cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24132240
title: Supplementary Material for: Diversity and evolution of highly repetitive DNA sequences constituting chromosome-site specific heterochromatin in two Gerbillinae species

description: Constitutive heterochromatin, consisting of repetitive sequences, diverges very rapidly; therefore, its nucleotide sequences and chromosomal distributions are often largely different, even between closely related species. The chromosome C-banding patterns of two Gerbillinae species, Meriones unguiculatus and Gerbillus perpallidus, vary greatly, even though they belong to the same subfamily. To understand the evolution of C-positive heterochromatin in these species, we isolated highly repetitive sequences, determined their nucleotide sequences, and characterized them using chromosomal and filter hybridization. We obtained a centromeric repeat (MUN-HaeIII) and a chromosome 13-specific repeat (MUN-EcoRI) from M. unguiculatus. We also isolated a centromeric/pericentromeric repeat (GPE-MBD) and an interspersed-type repeat that was predominantly amplified in the X and Y chromosomes (GPE-EcoRI) from G. perpallidus. GPE-MBD was found to contain a 17-bp motif that is essential for binding to the centromere-associated protein CENP-B. This indicates that it may play a role in the formation of a specified structure and/or function of centromeres. The nucleotide sequences of the three sequence families, except GPE-EcoRI, were conserved only in Gerbillinae. GPE-EcoRI was derived from the LINE-1 retrotransposon and showed sequence homology throughout Muridae and Cricetidae species, indicating that the repeat sequence occurred at least in the common ancestor of Muridae and Cricetidae. Due to a lack of assembly data of highly repetitive sequences constituting heterochromatin in whole-genome sequences of vertebrate species published to date, the knowledge obtained in this study provides useful information for a deep understanding of the evolution of repetitive sequences in not only rodents but also in mammals.
DOI: 10.6084/m9.figshare.24132240.v1
title: Supplementary Material for: Diversity and evolution of highly repetitive DNA sequences constituting chromosome-site specific heterochromatin in two Gerbillinae species

description: Constitutive heterochromatin, consisting of repetitive sequences, diverges very rapidly; therefore, its nucleotide sequences and chromosomal distributions are often largely different, even between closely related species. The chromosome C-banding patterns of two Gerbillinae species, Meriones unguiculatus and Gerbillus perpallidus, vary greatly, even though they belong to the same subfamily. To understand the evolution of C-positive heterochromatin in these species, we isolated highly repetitive sequences, determined their nucleotide sequences, and characterized them using chromosomal and filter hybridization. We obtained a centromeric repeat (MUN-HaeIII) and a chromosome 13-specific repeat (MUN-EcoRI) from M. unguiculatus. We also isolated a centromeric/pericentromeric repeat (GPE-MBD) and an interspersed-type repeat that was predominantly amplified in the X and Y chromosomes (GPE-EcoRI) from G. perpallidus. GPE-MBD was found to contain a 17-bp motif that is essential for binding to the centromere-associated protein CENP-B. This indicates that it may play a role in the formation of a specified structure and/or function of centromeres. The nucleotide sequences of the three sequence families, except GPE-EcoRI, were conserved only in Gerbillinae. GPE-EcoRI was derived from the LINE-1 retrotransposon and showed sequence homology throughout Muridae and Cricetidae species, indicating that the repeat sequence occurred at least in the common ancestor of Muridae and Cricetidae. Due to a lack of assembly data of highly repetitive sequences constituting heterochromatin in whole-genome sequences of vertebrate species published to date, the knowledge obtained in this study provides useful information for a deep understanding of the evolution of repetitive sequences in not only rodents but also in mammals.
DOI: 10.48758/ufz.12262
title: Eddy covariance rawdata for Hohes Holz (DE-HoH) in Central Germany, 49m for 201502

description: Eddy-covariance Rawdata (20Hz) from CSAT and LI7500 Hohes Holz 49m, Febuary 2015. Converted from slt- and dia-files to ascii-format (u, v, w, T_Sonic, CO2, H2O, p_75, SA_DIAG, GA75_DIAG), no time column, individual file names are indicating the starting time of the data acquisition period (according to RFlux-requirements)

file: DE-HoH_201502_eddy-raw_csv.zip

more files available in the same repository
DOI: 10.48758/ufz.12261
title: Eddy covariance rawdata for Hohes Holz (DE-HoH) in Central Germany, 49m for 201501

description: Eddy-covariance Rawdata (20Hz) from CSAT and LI7500 Hohes Holz 49m, January 2015. Converted from slt- and dia-files to ascii-format (u, v, w, T_Sonic, CO2, H2O, p_75, SA_DIAG, GA75_DIAG), no time column, individual file names are indicating the starting time of the data acquisition period (according to RFlux-requirements)

file: DE-HoH_201501_eddy-raw_csv.zip

more files available in the same repository
DOI: 10.48758/ufz.12260
title: Eddy covariance rawdata for Hohes Holz (DE-HoH) in Central Germany, 49m for 201412

description: Eddy-covariance Rawdata (20Hz) from CSAT and LI7500 Hohes Holz 49m, December 2014. Converted from slt- and dia-files to ascii-format (u, v, w, T_Sonic, CO2, H2O, p_75, SA_DIAG, GA75_DIAG), no time column, individual file names are indicating the starting time of the data acquisition period (according to RFlux-requirements)

file: DE-HoH_201412_eddy-raw_csv.zip

more files available in the same repository
DOI: 10.48758/ufz.12259
title: Eddy covariance rawdata for Hohes Holz (DE-HoH) in Central Germany, 49m for 201411

description: Eddy-covariance Rawdata (20Hz) from CSAT and LI7500 Hohes Holz 49m, November 2014. Converted from slt- and dia-files to ascii-format (u, v, w, T_Sonic, CO2, H2O, p_75, SA_DIAG, GA75_DIAG), no time column, individual file names are indicating the starting time of the data acquisition period (according to RFlux-requirements)

file: DE-HoH_XXXXXXX_eddy-raw_csv.zip

more files available in the same repository
DOI: 10.5281/zenodo.8340963
title: NIR-light activated core-shell electrospun [Structural Characterization]

description: Structural Characterization of NIR-light activated core-shell electrospun nanofibers.
DOI: 10.5281/zenodo.8340962
title: NIR-light activated core-shell electrospun [Structural Characterization]

description: Structural Characterization of NIR-light activated core-shell electrospun nanofibers.
DOI: 10.48758/ufz.12223
title: Eddy covariance rawdata for Hohes Holz (DE-HoH) in Central Germany, 49m for 201410

description: Eddy-covariance Rawdata (20Hz) from CSAT and LI7500 Hohes Holz 49m, October 2014. Converted from slt- and dia-files to ascii-format (u, v, w, T_Sonic, CO2, H2O, p_75, SA_DIAG, GA75_DIAG), no time column, individual file names are indicating the starting time of the data acquisition period (according to RFlux-requirements)

file: DE-HoH_201410_eddy-raw_csv.zip

more files available in the same repository
DOI: 10.48758/ufz.12220
title: Eddy covariance rawdata for Hohes Holz (DE-HoH) in Central Germany, 49m for 201409

description: Eddy-covariance Rawdata (20Hz) from CSAT and LI7500 Hohes Holz 49m, September 2014. Converted from slt- and dia-files to ascii-format (u, v, w, T_Sonic, CO2, H2O, p_75, SA_DIAG, GA75_DIAG), no time column, individual file names are indicating the starting time of the data acquisition period (according to RFlux-requirements)

file: DE-HoH_201409_eddy-raw_csv.zip

more files available in the same repository
DOI: 10.6084/m9.figshare.23993460.v1
title: Supplementary Material for: Detection rates of non-cavitary epithelioid cell granuloma by gastrointestinal biopsy in patients with treatment-naïve Crohn's disease

description: Introduction: Detecting non-cavitary epithelioid cell granuloma by gastrointestinal biopsy is important in the initial diagnosis of Crohn’s disease (CD). In the present study, we aimed to determine the rate of granuloma detection by gastrointestinal biopsy according to the number of biopsies performed. Methods: The present study included patients newly diagnosed with CD at our hospital between April 2017 and March 2023. During endoscopic examinations, biopsy specimens were taken from affected lesions. Initially, one section per biopsy was examined to detect granuloma. In cases where no granuloma were detected, step sections were additionally prepared and examined. The rate of granuloma detection by gastrointestinal biopsy were retrospectively examined. Results: A total of 30 patients with a new diagnosis of CD were included in this study. In total, 284 gastrointestinal biopsies were performed in 29 cases. The rate of granuloma detection by gastrointestinal biopsy per case was 58.6% (17 out of 29 cases). The rate of granuloma detection by gastrointestinal biopsy per biopsy was 6.0% (17 out of 284 biopsies) on initial histological examination, and 11.6 % (33 out of 284 biopsies) following examination of step sections. The rate of granuloma detection was significantly improved by performing histological examination of step sections compared with initial examinations (P &lt; 0.05). Conclusion: The rate of granuloma detection per biopsy was 11.6%, even after histological examination of step sections. These results indicate that performing multiple intestinal biopsies and assessing for the presence of granuloma using multiple section examinations is required in the initial diagnosis of CD.
DOI: 10.48758/ufz.12219
title: Eddy covariance rawdata for Hohes Holz (DE-HoH) in Central Germany, 49m for 201408

description: Eddy-covariance Rawdata (20Hz) from CSAT and LI7500 Hohes Holz 49m, August 2014. Converted from slt- and dia-files to ascii-format (u, v, w, T_Sonic, CO2, H2O, p_75, SA_DIAG, GA75_DIAG), no time column, individual file names are indicating the starting time of the data acquisition period (according to RFlux-requirements)

file: DE-HoH_201408_eddy-raw_csv.zip

more files available in the same repository
DOI: 10.5281/zenodo.4383290
title: CoronaCentral

description: This describes the output file for the CoronaCentral data. The scripts used to create it are hosted in the corona-ml Github repo. The sources for the documents before processing for CoronaCentral are PubMed and CORD-19. The file is a gzipped JSON document containing one record per document. Each document has at least one of: a PubMed ID, a CORD-19 ID (cord_uid), a DOI or a URL. The fields that documents should have are: pubmed_id: PubMed identifier (optional) pmcid: PubMed Central identifier (optional) doi: Digital object identifier (optional) cord_uid: CORD-19 identifier (optional) url: URL journal: Journal/preprint server publish_year: Year of publication (optional) publish_month: Month of publication (optional) publish_day: Day of publication (optional) title: Title of article abstract: Abstract of article (optional) is_preprint: Whether the article is a preprint categories: Predicted categories for article entities: Extracted entities (e.g. drugs) with identifiers and locations within text Please report issues to the corona-ml Github issues page.
DOI: 10.5281/zenodo.4415410
title: CoronaCentral

description: This describes the output file for the CoronaCentral data. The scripts used to create it are hosted in the corona-ml Github repo. The sources for the documents before processing for CoronaCentral are PubMed and CORD-19. The file is a gzipped JSON document containing one record per document. Each document has at least one of: a PubMed ID, a CORD-19 ID (cord_uid), a DOI or a URL. The fields that documents should have are: pubmed_id: PubMed identifier (optional) pmcid: PubMed Central identifier (optional) doi: Digital object identifier (optional) cord_uid: CORD-19 identifier (optional) url: URL journal: Journal/preprint server publish_year: Year of publication (optional) publish_month: Month of publication (optional) publish_day: Day of publication (optional) title: Title of article abstract: Abstract of article (optional) is_preprint: Whether the article is a preprint categories: Predicted categories for article entities: Extracted entities (e.g. drugs) with identifiers and locations within text Please report issues to the corona-ml Github issues page.
DOI: 10.5281/zenodo.4430443
title: CoronaCentral

description: This describes the output file for the CoronaCentral data. The scripts used to create it are hosted in the corona-ml Github repo. The sources for the documents before processing for CoronaCentral are PubMed and CORD-19. The file is a gzipped JSON document containing one record per document. Each document has at least one of: a PubMed ID, a CORD-19 ID (cord_uid), a DOI or a URL. The fields that documents should have are: pubmed_id: PubMed identifier (optional) pmcid: PubMed Central identifier (optional) doi: Digital object identifier (optional) cord_uid: CORD-19 identifier (optional) url: URL journal: Journal/preprint server publish_year: Year of publication (optional) publish_month: Month of publication (optional) publish_day: Day of publication (optional) title: Title of article abstract: Abstract of article (optional) is_preprint: Whether the article is a preprint categories: Predicted categories for article entities: Extracted entities (e.g. drugs) with identifiers and locations within text Please report issues to the corona-ml Github issues page.
DOI: 10.5281/zenodo.4446349
title: CoronaCentral

description: This describes the output file for the CoronaCentral data. The scripts used to create it are hosted in the corona-ml Github repo. The sources for the documents before processing for CoronaCentral are PubMed and CORD-19. The file is a gzipped JSON document containing one record per document. Each document has at least one of: a PubMed ID, a CORD-19 ID (cord_uid), a DOI or a URL. The fields that documents should have are: pubmed_id: PubMed identifier (optional) pmcid: PubMed Central identifier (optional) doi: Digital object identifier (optional) cord_uid: CORD-19 identifier (optional) url: URL journal: Journal/preprint server publish_year: Year of publication (optional) publish_month: Month of publication (optional) publish_day: Day of publication (optional) title: Title of article abstract: Abstract of article (optional) is_preprint: Whether the article is a preprint categories: Predicted categories for article entities: Extracted entities (e.g. drugs) with identifiers and locations within text Please report issues to the corona-ml Github issues page.
DOI: 10.5281/zenodo.4460686
title: CoronaCentral

description: This describes the output file for the CoronaCentral data. The scripts used to create it are hosted in the corona-ml Github repo. The sources for the documents before processing for CoronaCentral are PubMed and CORD-19. The file is a gzipped JSON document containing one record per document. Each document has at least one of: a PubMed ID, a CORD-19 ID (cord_uid), a DOI or a URL. The fields that documents should have are: pubmed_id: PubMed identifier (optional) pmcid: PubMed Central identifier (optional) doi: Digital object identifier (optional) cord_uid: CORD-19 identifier (optional) url: URL journal: Journal/preprint server publish_year: Year of publication (optional) publish_month: Month of publication (optional) publish_day: Day of publication (optional) title: Title of article abstract: Abstract of article (optional) is_preprint: Whether the article is a preprint categories: Predicted categories for article entities: Extracted entities (e.g. drugs) with identifiers and locations within text Please report issues to the corona-ml Github issues page.
DOI: 10.5281/zenodo.4483257
title: CoronaCentral

description: This describes the output file for the CoronaCentral data. The scripts used to create it are hosted in the corona-ml Github repo. The sources for the documents before processing for CoronaCentral are PubMed and CORD-19. The file is a gzipped JSON document containing one record per document. Each document has at least one of: a PubMed ID, a CORD-19 ID (cord_uid), a DOI or a URL. The fields that documents should have are: pubmed_id: PubMed identifier (optional) pmcid: PubMed Central identifier (optional) doi: Digital object identifier (optional) cord_uid: CORD-19 identifier (optional) url: URL journal: Journal/preprint server publish_year: Year of publication (optional) publish_month: Month of publication (optional) publish_day: Day of publication (optional) title: Title of article abstract: Abstract of article (optional) is_preprint: Whether the article is a preprint categories: Predicted categories for article entities: Extracted entities (e.g. drugs) with identifiers and locations within text Please report issues to the corona-ml Github issues page.
DOI: 10.5281/zenodo.4517673
title: CoronaCentral

description: This describes the output file for the CoronaCentral data. The scripts used to create it are hosted in the corona-ml Github repo. The sources for the documents before processing for CoronaCentral are PubMed and CORD-19. The file is a gzipped JSON document containing one record per document. Each document has at least one of: a PubMed ID, a CORD-19 ID (cord_uid), a DOI or a URL. The fields that documents should have are: pubmed_id: PubMed identifier (optional) pmcid: PubMed Central identifier (optional) doi: Digital object identifier (optional) cord_uid: CORD-19 identifier (optional) url: URL journal: Journal/preprint server publish_year: Year of publication (optional) publish_month: Month of publication (optional) publish_day: Day of publication (optional) title: Title of article abstract: Abstract of article (optional) is_preprint: Whether the article is a preprint categories: Predicted categories for article entities: Extracted entities (e.g. drugs) with identifiers and locations within text Please report issues to the corona-ml Github issues page.
DOI: 10.5281/zenodo.4540455
title: CoronaCentral

description: This describes the output file for the CoronaCentral data. The scripts used to create it are hosted in the corona-ml Github repo. The sources for the documents before processing for CoronaCentral are PubMed and CORD-19. The file is a gzipped JSON document containing one record per document. Each document has at least one of: a PubMed ID, a CORD-19 ID (cord_uid), a DOI or a URL. The fields that documents should have are: pubmed_id: PubMed identifier (optional) pmcid: PubMed Central identifier (optional) doi: Digital object identifier (optional) cord_uid: CORD-19 identifier (optional) url: URL journal: Journal/preprint server publish_year: Year of publication (optional) publish_month: Month of publication (optional) publish_day: Day of publication (optional) title: Title of article abstract: Abstract of article (optional) is_preprint: Whether the article is a preprint categories: Predicted categories for article entities: Extracted entities (e.g. drugs) with identifiers and locations within text Please report issues to the corona-ml Github issues page.
DOI: 10.5281/zenodo.4553832
title: CoronaCentral

description: This describes the output file for the CoronaCentral data. The scripts used to create it are hosted in the corona-ml Github repo. The sources for the documents before processing for CoronaCentral are PubMed and CORD-19. The file is a gzipped JSON document containing one record per document. Each document has at least one of: a PubMed ID, a CORD-19 ID (cord_uid), a DOI or a URL. The fields that documents should have are: pubmed_id: PubMed identifier (optional) pmcid: PubMed Central identifier (optional) doi: Digital object identifier (optional) cord_uid: CORD-19 identifier (optional) url: URL journal: Journal/preprint server publish_year: Year of publication (optional) publish_month: Month of publication (optional) publish_day: Day of publication (optional) title: Title of article abstract: Abstract of article (optional) is_preprint: Whether the article is a preprint categories: Predicted categories for article entities: Extracted entities (e.g. drugs) with identifiers and locations within text Please report issues to the corona-ml Github issues page.
DOI: 10.5281/zenodo.4568704
title: CoronaCentral

description: This describes the output file for the CoronaCentral data. The scripts used to create it are hosted in the corona-ml Github repo. The sources for the documents before processing for CoronaCentral are PubMed and CORD-19. The file is a gzipped JSON document containing one record per document. Each document has at least one of: a PubMed ID, a CORD-19 ID (cord_uid), a DOI or a URL. The fields that documents should have are: pubmed_id: PubMed identifier (optional) pmcid: PubMed Central identifier (optional) doi: Digital object identifier (optional) cord_uid: CORD-19 identifier (optional) url: URL journal: Journal/preprint server publish_year: Year of publication (optional) publish_month: Month of publication (optional) publish_day: Day of publication (optional) title: Title of article abstract: Abstract of article (optional) is_preprint: Whether the article is a preprint categories: Predicted categories for article entities: Extracted entities (e.g. drugs) with identifiers and locations within text Please report issues to the corona-ml Github issues page.
DOI: 10.5281/zenodo.4587684
title: CoronaCentral

description: This describes the output file for the CoronaCentral data. The scripts used to create it are hosted in the corona-ml Github repo. The sources for the documents before processing for CoronaCentral are PubMed and CORD-19. The file is a gzipped JSON document containing one record per document. Each document has at least one of: a PubMed ID, a CORD-19 ID (cord_uid), a DOI or a URL. The fields that documents should have are: pubmed_id: PubMed identifier (optional) pmcid: PubMed Central identifier (optional) doi: Digital object identifier (optional) cord_uid: CORD-19 identifier (optional) url: URL journal: Journal/preprint server publish_year: Year of publication (optional) publish_month: Month of publication (optional) publish_day: Day of publication (optional) title: Title of article abstract: Abstract of article (optional) is_preprint: Whether the article is a preprint categories: Predicted categories for article entities: Extracted entities (e.g. drugs) with identifiers and locations within text Please report issues to the corona-ml Github issues page.
DOI: 10.5281/zenodo.4603527
title: CoronaCentral

description: This describes the output file for the CoronaCentral data. The scripts used to create it are hosted in the corona-ml Github repo. The sources for the documents before processing for CoronaCentral are PubMed and CORD-19. The file is a gzipped JSON document containing one record per document. Each document has at least one of: a PubMed ID, a CORD-19 ID (cord_uid), a DOI or a URL. The fields that documents should have are: pubmed_id: PubMed identifier (optional) pmcid: PubMed Central identifier (optional) doi: Digital object identifier (optional) cord_uid: CORD-19 identifier (optional) url: URL journal: Journal/preprint server publish_year: Year of publication (optional) publish_month: Month of publication (optional) publish_day: Day of publication (optional) title: Title of article abstract: Abstract of article (optional) is_preprint: Whether the article is a preprint categories: Predicted categories for article entities: Extracted entities (e.g. drugs) with identifiers and locations within text Please report issues to the corona-ml Github issues page.