cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.24352/ub.ovgu-2022-080
title: Finger kinematics of natural hand movements recorded by an exoskeleton data glove in younger and elderly persons

description: The data set provides measurements using a SenseGlove DK1.3 exoskeleton data glove while healthy younger and elderly participants performed natural hand movements. Specifically, participants were asked to perform six types of hand-movements where each finger (D2-D5) was used once per trial to perform a grip movement together with the thumb resulting in a sequence of four finger movements per trial.
DOI: 10.5258/soton/d3014
title: Dataset supporting the University of Southampton Doctoral Thesis "Silicon fibres for nonlinear wavelength conversion"

description: Dataset supporting the University of Southampton Doctoral Thesis "Silicon fibres for nonlinear wavelength conversion"

This data is an excel file and it includes the experimental data and simulation results used to support the thesis (from Chapter1 to Chapter6). The sheet names in the excel file 'data_summary.xlsx' are corresponding to the figure names in the thesis.

Dataset is available under a CC BY 4.0 licence

Related projects/Funders:
Engineering and Physical Sciences Research Council (EPSRC) (EP/P000940/1 & EP/P027644/1)
J.E. Sirrine Foundation
DOI: 10.5519/qd.t2ja5i17
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (33490 records)
DOI: 10.5281/zenodo.10640249
title: Data and Script for Network nestedness in primates: a structural constraint or a biological advantage of social complexity?
DOI: 10.5281/zenodo.10822245
title: Results of Room temperature compression tests

description: The results of room temperature compression tests carried out on porous H-X alloys
DOI: 10.5281/zenodo.10822246
title: Results of Room temperature compression tests

description: The results of room temperature compression tests carried out on porous H-X alloys
DOI: 10.5281/zenodo.10805109
title: test version 0.3

description: Data and code
DOI: 10.6084/m9.figshare.25417105
title: Mapping the landscape of automated species identification apps

description: Numerous automated species identification apps can be found online, free or for a fee. This proprietary landscape is both rich and dynamic, with apps coming and going, and thus its nature changes over time. To map the landscape of automated species identification apps, we used a method that mirrors a typical user's path to find an app that can help identify a specimen via a smartphone. Initial queries were made on the two predominant platforms for app acquisition, Google Playstore and Apple's App Store, using broad search terms (e.g. 'species identification AI', 'species ID AI', and 'species identifier'). These were then iteratively refined as well as expanded on with more targeted queries (e.g. 'plants', 'insects', 'birds', 'fish' and 'mushrooms') in response to the landscape that slowly became visible and to ensure a thorough cataloguing. During this process, we also came across adjacent topics such as 'rocks', 'plant diseases and care', as well as ‘pets’. To ensure that no important natural history apps were missed, we included these topics in our searches, and as they were part of our findings, we included them in the final dataset.
DOI: 10.5281/zenodo.10817684
title: USENIX'24 Artifact Datasets: With Great Power Come Great Side Channels: Statistical Timing Side-Channel Analyses with Bounded Type-1 Errors

description: This dataset contains the measurements and analysis results for our USENIX Security '24 paper 'With Great Power Come Great Side Channels: Statistical Timing Side-Channel Analyses with Bounded Type-1 Errors'.
DOI: 10.5281/zenodo.10817685
title: USENIX'24 Artifact Datasets: With Great Power Come Great Side Channels: Statistical Timing Side-Channel Analyses with Bounded Type-1 Errors

description: This dataset contains the measurements and analysis results for our USENIX Security '24 paper 'With Great Power Come Great Side Channels: Statistical Timing Side-Channel Analyses with Bounded Type-1 Errors'.
DOI: 10.6084/m9.figshare.25398895.v2
title: Data related to the identification and genomic characterisation of egoviruses

description: Databases our study used or generated include (1) all RNApolA proteins identified from the metagenomic survey, (2) Egovirales RNApolA proteins and major capsid proteins (MCPs) also identified from the metagenomic survey, (3) HMMs for the RNApolA and <i>Egovirales</i> MCP, (4) and the 57 Egovirales genomes &gt;50kb in length (including 13 complete genomes).
DOI: 10.5281/zenodo.10812989
title: Questionnaire and interview guidelines related to Süsser et al. (2024) on how the COVID-19 pandemic changed stakeholder engagement processes in sustainability research in the long-term

description: The survey questionnaire and the interview guidelines have been designed in the framework of the EU H2020 projects SENTINEL to study the impact of the coronavirus disease 2019 (COVID-19) on the stakeholder engagement processes in susatinability research in the longterm. We conducted interviews with researchers and engaged stakeholders, using a semi-structured interview guideline. We designed the survey as an explorative, semi-quantitative, self-completion online questionnaire, using the online tool “LimeSurvey”. The survey was reworked based on our previous survey. The study was a follow-up study on our own work (Süsser, D., Ceglarz, A., Stavrakas, V., & Lilliestam, J. COVID-19 vs. stakeholder engagement: the impact of coronavirus containment measures on stakeholder involvement in European energy research projects [version 1; peer review: awaiting peer review]. Open Research Europe 2021,1:57. doi: https://doi.org/10.12688/openreseurope.13683.1). 

If you use this questionnaire or the interview guideline in an academic publication, please cite the following article:

Süsser, D., Schibline, A. Ceglarz, A., Lilliestam, J., Stavrakas, V., & Schweizer, P.J. (2024), How the COVID-19 pandemic changed stakeholder engagement processes in sustainability research in the long-term, F1000 Research (to be published).
DOI: 10.5281/zenodo.10812990
title: Questionnaire and interview guidelines related to Süsser et al. (2024) on how the COVID-19 pandemic changed stakeholder engagement processes in sustainability research in the long-term

description: The survey questionnaire and the interview guidelines have been designed in the framework of the EU H2020 projects SENTINEL to study the impact of the coronavirus disease 2019 (COVID-19) on the stakeholder engagement processes in susatinability research in the longterm. We conducted interviews with researchers and engaged stakeholders, using a semi-structured interview guideline. We designed the survey as an explorative, semi-quantitative, self-completion online questionnaire, using the online tool “LimeSurvey”. The survey was reworked based on our previous survey. The study was a follow-up study on our own work (Süsser, D., Ceglarz, A., Stavrakas, V., & Lilliestam, J. COVID-19 vs. stakeholder engagement: the impact of coronavirus containment measures on stakeholder involvement in European energy research projects [version 1; peer review: awaiting peer review]. Open Research Europe 2021,1:57. doi: https://doi.org/10.12688/openreseurope.13683.1). 

If you use this questionnaire or the interview guideline in an academic publication, please cite the following article:

Süsser, D., Schibline, A. Ceglarz, A., Lilliestam, J., Stavrakas, V., & Schweizer, P.J. (2024), How the COVID-19 pandemic changed stakeholder engagement processes in sustainability research in the long-term, F1000 Research (to be published).
DOI: 10.5281/zenodo.10812915
title: Innovative Strategies for Blood-Brain Barrier (BBB) Permeability Modeling: Harnessing the Power of Machine Learning-based q-RASAR Approach

description: In the current research, we have unveiled an advanced technique termed the quantitative Read-Across Structure-Activity Relationship (q-RASAR) framework to harnesses the power of machine learning (ML) for significantly enhancing the precision of predictions related to blood-brain barrier (BBB) permeability. It is important to emphasize that the central objective of this study is not to introduce another model for predicting BBB permeability. Instead, our focus is on highlighting the improvement in predicting the BBB permeability of organic compounds by introducing the q-RASAR approach. This innovative methodology strives to enhance the precision of evaluating neuropharmacological implications and streamline the drug development process. In this investigation, we developed an ML-based q-RASAR PLS model using a dataset comprising 1012 compounds of diverse classes of heterocyclic and aromatic hydrocarbons, obtained from the freely accessible B3DB database (accessible at https://github.com/theochem/B3DB) to predict BBB permeability during the lead discovery phase for central nervous system (CNS) drugs. The model's predictive capability underwent validation using two external sets, encompassing a total of 1,130,315 compounds, including synthetic compounds and natural products (NPs) for data gap filling and other two external sets comprising 116 drug-like/drug compounds from FDA and ChEMBL databases to assess the model's reliability. This study aimed to bridge the data gap by employing a predictive model to estimate the impact of brain-plasma concentration ratios on BBB permeability for both synthetic compounds and natural products (NPs). To further enhance predictability, we have developed various other ML-based q-RASAR models. The insights from the developed model highlight the pivotal roles played by hydrophobicity, electronic effects, degree of ionization and steric factors as essential features facilitating the traversal of the blood-brain barrier. This research not only advances our understanding of the molecular determinants influencing the permeability of central nervous system drugs but also establishes a versatile computational platform for the rapid assessment of diverse compounds, facilitating informed decision-making in the realms of drug development and design.
DOI: 10.5281/zenodo.10812916
title: Innovative Strategies for Blood-Brain Barrier (BBB) Permeability Modeling: Harnessing the Power of Machine Learning-based q-RASAR Approach

description: In the current research, we have unveiled an advanced technique termed the quantitative Read-Across Structure-Activity Relationship (q-RASAR) framework to harnesses the power of machine learning (ML) for significantly enhancing the precision of predictions related to blood-brain barrier (BBB) permeability. It is important to emphasize that the central objective of this study is not to introduce another model for predicting BBB permeability. Instead, our focus is on highlighting the improvement in predicting the BBB permeability of organic compounds by introducing the q-RASAR approach. This innovative methodology strives to enhance the precision of evaluating neuropharmacological implications and streamline the drug development process. In this investigation, we developed an ML-based q-RASAR PLS model using a dataset comprising 1012 compounds of diverse classes of heterocyclic and aromatic hydrocarbons, obtained from the freely accessible B3DB database (accessible at https://github.com/theochem/B3DB) to predict BBB permeability during the lead discovery phase for central nervous system (CNS) drugs. The model's predictive capability underwent validation using two external sets, encompassing a total of 1,130,315 compounds, including synthetic compounds and natural products (NPs) for data gap filling and other two external sets comprising 116 drug-like/drug compounds from FDA and ChEMBL databases to assess the model's reliability. This study aimed to bridge the data gap by employing a predictive model to estimate the impact of brain-plasma concentration ratios on BBB permeability for both synthetic compounds and natural products (NPs). To further enhance predictability, we have developed various other ML-based q-RASAR models. The insights from the developed model highlight the pivotal roles played by hydrophobicity, electronic effects, degree of ionization and steric factors as essential features facilitating the traversal of the blood-brain barrier. This research not only advances our understanding of the molecular determinants influencing the permeability of central nervous system drugs but also establishes a versatile computational platform for the rapid assessment of diverse compounds, facilitating informed decision-making in the realms of drug development and design.
DOI: 10.6084/m9.figshare.24221323
title: Djiofack et al 2024 GCB data

description: To increase our understanding of carbon and diversity dynamics in this savanna ecosystem, 101 permanent plots of 50 m by 80 m each with 20 m between the plots were installed in 2010 within the contiguous 88-ha fire exclusion area in Manzonzi (Kongo Central province, RD Congo). The total monitored area in 2010 was 40.4 ha with each plot being 0.4 ha (Deklerck et al., 2019). However, between 2010 and 2022, 14 plots were transformed back to agricultural land to meet some needs of the local community. Therefore, only the 87 undisturbed plots (comprising 34.8 ha) are considered in the inventory campaign of 2022.
DOI: 10.5281/zenodo.10812942
title: Meshing of Spiny Neuronal Morphologies using Union Operators

description: Resulting datasets of the conference paper "Meshing of Spiny Neuronal Morphologies using Union Operators". The paper is published in 2022 in EG UK Computer Graphics & Visual Computing (2022).

DOI: 10.2312/cgvc.20221168

The dataset consists of the resulting meshes of a set of exemplar neurons created using the union-operator-based meshing algorithm that is described in the paper.
DOI: 10.6084/m9.figshare.24221323.v1
title: Djiofack_et_al_data_Manzonzi.xlsx

description: To increase our understanding of carbon and diversity dynamics in this savanna ecosystem, 101 permanent plots of 50 m by 80 m each with 20 m between the plots were installed in 2010 within the contiguous 88-ha fire exclusion area in Manzonzi (Kongo Central province, RD Congo). The total monitored area in 2010 was 40.4 ha with each plot being 0.4 ha (Deklerck et al., 2019). However, between 2010 and 2022, 14 plots were transformed back to agricultural land to meet some needs of the local community. Therefore, only the 87 undisturbed plots (comprising 34.8 ha) are considered in the inventory campaign of 2022.
DOI: 10.5281/zenodo.10812941
title: Meshing of Spiny Neuronal Morphologies using Union Operators

description: Resulting datasets of the conference paper "Meshing of Spiny Neuronal Morphologies using Union Operators". The paper is published in 2022 in EG UK Computer Graphics & Visual Computing (2022).

DOI: 10.2312/cgvc.20221168

The dataset consists of the resulting meshes of a set of exemplar neurons created using the union-operator-based meshing algorithm that is described in the paper.
DOI: 10.5281/zenodo.10593056
title: Supplementary Data for "Detection of peptide segments protruding from the MHC-I binding groove"
DOI: 10.5281/zenodo.10593055
title: Supplementary Data for "Detection of peptide segments protruding from the MHC-I binding groove"
DOI: 10.5281/zenodo.10813067
title: THE ROLE OF MODERN EDUCATIONAL TECHNOLOGIES IN EFL/ESL CLASSROOM
DOI: 10.5281/zenodo.10813068
title: THE ROLE OF MODERN EDUCATIONAL TECHNOLOGIES IN EFL/ESL CLASSROOM
DOI: 10.5281/zenodo.10812365
title: Data and code used in "Adaptive expansion of ERVK solo-LTRs is associated with Passeriformes speciation events"

description: Data and code used in "Adaptive expansion of ERVK solo-LTRs is associated with Passeriformes speciation events"
DOI: 10.5281/zenodo.10522999
title: 16S rRNA Sequencing Data of Fecal Microbiota in an Italian Cohort of Patients with CDKL5 Deficiency Disorder

description: Summary of the study 

CDKL5 deficiency disorder (CDD) is a neurodevelopmental condition characterized by global developmental delay, early-onset seizures, intellectual disability, visual and motor impairments, distinct from Rett Syndrome (RTT) due to the absence of a clear regression period. Gastrointestinal (GI) disturbances and signs of subclinical immune dysregulation are common in CDD patients, yet the underlying causes are unknown. Recent studies hint at a possible link between neurological disorders and gut microbiota, an unexplored area in CDD. In this groundbreaking study, we examined fecal microbiota in CDD patients and their healthy relatives, revealing differences in bacterial diversity and composition. We further investigated microbiota changes based on various factors, including the severity of GI issues, seizure frequency, sleep disorders, food intake type, neuro-behavioral features (assessed through the RTT Behaviour Questionnaire – RSBQ), and ambulation capacity. 

Our findings suggest a potential connection between CDD, microbiota, and symptom severity. This study represents the first exploration of the gut-microbiota-brain axis in CDD patients, contributing to the growing body of research on the role of gut microbiota in neurodevelopmental disorders. It opens doors to potential interventions targeting intestinal microbes to enhance the well-being of individuals with CDD.

Mehods

The Dataset represent the raw data (.fastq) obtained from the sequencing of the fecal samples from 17 Italian Patients with CDD, and 17 Healthy Relatives (i.e. siblings or mother), collected at a single time-point.

Samples from Patients affected by CDD are called CDD, samples from Healthy Relatives are called HC-CDD (i.e. healthy controls of patients affected by CDD). For details about the sample names see the “Explanation Table”.

Bacterial DNA was extracted from fecal samples using the QIAmp Powerfexal DNA Kit (Qiagen, Germany) following the manufacturer's protocol. The 16S rRNA sequencing and analysis was performed by a service offered by Zymo Research (Germany).

Targeted Library Preparation: The DNA samples were prepared for targeted sequencing with the Quick-16S™ NGS Library Prep Kit (Zymo Research). The primer sets used were Quick-16S™ Primer Set V3-V4 (Zymo Research). The sequencing library was prepared using an innovative library preparation process in which PCR reactions were performed in real-time PCR machines to control cycles and therefore limit PCR chimera formation. The final PCR products were quantified with qPCR fluorescence readings and pooled together based on equal molarity. The final pooled library was cleaned up with the Select-a-Size DNA Clean & Concentrator™, then quantified with TapeStation® (Agilent Technologies, Santa Clara, CA) and Qubit® (Thermo Fisher Scientific, Waltham, WA).  

Sequencing: The final library was sequenced on Illumina® MiSeq™ with a v3 reagent kit (600 cycles).
DOI: 10.5281/zenodo.10523000
title: 16S rRNA Sequencing Data of Fecal Microbiota in an Italian Cohort of Patients with CDKL5 Deficiency Disorder

description: Summary of the study 

CDKL5 deficiency disorder (CDD) is a neurodevelopmental condition characterized by global developmental delay, early-onset seizures, intellectual disability, visual and motor impairments, distinct from Rett Syndrome (RTT) due to the absence of a clear regression period. Gastrointestinal (GI) disturbances and signs of subclinical immune dysregulation are common in CDD patients, yet the underlying causes are unknown. Recent studies hint at a possible link between neurological disorders and gut microbiota, an unexplored area in CDD. In this groundbreaking study, we examined fecal microbiota in CDD patients and their healthy relatives, revealing differences in bacterial diversity and composition. We further investigated microbiota changes based on various factors, including the severity of GI issues, seizure frequency, sleep disorders, food intake type, neuro-behavioral features (assessed through the RTT Behaviour Questionnaire – RSBQ), and ambulation capacity. 

Our findings suggest a potential connection between CDD, microbiota, and symptom severity. This study represents the first exploration of the gut-microbiota-brain axis in CDD patients, contributing to the growing body of research on the role of gut microbiota in neurodevelopmental disorders. It opens doors to potential interventions targeting intestinal microbes to enhance the well-being of individuals with CDD.

Mehods

The Dataset represent the raw data (.fastq) obtained from the sequencing of the fecal samples from 17 Italian Patients with CDD, and 17 Healthy Relatives (i.e. siblings or mother), collected at a single time-point.

Samples from Patients affected by CDD are called CDD, samples from Healthy Relatives are called HC-CDD (i.e. healthy controls of patients affected by CDD). For details about the sample names see the “Explanation Table”.

Bacterial DNA was extracted from fecal samples using the QIAmp Powerfexal DNA Kit (Qiagen, Germany) following the manufacturer's protocol. The 16S rRNA sequencing and analysis was performed by a service offered by Zymo Research (Germany).

Targeted Library Preparation: The DNA samples were prepared for targeted sequencing with the Quick-16S™ NGS Library Prep Kit (Zymo Research). The primer sets used were Quick-16S™ Primer Set V3-V4 (Zymo Research). The sequencing library was prepared using an innovative library preparation process in which PCR reactions were performed in real-time PCR machines to control cycles and therefore limit PCR chimera formation. The final PCR products were quantified with qPCR fluorescence readings and pooled together based on equal molarity. The final pooled library was cleaned up with the Select-a-Size DNA Clean & Concentrator™, then quantified with TapeStation® (Agilent Technologies, Santa Clara, CA) and Qubit® (Thermo Fisher Scientific, Waltham, WA).  

Sequencing: The final library was sequenced on Illumina® MiSeq™ with a v3 reagent kit (600 cycles).
DOI: 10.5281/zenodo.10803851
title: Data and code used in "Adaptive expansion of ERVK solo-LTRs is associated with Passeriformes speciation events"

description: Data and code used in "Adaptive expansion of ERVK solo-LTRs is associated with Passeriformes speciation events"
DOI: 10.5281/zenodo.10812325
title: test version 0.4

description: test version 0.4
DOI: 10.5281/zenodo.10813001
title: ПРИМЕНЕНИЕ МЕЖДУНАРОДНЫХ СТАНДАРТОВ ФИНАНСОВОЙ ОТЧЕТНОСТИ (МСФО) ПРИ ГРУППИРОВКЕ ЗАТРАТ
DOI: 10.5281/zenodo.10813002
title: ПРИМЕНЕНИЕ МЕЖДУНАРОДНЫХ СТАНДАРТОВ ФИНАНСОВОЙ ОТЧЕТНОСТИ (МСФО) ПРИ ГРУППИРОВКЕ ЗАТРАТ
DOI: 10.5281/zenodo.10812983
title: RECOVERY OF HIGH MOLECULAR HYDROCARBONS FROM SYNTHESIS GAS AND PHYSICO-CHEMICAL CHARACTERISTICS OF THE CATALYST
DOI: 10.5281/zenodo.10812984
title: RECOVERY OF HIGH MOLECULAR HYDROCARBONS FROM SYNTHESIS GAS AND PHYSICO-CHEMICAL CHARACTERISTICS OF THE CATALYST
DOI: 10.5281/zenodo.10822273
title: EpiMapper - CUT&Tag FASTQ
DOI: 10.5281/zenodo.10822274
title: EpiMapper - CUT&Tag FASTQ
DOI: 10.5281/zenodo.10822271
title: Complementary Data of Groundwater model for the Publication: "Comparison of methods to calculate groundwater recharge for karst aquifers under Mediterranean climate"

description: This repository provides the resources related to the publication "Comparison of methods to calculate groundwater recharge for karst aquifers under Mediterranean climate." It includes the groundwater model data sets.
description: This work was supported by the German Federal Ministry of Education and Research (BMBF) under grant numbers 02WGR1428C and 02WM1533A. The authors would like to express their appreciation to the partners of the MedWater project. Additionally, the authors extend their gratitude to Muath Abusaada and Flann Corcoran for providing aquifer data and a primary Modflow model. This publication was made possible with the support of the Open Access Publication Funds by the Technical University of Berlin.
DOI: 10.5281/zenodo.10822348
title: EpiMapper - CUT&Tag Demo - Required files
DOI: 10.5281/zenodo.10822349
title: EpiMapper - CUT&Tag Demo - Required files