cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.26777800
title: Additional file 14 of Overlooked poor-quality patient samples in sequencing data impair reproducibility of published clinically relevant datasets

description: Additional file 14: Table S13. Quality Features and Plow values as input and output by seqQscorer respectively. Column prefixes designate types of quality features as follows. BowtieMI: mapping metrics from bowtie2 ran on the first fastq file for paired reads to allow comparison to single read data as used by seqQscorer. FASTQC: ordinal outputs of FastQC. TSS: percentage of counted reads in bins around transcription start sites (TSS) with relative bin position in base pairs given in full feature name. ReadsAnno: percentage of sequencing reads in different types of genomic regions described in full feature name. Accession: SRR run accession number. Dataset: GEO accession number. Plow: low-quality probability predicted by the seqQscorer tool.
DOI: 10.6084/m9.figshare.26777791
title: Additional file 11 of Overlooked poor-quality patient samples in sequencing data impair reproducibility of published clinically relevant datasets

description: Additional file 11: Table S10. Overlapping Pathways enriched in High-Quality Markers in RNA- and ChIP-seq Datasets.
DOI: 10.6084/m9.figshare.26777629.v1
title: Additional file 1 of Clinical and genetic characteristics of Chinese patients with congenital fibrosis of the extraocular muscles

description: Supplementary Material 1
DOI: 10.6084/m9.figshare.26777770.v1
title: Additional file 4 of Overlooked poor-quality patient samples in sequencing data impair reproducibility of published clinically relevant datasets

description: Additional file 4: Table S3. Low-Quality Marker Genes. Column N: number of datasets out of 13 in which the gene expression positively correlated with a machine-learning derived low-quality probability. Each of the 13 datasets represented a different disease and had a low-quality imbalance between control and patient groups of samples.
DOI: 10.6084/m9.figshare.26777953
title: Additional file 3 of What evidence exists on the effect of the main European lowland crop and grassland management practices on biodiversity indicator species groups? a systematic map

description: Additional file 3. Inclusion/exclusion criteria
DOI: 10.6084/m9.figshare.26777767.v1
title: Additional file 3 of Overlooked poor-quality patient samples in sequencing data impair reproducibility of published clinically relevant datasets

description: Additional file 3: Table S2. Datasets Metadata ChIP-seq.
DOI: 10.6084/m9.figshare.26777815.v1
title: Additional file 6 of Sexually dimorphic DNA methylation and gene expression patterns in human first trimester placenta

description: Additional file 6. Total RNA-seq sex differences in first trimester human placenta, adjusted for two sequencing runs. DESeq2 results annotated with Ensembl release 91. Sex and batch details are included for each subject.
DOI: 10.6084/m9.figshare.26777581.v1
title: Additional file 3 of Where is dinner? The spatiality of the trophic niche of terrestrial mammalian carnivores in Chile, a systematization for their conservation

description: Supplementary Material 3
DOI: 10.5281/zenodo.13340228
title: agama_doriae_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.13340229
title: agama_doriae_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.25946/26776417.v1
title: Mango DMC and NIR spectra

description: The dry matter content of mango fruit is an important metric for determining harvest maturity and ensuring the eating quality of the ripened fruit. Near infrared spectroscopy can be used as a non-invasive method of estimating attributes of individual fruit, including dry matter content. The technique relies on statistic models (‘chemometrics’) to deduce information on sample attributes from spectra collected from the fruit. Barriers to the adoption of this technique for practical use in the fruit industry include the robustness of models across fruit from different growing conditions and spectra collected on different instruments. The proposed research is intended to reduce these barriers for the assessment of mango dry matter content by exploring new techniques for developing robust, global models across season, growing conditions, fruit variety, individual instruments and other variations. This would allow new instruments to be used ‘out of the box’ without the need for local calibration, hence greatly reducing the cost of uptake. Deep learning modelling techniques have been recently applied to spectroscopic applications, with claims of improved performance over the standard chemometric method, Partial Least Squares Regression, although these studies have typically involved relatively small datasets with limited testing on new populations of data. With access to an extended dataset of over 80,000 spectra from over 500 fruit populations, the aim of this proposed study is to validate previous publication claims that the use of a Convolutional Neural Network (CNN) model, a deep learning technique, is superior to existing methods in NIRS based prediction of mango dry matter content. The study also aims to optimise the operation and the architecture of the CNN model over that employed in previous publications, in context of the mango dry matter data set and use on a portable instrument.
DOI: 10.6084/m9.figshare.26777776.v1
title: Additional file 6 of Overlooked poor-quality patient samples in sequencing data impair reproducibility of published clinically relevant datasets

description: Additional file 6: Table S5. High-Quality Marker Genes. Column N: number of datasets out of 13 in which the gene expression negatively correlated with a machine-learning derived low-quality probability. Each of the 13 datasets represented a different disease and had a low-quality imbalance between control and patient groups of samples.
DOI: 10.6084/m9.figshare.26768405
title: Additional file 2 of Emergency infection prevention and control training in fragile, conflict-affected or vulnerable settings: a scoping review

description: Supplementary Material 2.
DOI: 10.6084/m9.figshare.26777614
title: Additional file 6 of Social environment influences microbiota and potentially pathogenic bacterial communities on the skin of developing birds

description: Additional file 6.
DOI: 10.6084/m9.figshare.26777773.v1
title: Additional file 5 of Overlooked poor-quality patient samples in sequencing data impair reproducibility of published clinically relevant datasets

description: Additional file 5: Table S4. Regulators of the Low-Quality Marker Genes. This table shows statistically significant molecular regulators of the top 500 low-quality marker genes derived by an enrichment analysis (transcription factors or miRNAs). P-value was derived from a Fisher’s exact test and adjusted by the Bonferroni method.
DOI: 10.6084/m9.figshare.26777797
title: Additional file 13 of Overlooked poor-quality patient samples in sequencing data impair reproducibility of published clinically relevant datasets

description: Additional file 13: Table S12. Relevance of pathways enrichment after quality imbalance mitigation. Mitigation of quality imbalance was performed either within the differential analysis (using low-quality probabilities of the samples, Plow values, as covariates in DESeq2) or by removing quality outlier samples (based on Plow values). First, pathways associated with low-quality markers (low-quality pathways) were derived for various reference gene lists of the MSigDB database (column gene_sets). For each dataset, a gene set enrichment analysis was performed on differential genes (differential pathways) with a positive or negative log-fold change (pos and neg values in column deg_sign, respectively). We summarized results only for datasets for which at least 1 low-quality pathway overlaps with differential pathways (n_datasets) and counted the number of datasets where the mitigation method had positive (column better), negative (column worse), or no substantial impact (column no_change).
DOI: 10.6084/m9.figshare.26777605
title: Additional file 3 of Social environment influences microbiota and potentially pathogenic bacterial communities on the skin of developing birds

description: Additional file 3.
DOI: 10.6084/m9.figshare.26777608
title: Additional file 4 of Social environment influences microbiota and potentially pathogenic bacterial communities on the skin of developing birds

description: Additional file 4.
DOI: 10.13145/bacdive12555.20241212.9.2
title: Hoylesella nanceiensis

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12556.20241212.9.2
title: Hoylesella timonensis Glazunova et al. 2007) Hitch et al. 2023

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12557.20241212.9.2
title: Segatella maculosa (Downes et al. 2007) Hitch et al. 2023

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12558.20241212.9.2
title: Segatella paludivivens (Ueki et al. 2007) Hitch et al. 2023

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12560.20241212.9.2
title: Prevotella histicola Downes et al. 2008 emend. Hahnke et al. 2016

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12562.20241212.9.2
title: Prevotella amnii Lawson et al. 2008 emend. Hahnke et al. 2016

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12563.20241212.9.2
title: Prevotella falsenii Sakamoto et al. 2009

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12564.20241212.9.2
title: Prevotella micans Downes et al. 2009

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12565.20241212.9.2
title: Prevotella dentasini Takada et al. 2010

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12566.20241212.9.2
title: Hoylesella saccharolytica (Downes et al. 2010) Hitch et al. 2023

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12567.20241212.9.2
title: Prevotella fusca Downes and Wade 2011

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12568.20241212.9.2
title: Prevotella scopos Downes and Wade 2011

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12569.20241212.9.2
title: Paraprevotella clara Morotomi et al. 2009

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12570.20241212.9.2
title: Paraprevotella xylaniphila Morotomi et al. 2009

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12572.20241212.9.2
title: Cellulosimicrobium cellulans (Metcalfe and Brown 1957) Schumann et al. 2001

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12574.20241212.9.2
title: Cellulosimicrobium cellulans (Metcalfe and Brown 1957) Schumann et al. 2001

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12575.20241212.9.2
title: Cellulosimicrobium cellulans (Metcalfe and Brown 1957) Schumann et al. 2001

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12576.20241212.9.2
title: Cellulosimicrobium cellulans (Metcalfe and Brown 1957) Schumann et al. 2001

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12578.20241212.9.2
title: Cellulosimicrobium cellulans (Metcalfe and Brown 1957) Schumann et al. 2001

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12580.20241212.9.2
title: Cellulosimicrobium cellulans (Metcalfe and Brown 1957) Schumann et al. 2001

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12581.20241212.9.2
title: Cellulosimicrobium funkei Brown et al. 2006

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12582.20241212.9.2
title: Cellulosimicrobium terreum Yoon et al. 2007

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12583.20241212.9.2
title: Isoptericola variabilis (Bakalidou et al. 2002) Stackebrandt et al. 2004

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12584.20241212.9.2
title: Isoptericola halotolerans Zhang et al. 2005

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12585.20241212.9.2
title: Isoptericola hypogeus Groth et al. 2005

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12586.20241212.9.2
title: Isoptericola jiangsuensis Wu et al. 2010 emend. Nouioui et al. 2018

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive12588.20241212.9.2
title: Myceligenerans xiligouense Cui et al. 2004

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15468/dl.8hwdrb
title: Occurrence Download

description: A dataset containing 909 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is South American Antarctic Marine Biodiversity Literature"
 ]
}

 The dataset includes 909 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017467-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29413154
title: Supplementary Material for: A SYSTEMATIC REVIEW AND META-ANALYSIS ON SEDIMENT CYTOLOGY: AN ADJUNCT TO EARLY DIAGNOSIS

description: Introduction: Sediment cytology, initially applied in urinary diagnostics, has expanded to formalin-fixed biopsy specimens, evaluating exfoliated cells suspended in formalin fixative. Though traditionally discarded, this sediment contains diagnostic material, especially in friable or malignant tissues. It offers a rapid, cost-effective, preliminary diagnostic tool when histopathology is delayed, such as in bone lesions requiring decalcification. This systematic review aimed to comprehensively assess the diagnostic accuracy, applicability, and limitations of sediment cytology across diverse anatomical sites.
Methods: This systematic review followed PRISMA guidelines, and an electronic search was conducted across PubMed, Scopus, and Google Scholar databases until January 1, 2025, without year restrictions. Keywords included “Sediment cytology”, “oral neoplasms”, “bone lesions”, and related terms. Articles in English reporting sediment cytology on formalin-fixed biopsies with cytological-histological correlation were included.
Results: Nine eligible studies were identified, involving various lesions including OSCC, OPMDs, bone lesions, GIT, cervical and ovarian neoplasms. Pooled sensitivity and specificity were 73.3% and 92.1% respectively. Subgroup analysis revealed excellent concordance for OSCC and bone malignancies. Sensitivity was lower in benign lesions and OPMDs, attributed to poor cellularity and exfoliation. Methodological heterogeneity was noted, though overall bias remained low.
Conclusion: Sediment cytology shows valuable diagnostic potential as a preliminary adjunct to histopathology, particularly for malignancies, warranting further standardized multicenter studies.
DOI: 10.6084/m9.figshare.29413154.v1
title: Supplementary Material for: A SYSTEMATIC REVIEW AND META-ANALYSIS ON SEDIMENT CYTOLOGY: AN ADJUNCT TO EARLY DIAGNOSIS

description: Introduction: Sediment cytology, initially applied in urinary diagnostics, has expanded to formalin-fixed biopsy specimens, evaluating exfoliated cells suspended in formalin fixative. Though traditionally discarded, this sediment contains diagnostic material, especially in friable or malignant tissues. It offers a rapid, cost-effective, preliminary diagnostic tool when histopathology is delayed, such as in bone lesions requiring decalcification. This systematic review aimed to comprehensively assess the diagnostic accuracy, applicability, and limitations of sediment cytology across diverse anatomical sites.
Methods: This systematic review followed PRISMA guidelines, and an electronic search was conducted across PubMed, Scopus, and Google Scholar databases until January 1, 2025, without year restrictions. Keywords included “Sediment cytology”, “oral neoplasms”, “bone lesions”, and related terms. Articles in English reporting sediment cytology on formalin-fixed biopsies with cytological-histological correlation were included.
Results: Nine eligible studies were identified, involving various lesions including OSCC, OPMDs, bone lesions, GIT, cervical and ovarian neoplasms. Pooled sensitivity and specificity were 73.3% and 92.1% respectively. Subgroup analysis revealed excellent concordance for OSCC and bone malignancies. Sensitivity was lower in benign lesions and OPMDs, attributed to poor cellularity and exfoliation. Methodological heterogeneity was noted, though overall bias remained low.
Conclusion: Sediment cytology shows valuable diagnostic potential as a preliminary adjunct to histopathology, particularly for malignancies, warranting further standardized multicenter studies.
DOI: 10.15468/dl.r68xyy
title: Occurrence Download

description: A dataset containing 55 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ficocyba ficaria (Horváth, 1897)"
 ]
}

 The dataset includes 55 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069798-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.394rp2
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Eumunida funambulus Gordon, 1930"
 ]
}

 The dataset includes 25 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0119056-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.msf85c
title: Occurrence Download

description: A dataset containing 18475 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "Continent is Europe",
 "Year is greater than 1978",
 "Year is less than 2014",
 "HasGeospatialIssue is false",
 "TaxonKey is Traunsteinera globosa (L.) Rchb."
 ]
}

 The dataset includes 18475 records from 90 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040897-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f9xdy3
title: Occurrence Download

description: A dataset containing 405 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-80.0948671000000019 40.4174936000000002, -80.0948671000000019 40.4201708699999998, -80.0948671000000019 40.4219557199999997, -80.0948671000000019 40.4259716199999986, -80.0948671000000019 40.4285001499999979, -80.0948671000000019 40.4313261599999976, -80.0948671000000019 40.4337059599999975, -80.0948671000000019 40.4362344899999968, -80.0948671000000019 40.4378705999999966, -80.0917475699999954 40.4378705999999966, -80.0881823900000001 40.4378705999999966, -80.0846172199999984 40.4378705999999966, -80.0800121999999988 40.4378705999999966, -80.0800121999999988 40.4356395399999968, -80.0800121999999988 40.4325160599999975, -80.0800121999999988 40.4301362599999976, -80.0800121999999988 40.4274589899999981, -80.0800121999999988 40.4253766699999986, -80.0800121999999988 40.4226993999999991, -80.0800121999999988 40.4195759199999998, -80.0800121999999988 40.4174936000000002, -80.0846172199999984 40.4174936000000002, -80.0880338500000022 40.4174936000000002, -80.0904106299999938 40.4174936000000002, -80.0948671000000019 40.4174936000000002))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1932",
 "BasisOfRecord is one of (Human Observation, Living Specimen, Observation, Specimen, Machine Observation)"
 ]
}

 The dataset includes 405 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078185-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6dz5yv
title: Occurrence Download

description: A dataset containing 198 species occurrences available in GBIF matching the query:
{
 "and" : [
 "VerbatimScientificName is Graminicola striatus",
 "BasisOfRecord is one of (Machine Observation, Human Observation)",
 "Year 2011-2021",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Specimen, Material citation, Material sample)"
 ]
 }
 }
 ]
}

 The dataset includes 198 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0072760-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.