjjzvn.1
title: Post-Disaster Recovery Assessment of Mangrove Forests in Leyte Island, Philippines using Sentinel-2 Imagery

description: The research investigated the synergy of different vegetation indices, biophysical variables, and landscape metrics in assessing the post-disaster recovery of the mangrove forests in Maasin City and Matalom in Leyte Island, Philippines after Super Typhoon Odette (International Name: Rai). These data show the monthly mean vegetation indices, monthly mean biophysical variables, and four-month landscape metrics values in the study area from 2019 to 2022, which spans three years before and one year after Super Typhoon Odette hit the area. The data were gathered using Google Earth Engine, SNAP Biophysical Processor, and QGIS Landscape Ecology Statistics Plug-In. Time series analysis was used to interpret the data, which includes the Theil-Sen Estimator and Mann-Kendall Test. Notable finding includes decline across all post-disaster parameters which reveal substantial damage to the functional, structural, and landscape configuration characteristics of the mangrove forest. Contrasting recovery and resiliency profiles were observed between vegetation indices and biophysical variables, which indicate that previous post-disaster studies that only employed NDVI may have reported underestimd recovery values.
DOI: 10.17632/fzrmgjfyg7.1
title: Transcription Induced Domains form the elementary constraining building blocks of the bacterial chromosome

description: Microscopy images supporting Figure 4 
Fig 4b-i Raw images of the panels comparing the positions of one or two parS tags in one or two conditions. Images correspond to phase contrast of the bacteria outline, YFP of the parSpMT1 tag and CFP of the parSP1 tag. Conditions are desc
Fig 4j: Raw data for the analysis of the dynamics of the three lacO arrays inserted in the vicinity of the T7 promoter. LacI-YFP foci dynamics was analyzed for 100 time intervals of 1 sec, for each replicate (N = 4 -8) the median MSDα measurement for ~1,000 trajectories of the fluorescently labeled loci was computed. Experiment were performed in the absence of rifampicin upon induction of the T7 TU.
DOI: 10.17632/rckt4c3522.1
title: LINF_360054000

description: DnaJ domain-containing protein | JDP33 | J33
Leishmania infantum (strain JPCM5)
DOI: 10.17632/n4hnvvrjx6.1
title: LINF_360053900

description: protein of unknown function - conserved
Leishmania infantum (strain JPCM5)
DOI: 10.17632/djvkx8mh2t.1
title: LINF_360055000

description: protein of unknown function - conserved
Leishmania infantum (strain JPCM5)
DOI: 10.15468/dl.v3f4a4
title: Occurrence Download

description: A dataset containing 24105 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (South Africa, Lesotho, Eswatini)",
 "TaxonKey is one of (Cisticola aberrans (A.Smith, 1843))",
 "BasisOfRecord is one of (Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1950",
 "OccurrenceStatus is Present",
 "DistanceFromCentroidInMeters is greater than or equal to 2000"
 ]
}

 The dataset includes 24105 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004436-250117142028555/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/y3b8kr68t4.1
title: LINF_170011300

description: Hypothetical protein - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.17632/wfpbgcyn7k.1
title: LINF_170011600

description: Kinesin-like protein; Leishmania infantum (strain JPCM5)
DOI: 10.5281/zenodo.13834596
title: Data repository for "Evolution of social contacts patterns in France over the SARS-CoV-2 pandemic: results from the SocialCov survey"
DOI: 10.5281/zenodo.13834595
title: Data repository for "Evolution of social contacts patterns in France over the SARS-CoV-2 pandemic: results from the SocialCov survey"
DOI: 10.17632/4c8dhj9xbp.1
title: Margouillat Survey
DOI: 10.17632/78rz8vw46x.1
title: Micro-scale potentiodynamic polarisation curves of 316L stainless steel

description: This database comprises 5 Potentiodynamic Polarisation (PP) datasets. Each dataset consists of a pair of CSVs: 1 file containing the values of the applied potential scan rate (mV/s); and 1 having the corresponding current density j (µA/cm²) values.

This database was first deployed in the following scientific article, accepted for publication in Corrosion Science journal on 10 March 2023:
"Probing the randomness of the local current distributions of 316L stainless steel corrosion in NaCl solution".
Leonardo Bertolucci Coelho1,2,∗, Daniel Torres1, Miguel Bernal1, Gian Marco Paldino3, Gianluca Bontempi3, Jon Ustarroz 1,2
1 ChemSIN – Chemistry of Surfaces, Interfaces and Nanomaterials, Université libre de Bruxelles (ULB), Brussels, Belgium
2 Research Group Electrochemical and Surface Engineering (SURF), Vrije Universiteit Brussel, Brussels, Belgium
3 Machine Learning Group (MLG), Université libre de Bruxelles (ULB), Brussels, Belgium
*leonardo.bertolucci.coelho@ulb.be

955 PP curves were recorded on the same 316L sample using the Scanning Electrochemical Cell Microscopy (SECCM) in hopping-mode protocol. A total of 12 SECCM sessions were performed, comprising maps with up to 17x17 points (during up to 24 h of continuous measurement). 
Five different combinations of [NaCl] and voltammetric scan rates were employed: 0.005 M NaCl – 100 mV/s, 0.01 M NaCl – 100 mV/s, 0.01 M NaCl – 50 mV/s, 0.05 M NaCl – 100 mV/s, 0.05 M NaCl – 50 mV/s. The number of data samples (j Vs E curves) is 287, 377, 119, 125 and 47 for each dataset. These quantities of curves were considered representative of each testing condition, as the underlying j distributions were continuous. 
The datasets were sliced from 0.5 V Vs Ag/AgCl (considerably more positive than the OCP) upward to avoid the eventual and deleterious occurrence of negative currents. Positive j values were preferred for building histograms and obtaining defined log(j) numbers.
The SECCM experiments were designed to detect low corrosion currents, and the trade-off for such a high signal sensitivity was the I saturation level at 10 nA. In case of missing data points in the anodic sweep data (most often caused by oversaturation at high applied overpotentials), these were replaced by data interpolation (Python pandas interpolate() method, spline). This data-filling procedure was used for a small proportion of the data populations (0%, 0%, 1%, 1% and 11%, respectively, for the datasets with increasing testing aggressiveness). The final shapes of the j Vs E curves were validated against similar curves with unsaturated signals at the relevant E ranges.
DOI: 10.17632/4c8dhj9xbp.2
title: Margouillat Survey

description: Data from the Margouillat Survey, an original hypothetical survey experiment designed to assess the acceptability of the First-Come, First-Served rule in a context of discrimination on the rental housing market. The survey was completed via SurveyMonkey in June, 2019 by 1.541 respondents representative of the US population.
DOI: 10.17632/r5ht4s3zz8.1
title: LINF_170017900

description: Hypothetical protein - conserved; Leishmania infantum (strain JPCM5)
DOI: 10.17632/jz726skp6d.3
title: Data for: Chapter 4 (Properties of and Charged Defects in ThO2) of the PhD Thesis - Large-Scale Density Functional Theory (DFT) Simulations of Lanthanide and Actinide Oxide Materials

description: This repository hosts computational data supporting the findings in Chapter 4 (Properties and Charged Defects) of the PhD thesis: Large-Scale Density Functional Theory (DFT) Simulations of Lanthanide and Actinide Oxide Materials in ThO2 by Nabeel Anwar. Supervised by Robert M. Harker, Mark T. Storr, Marco Molinari and Chris-Kriton Skylaris. Available from: https://eprints.soton.ac.uk/495909/.

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