cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10519123
title: TDAH Repository

description: The dataset provides data obtained with eye-tracking while 55 volunteers solved 4 distinct neuropsychological tests on a screen inside a closed room. Among the 55 volunteers, 22 were women and 33 were men, all with ages ranging between 9 and 50, and 5 of whom were diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) [1]. The eye-tracker used for the collection of the data was an EyeTribe, which has a sampling rate of 60 Hz and an average visual angle between 0.5 and 1, which correspond to an on-screen error between 0.5 and 1cm (0.1969 to 0.393 inches aprox) respectively, when the distance to the user is around 60cm (23.62 in) [2], which was the case during the collection of these data. The neuropsychological tests were implemented in a software named NEURO-INNOVA KIDS® [3], which are the following: a domino test adapted from the D-48 intelligence test [4], an adaptation of the MASMI test consisting of unfolded cubes [5], the figures series completion test adapted from [6], and the Poppelreuter figures test [7]. Before each of the tests, a calibration process was performed, ensuring that the visual angle error was less than or equal to 0.5 cm (0.1969 in), which is considered an acceptable calibration. The collective mean duration of the four administered tests amounted to 20 minutes. This dataset exhibits significant promise for potential utilization due to the extensive prevalence of these neuropsychological assessments among healthcare practitioners for evaluating diverse cognitive faculties in individuals. Moreover, it has been empirically established that poor performance on these tests is associated with attention deficits [8].
DOI: 10.5281/zenodo.10519124
title: TDAH Repository

description: The dataset provides data obtained with eye-tracking while 55 volunteers solved 4 distinct neuropsychological tests on a screen inside a closed room. Among the 55 volunteers, 22 were women and 33 were men, all with ages ranging between 9 and 50, and 5 of whom were diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) [1]. The eye-tracker used for the collection of the data was an EyeTribe, which has a sampling rate of 60 Hz and an average visual angle between 0.5 and 1, which correspond to an on-screen error between 0.5 and 1cm (0.1969 to 0.393 inches aprox) respectively, when the distance to the user is around 60cm (23.62 in) [2], which was the case during the collection of these data. The neuropsychological tests were implemented in a software named NEURO-INNOVA KIDS® [3], which are the following: a domino test adapted from the D-48 intelligence test [4], an adaptation of the MASMI test consisting of unfolded cubes [5], the figures series completion test adapted from [6], and the Poppelreuter figures test [7]. Before each of the tests, a calibration process was performed, ensuring that the visual angle error was less than or equal to 0.5 cm (0.1969 in), which is considered an acceptable calibration. The collective mean duration of the four administered tests amounted to 20 minutes. This dataset exhibits significant promise for potential utilization due to the extensive prevalence of these neuropsychological assessments among healthcare practitioners for evaluating diverse cognitive faculties in individuals. Moreover, it has been empirically established that poor performance on these tests is associated with attention deficits [8].
DOI: 10.6084/m9.figshare.24635172
title: FIA49k_2d_descriptors_selection.json

description: All 2D molecular descriptors after the first and second round of feature selection.
DOI: 10.6084/m9.figshare.24635181
title: FIA49k_fia_gnn_embeddings.csv.gz

description: All extracted latent space vectors of the two GNN's of FIA-GNN.
DOI: 10.6084/m9.figshare.24635181.v1
title: FIA49k_fia_gnn_embeddings.csv.gz

description: All extracted latent space vectors of the two GNN's of FIA-GNN.
DOI: 10.6084/m9.figshare.24635187
title: FIA49k_predictions.csv.gz

description: All predicted FIA values (FIA-GNN).
DOI: 10.6084/m9.figshare.24635187.v1
title: FIA49k_predictions.csv.gz

description: All predicted FIA values (FIA-GNN).
DOI: 10.6084/m9.figshare.24635163
title: FIA49k_2d_descriptors.csv

description: All calculated 2D molecular descriptors (RDKit and mordred).
DOI: 10.6084/m9.figshare.24635163.v1
title: FIA49k_2d_descriptors.csv

description: All calculated 2D molecular descriptors (RDKit and mordred).
DOI: 10.6084/m9.figshare.24635157
title: FIA49k.zip

description: All xyz structures.
DOI: 10.6084/m9.figshare.24635157.v1
title: FIA49k.zip

description: All xyz structures.
DOI: 10.6084/m9.figshare.24635136
title: FIA49k.csv.gz

description: All extracted quantum chemical features, FIA values, and further molecular information (e.g., SMILES string, central atom class).
DOI: 10.5281/zenodo.10518961
title: A demo fluorescence dataset in different formats

description: The 4 files contain the same dataset in 4 different formats:



Data_POMC.fits (FITS format).

Data_POMC;json (JSON format with data as nested arrays suitable for direct import in Python).

Data_POMC2.json (JSON format with "flattened" array).

Data_POMC.py (a Python module containing a description and a single variable 'stack' with the data as a 3D NumPy array).


The data are POMC neuron image stack. The CCD chip size (after binning) is 60x80 and 168 fluorescence images were taken. The fluorophore used was Fura-2. Fluorescence images were acquired at 340 nm every 150 ms (exposure time: 12 ms). The imaging setup consisted of an Imago SensiCam CCD camera with a 640x480 chip (Till Photonics, Graefelfing, Germany) and a Polychromator IV (Till Photonics) that was coupled via an optical fiber into the upright microscope. Emitted fluorescence was detected through a 440 nm long-pass filter (LP440). Data  were acquired as 80x60 frames using 8x8 on-chip binning. Images were recorded in analog-to-digital units (ADUs) and stored as 12-bit grayscale images. A depolarizing currrent pulse was applied just before frame 13 provoking calcium entry. The data were acquired by Andreas Pippow.Reference: JOUCLA ET AL. (2013) CELL CALCIUM. 54(2):71-85

To read Data_POMC.fits into a Python session do:



import fitsioimport numpy as np fits = fitsio.FITS('Data_POMC.fits','r')fits


To read Data_POMC.py into a Python session do:



import Data_POMChelp(Data_POMC)


To read Data_POMC.json into a Python session do:



import jsonimport numpy as npwith open("Data_POMC.json","r") as f:    pomc = json.load(f) # pomc is a dictionarypomc_stack = np.array(pomc['stack'])print(pomc['metadata'])


To read Data_POMC2.json into a Python session do:



import jsonimport numpy as npwith open("Data_POMC2.json","r") as f:    pomc = json.load(f) # pomc is a dictionarypomc_stack = np.reshape(pomc['stack'],(60,80,168),order='f')print(pomc['metadata'])
DOI: 10.5281/zenodo.10518962
title: A demo fluorescence dataset in different formats

description: The 4 files contain the same dataset in 4 different formats:



Data_POMC.fits (FITS format).

Data_POMC;json (JSON format with data as nested arrays suitable for direct import in Python).

Data_POMC2.json (JSON format with "flattened" array).

Data_POMC.py (a Python module containing a description and a single variable 'stack' with the data as a 3D NumPy array).


The data are POMC neuron image stack. The CCD chip size (after binning) is 60x80 and 168 fluorescence images were taken. The fluorophore used was Fura-2. Fluorescence images were acquired at 340 nm every 150 ms (exposure time: 12 ms). The imaging setup consisted of an Imago SensiCam CCD camera with a 640x480 chip (Till Photonics, Graefelfing, Germany) and a Polychromator IV (Till Photonics) that was coupled via an optical fiber into the upright microscope. Emitted fluorescence was detected through a 440 nm long-pass filter (LP440). Data  were acquired as 80x60 frames using 8x8 on-chip binning. Images were recorded in analog-to-digital units (ADUs) and stored as 12-bit grayscale images. A depolarizing currrent pulse was applied just before frame 13 provoking calcium entry. The data were acquired by Andreas Pippow.Reference: JOUCLA ET AL. (2013) CELL CALCIUM. 54(2):71-85

To read Data_POMC.fits into a Python session do:



import fitsioimport numpy as np fits = fitsio.FITS('Data_POMC.fits','r')fits


To read Data_POMC.py into a Python session do:



import Data_POMChelp(Data_POMC)


To read Data_POMC.json into a Python session do:



import jsonimport numpy as npwith open("Data_POMC.json","r") as f:    pomc = json.load(f) # pomc is a dictionarypomc_stack = np.array(pomc['stack'])print(pomc['metadata'])


To read Data_POMC2.json into a Python session do:



import jsonimport numpy as npwith open("Data_POMC2.json","r") as f:    pomc = json.load(f) # pomc is a dictionarypomc_stack = np.reshape(pomc['stack'],(60,80,168),order='f')print(pomc['metadata'])
DOI: 10.6084/m9.figshare.23507517
title: ContScout supporting data

description: Script and supporting data for Balint et al "ContScout: Sensitive detection and removal of contamination from annotated genomes"
DOI: 10.6084/m9.figshare.23507517.v2
title: ContScout supporting data

description: Supporting data for Balint et al "Sensitive detection and removal of contamination from annotated genomes by ContScout"
DOI: 10.5519/qd.rdmcqe5h
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5066/p94ihw4x
title: Hawaiian Islands downscaled climate projections for baseline (1983-2012), mid- (2040-2059), and late-century (2060-2079) scenarios

description: Global downscaled projections are now some of the most widely used climate datasets in the world, however, they are rarely examined for representativeness of local climate or the plausibility of their projected changes. Here we apply steps to improve the utility of two such global datasets (CHELSA and WorldClim2) to provide credible climate scenarios for climate change impact studies in Hawaii. Our approach is based on three steps: 1) Using a standardized baseline period, comparing available global downscaled projections with regional observation-based datasets and regional downscaled datasets (if available); 2) bias correcting projections using observation-based data; and 3) creating ensembles to make use of the differential strengths [...]
DOI: 10.3886/e197681v1
title: Data for: Analysis of influencing factors of review efficiency of multidisciplinary scientific research projects based on improved DEMATEL model

description: In this study, we introduced a systematic indexing framework to identify factors influencing the appraisal of multidisciplinary efforts. This framework is further enriched by expert-driven questionnaires, capturing domain-specific insights to determine the significance and interplay of these factors. Employing an improved Decision-Making Trial and Evaluation Laboratory (DEMATEL) model, the study distills this data to pinpoint key influencing factors that can bolster review efficiency for multidisciplinary projects. The culmination of these efforts aims to provide pragmatic strategies and policy guidance, arming institutional entities and program leaders with the requisite tools to optimize the review process for multidisciplinary scientific research projects.
DOI: 10.3886/e197681
title: Data for: Analysis of influencing factors of review efficiency of multidisciplinary scientific research projects based on improved DEMATEL model

description: In this study, we introduced a systematic indexing framework to identify factors influencing the appraisal of multidisciplinary efforts. This framework is further enriched by expert-driven questionnaires, capturing domain-specific insights to determine the significance and interplay of these factors. Employing an improved Decision-Making Trial and Evaluation Laboratory (DEMATEL) model, the study distills this data to pinpoint key influencing factors that can bolster review efficiency for multidisciplinary projects. The culmination of these efforts aims to provide pragmatic strategies and policy guidance, arming institutional entities and program leaders with the requisite tools to optimize the review process for multidisciplinary scientific research projects.
DOI: 10.3886/e197681v2
title: Data for: Analysis of influencing factors of review efficiency of multidisciplinary scientific research projects based on improved DEMATEL model

description: In this study, we introduced a systematic indexing framework to identify factors influencing the appraisal of multidisciplinary efforts. This framework is further enriched by expert-driven questionnaires, capturing domain-specific insights to determine the significance and interplay of these factors. Employing an improved Decision-Making Trial and Evaluation Laboratory (DEMATEL) model, the study distills this data to pinpoint key influencing factors that can bolster review efficiency for multidisciplinary projects. The culmination of these efforts aims to provide pragmatic strategies and policy guidance, arming institutional entities and program leaders with the requisite tools to optimize the review process for multidisciplinary scientific research projects.
DOI: 10.34740/kaggle/dsv/7414190
title: Sentiment Analysis - Labelled Financial News Data
DOI: 10.14473/csda/bsrgev/aeximj
title: TI_CSDA00243.pdf

description: Technická informace
DOI: 10.14473/csda/bsrgev/p9spbb
title: D_CSDA00243.pdf

description: Dotazník
DOI: 10.14473/csda/bsrgev
title: Ženy a muži v ČR: životní dráhy a mezigenerační vztahy 2008

description: Generations and Gender Survey (v ČR pod názvem „Ženy a muži v ČR: životní dráhy a mezigenerační vztahy“) je mezinárodní panelové šetření zaměřené primárně na partnerské a mezigenerační vztahy. GGS navazuje na starší Fertility and Family Survey. Účelem výzkumu je sledování partnerských a rodinných drah v čase; zájmem jsou životní dráhy jednotlivců a rodinná dynamika. Alespoň jedné vlny šetření se dosud zúčastnilo 19 zemí. První vlna dat byla v ČR sbírána v roce 2005, druhá v roce 2008. Česká i mezinárodní panelová data jsou volně dostupná na stránkách projektu ().
DOI: 10.14473/csda/bsrgev/hn8u98
title: ASORA_120310_verze7_4_publikace.tab

description: Data
DOI: 10.18112/openneuro.ds004906.v2.1.2
title: rf_shimming_spinalcord
DOI: 10.6084/m9.figshare.24958731
title: <b>Differential global gene expression in adipose tissue of nappers and non-nappers</b>

description: Raw expression data of those genes differentially down or up expressed in adipose tissue of nappers vs. non-nappers.
DOI: 10.6084/m9.figshare.24958731.v1
title: <b>Differential global gene expression in adipose tissue of nappers and non-nappers</b>

description: Raw expression data of those genes differentially down or up expressed in adipose tissue of nappers vs. non-nappers.
DOI: 10.57745/5ftcul
title: Protocole_Suivi_Dendrometrique_Phenologique_Pin_Maritime_BouleauVerruqueux.pdf

description: Protocole expérimental du suivi dendrométrique et phénologique du Pin maritime et du Bouleau verruqueux dans le dispositif d'Orphée
DOI: 10.57745/j6ltw1
title: Releve_Suivi_Dendrometrique_Phenologique_Pin_Maritime_BouleauVerruqueux_2017_2018.xlsx

description: Fichier de mesure de dendrométrie et phénologie sur le pin maritime et le bouleau verruqueux sur 2 ans (2017 et 2018)
DOI: 10.6084/m9.figshare.25003526.v1
title: Involvement of Microglia-Expressed MS4A6A in the Onset of Glioblastoma(Raw Data)

description: Glioblastoma multiforme (GBM) represents the deadliest form of brain tumor, characterized by its low survival rate and grim prognosis. Cytokines released from glioma-associated microglia/macrophages are involving in establishing tumor microenvironment, thereby crucially promoting GBM progression.<i> MS4A6A</i> polymorphism was confirmed to be associated with neurodegenerative and polymorphisms disease pathobiology, but whether it participates in the regulation of GBM and the underlying mechanisms are still not elucidated. Here, we found that MS4A6A was significantly upregulated in GBM patient samples. The results from single-cell RNA-sequencing (scRNA-seq) database and immunostaining demonstrated the specific expression of MS4A6A in microglial cells. In vitro, microglial overexpression of MS4A6A stimulated the proliferation and migration of glioblastoma cells. Moreover, high<i> MS4A6A </i>mRNA expression was related with poor prognosis in GBM patients. Our study highlights the potential of MS4A6A as a promising biomarker for GBM, which may provide novel strategies for its prevention, diagnosis and treatment.
DOI: 10.6084/m9.figshare.25003526
title: Involvement of Microglia-Expressed MS4A6A in the Onset of Glioblastoma(Raw Data)

description: Glioblastoma multiforme (GBM) represents the deadliest form of brain tumor, characterized by its low survival rate and grim prognosis. Cytokines released from glioma-associated microglia/macrophages are involving in establishing tumor microenvironment, thereby crucially promoting GBM progression.<i> MS4A6A</i> polymorphism was confirmed to be associated with neurodegenerative and polymorphisms disease pathobiology, but whether it participates in the regulation of GBM and the underlying mechanisms are still not elucidated. Here, we found that MS4A6A was significantly upregulated in GBM patient samples. The results from single-cell RNA-sequencing (scRNA-seq) database and immunostaining demonstrated the specific expression of MS4A6A in microglial cells. In vitro, microglial overexpression of MS4A6A stimulated the proliferation and migration of glioblastoma cells. Moreover, high<i> MS4A6A </i>mRNA expression was related with poor prognosis in GBM patients. Our study highlights the potential of MS4A6A as a promising biomarker for GBM, which may provide novel strategies for its prevention, diagnosis and treatment.
DOI: 10.6084/m9.figshare.25003823
title: urban data

description: building layout and road network in Zhengzhou city
DOI: 10.6084/m9.figshare.25004552
title: 3300028067_4.xlsx

description: BLASTP results of all contigs in the MAG 3300028067-4
DOI: 10.6084/m9.figshare.25004552.v1
title: 3300028067_4.xlsx

description: BLASTP results of all contigs in the MAG 3300028067-4
DOI: 10.14473/csda/klfyip/c1nwwr
title: D_CSDA00244.pdf

description: Dotazník
DOI: 10.14473/csda/klfyip/kbpevj
title: TI_CSDA00244.pdf

description: Technická informace
DOI: 10.14473/csda/klfyip
title: Kontinuita a diskontinuity v náboženské paměti v České republice 2015 – hlavní výzkum (dospělá populace)

description: Společnost ppm factum research s.r.o. realizovala pro zadavatele Západočeskou univerzitu v Plzni sběr dat pro projekt „Kontinuita a diskontinuity v náboženské paměti v České republice“. Úkolem společnosti ppm factum research s.r.o. bylo především připravit výběrovou metodu pro adresný náhodný výběr, provést terénní sběr dat a kontrolu práce tazatelů, to vše v souladu se zadávací dokumentací zadavatele (ZČU) a nabídkou, kterou společnost jako uchazeč o veřejnou zakázku zadavateli předložila. Projekt se skládá z 2 částí. Část A je zaměřena na obecnou populaci ČR a část B pak na rodinné příslušníky respondentů oslovených v části A šetření. Část A: · Cílová skupina: Obecná populace ČR 18+ let · Požadovaná velikost vzorku: n=1000 osob · Výběrová metoda: Stratifikovaný náhodný výběr s oporou adres · Metoda sběru dat: Osobní dotazování s pomocí počítače (CAPI) Část B: · Cílová skupina: Děti respondentů starší 18 let nebo rodiče respondentů z části A · Požadovaná velikost vzorku: n=300 osob · Výběrová metoda: Prostý náhodný výběr s oporou kontaktů z databáze · Metoda sběru dat: Osobní dotazování s pomocí počítače (CAPI)
DOI: 10.14473/csda/klfyip/2rasia
title: ZČU_ppm_náboženství_SPSS_část_A_2015.tab

description: Data
DOI: 10.25387/g3.24076638
title: Supplemental Material for Pozo et al., 2023

description: The raw reads, assembly and annotation obtained after sequencing one individual of<em> Ateles fusciceps fusciceps</em> using a MinION Mk1C with 9.4 flow cells. 
<br>
 
The raw reads are Q7 or higher and have been concatenated. They are in fastq format.  File has been divided into 6 parts (Part A through Part F).
 
The assembly was obtained using Flye, aligned to reference (Accession GCA_023783555.1) with Minimap2 and polished with Medaka. It is in fasta format.  
 
The annotation was obtained using Maker and is in gff format.
DOI: 10.25387/g3.24076638.v1
title: Supplemental Material for Pozo et al., 2023

description: The raw reads, assembly and annotation obtained after sequencing one individual of<em> Ateles fusciceps fusciceps</em> using a MinION Mk1C with 9.4 flow cells. 
<br>
 
The raw reads are Q7 or higher and have been concatenated. They are in fastq format.  File has been divided into 6 parts (Part A through Part F).
 
The assembly was obtained using Flye, aligned to reference (Accession GCA_023783555.1) with Minimap2 and polished with Medaka. It is in fasta format.  
 
The annotation was obtained using Maker and is in gff format.
DOI: 10.6084/m9.figshare.25004558.v1
title: 3300028083_5.xlsx

description: BLASTP results of all contigs in 3300028083-5
DOI: 10.6084/m9.figshare.25004558
title: 3300028083_5.xlsx

description: BLASTP results of all contigs in 3300028083-5
DOI: 10.6084/m9.figshare.25004465
title: 3300027627_27.xlsx

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004465.v1
title: 3300027627_27.xlsx

description: BLASTP results of all contigs
DOI: 10.17637/rh.14823768
title: LIFE Database.xlsx

description: The full Livelihood Fire Database (LIFE), a global database of contemporary subsistence-oriented and smallholder fire use and mitigation practices in over 580 case study locations described in the literature.
DOI: 10.17637/rh.14823768.v4
title: LIFE Database.xlsx

description: The full Livelihood Fire Database (LIFE), a global database of contemporary subsistence-oriented and smallholder fire use and mitigation practices in over 580 case study locations described in the literature.
DOI: 10.17637/rh.14823768.v3
title: LIFE Database.xlsx

description: The full Livelihood Fire Database (LIFE), a global database of contemporary subsistence-oriented and smallholder fire use and mitigation practices in over 580 case study locations described in the literature.
DOI: 10.6084/m9.figshare.25003499.v1
title: 语言法的数据和法典

description: 该项目包含有关白头叶猴的语法数据、分析和绘图代码
DOI: 10.6084/m9.figshare.25004063
title: 3300028086_8.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004063.v1
title: 3300028086_8.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004306
title: 3300024494_6.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004306.v1
title: 3300024494_6.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004501
title: 3300028071_10.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004501.v1
title: 3300028071_10.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004531.v1
title: 3300024512_4.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004543
title: 3300024282_7.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004543.v1
title: 3300024282_7.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004066.v1
title: 3300028258_10.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004075
title: 3300028265_4.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004075.v1
title: 3300028265_4.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004531
title: 3300024512_4.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs
DOI: 10.6084/m9.figshare.25004066
title: 3300028258_10.xlsxBLASTP results of all contigs

description: BLASTP results of all contigs