cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.27692289
title: How Administrative Power Influences Cultural Diffusion: An Explanation from China's Weilongwu

description: The Weilongwu (WLW,围龙屋) architecture is a kind of symbolic cultural landscape of the Hakka, a typical migrant ethnic group in China. The distribution of this housing style reflects the diffusion of culture. Generally, research on cultural processes has primarily focused on cities and administrative logic, under the influence of power, neglecting the interaction between administrative boundaries and subcultures. In this study, WLWs in southern China were identified from remote sensing images. By examining the diffusion patterns of this architectural culture in conjunction with core and peripheral administrative elements, analyzing the cultural diffusion model through quantitative analysis methods, it shows that the scale of the diffusion of this vernacular housing style is related to the scales of ancient administrative districts. The diffusion was reinforced within intermediate-level scales but became constrained beyond these limits. It suggests that the administrative acted on cultural diffusion was not based on coercion but rather on coordinating Man-Land relationships within a region, through rulers’ means, eventually shaping a balance between local and imperial government relations.
DOI: 10.6084/m9.figshare.27692289.v1
title: How Administrative Power Influences Cultural Diffusion: An Explanation from China's Weilongwu

description: The Weilongwu (WLW,围龙屋) architecture is a kind of symbolic cultural landscape of the Hakka, a typical migrant ethnic group in China. The distribution of this housing style reflects the diffusion of culture. Generally, research on cultural processes has primarily focused on cities and administrative logic, under the influence of power, neglecting the interaction between administrative boundaries and subcultures. In this study, WLWs in southern China were identified from remote sensing images. By examining the diffusion patterns of this architectural culture in conjunction with core and peripheral administrative elements, analyzing the cultural diffusion model through quantitative analysis methods, it shows that the scale of the diffusion of this vernacular housing style is related to the scales of ancient administrative districts. The diffusion was reinforced within intermediate-level scales but became constrained beyond these limits. It suggests that the administrative acted on cultural diffusion was not based on coercion but rather on coordinating Man-Land relationships within a region, through rulers’ means, eventually shaping a balance between local and imperial government relations.
DOI: 10.5281/zenodo.13895108
title: Ab initio design of microbial communities from large-scale seed pools using deep learning and rapid ptimization

description: This repository contains the full results of our paper: Ab initio design of microbial communities from large-scale seed pools using deep learning and rapid ptimization.

Authors: Xiaoqing Jiang#, Jiaheng Hou#, Haoyu Zhang#, Jinyuan Guo, Shaohua Gu, Yulin Liao, Xinrun Yang, Peter X. Geng, Yiyan Zhou, Qian Guo, Chunhui Wang, Mo Li, Alexandre Jousset, Zhong Wei*, and Huaiqiu Zhu*

The results including:

(1) GEM.tar.gz: The eBiota-GEM dataset, containing 21,514 Genome-Scale Metabolic Models (GEMs) constructed using CarveMe based on RefSeq complete genomes.

(2) Baterial_evaluation.tar.gz: The evaluation of the ability to uptake substrates and secret productions for all 21,514 GEMs.

(3) Community_results.tar.gz: The results calculated from eBiota-GEM includes various combinations for two-bacterial consortia, covering strain IDs, substrates, products, yields, dual-bacterial growth, single-bacterial growth, co-occurrence predictions, interactions and total production.

(4) DeepCooc_files.tar.gz: The parameter files of DeepCooc, required by eBiota platform.
DOI: 10.5281/zenodo.13762655
title: Ab initio design of microbial communities from large-scale seed pools using deep learning and rapid ptimization

description: This repository contains the full results of our paper: Ab initio design of microbial communities from large-scale seed pools using deep learning and rapid ptimization.

Authors: Xiaoqing Jiang#, Jiaheng Hou#, Haoyu Zhang#, Jinyuan Guo, Shaohua Gu, Yulin Liao, Xinrun Yang, Peter X. Geng, Yiyan Zhou, Qian Guo, Chunhui Wang, Mo Li, Alexandre Jousset, Zhong Wei*, and Huaiqiu Zhu*

The results including:

(1) GEM.tar.gz: The eBiota-GEM dataset, containing 21,514 Genome-Scale Metabolic Models (GEMs) constructed using CarveMe based on RefSeq complete genomes.

(2) Baterial_evaluation.tar.gz: The evaluation of the ability to uptake substrates and secret productions for all 21,514 GEMs.

(3) Community_results.tar.gz: The results calculated from eBiota-GEM includes various combinations for two-bacterial consortia, covering strain IDs, substrates, products, yields, dual-bacterial growth, single-bacterial growth, co-occurrence predictions, interactions and total production.

(4) DeepCooc_files.tar.gz: The parameter files of DeepCooc, required by eBiota platform.
DOI: 10.15468/dl.fm3662
title: Occurrence Download

description: A dataset containing 55767 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Tardigrada"
 ]
}

 The dataset includes 55767 records from 243 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012396-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57745/bqo6tf
title: Readme_Data_Rheo_MA.md

description: :unav
DOI: 10.57745/h4r6ui
title: Data_Rheo_MA.tab

description: :unav
DOI: 10.57745/imozjx
title: Ecarttype_Data.tab

description: :unav
DOI: 10.57745/ixdgpr
title: Readme.md

description: :unav
DOI: 10.57745/x8v5wd
title: Moyenne_Data.tab

description: :unav
DOI: 10.21227/6v50-r718
title: Arxiv

description: The Ogbn-Arxiv dataset (Arxiv for short) represents an academic citation network. In this network structure, papers serve as nodes, citations between papers form edges, and paper abstracts constitute the textual attributes. The primary task involves subject prediction for papers. We utilize the publicly available partitions, ground truth labels, and textual data from OGB
DOI: 10.5284/1124339
title: Data from a Test Pit and Trial Trench Evaluation at Dews Farm, Colne Valley, South Embankment, Hillingdon, 2019-2022 (HS2 Phase One)

description: This collection includes text, site photographs, CAD data, spreadsheets, site records and Harris matrices from a test pit and trial trench evaluation carried out by MOLA Headland Infrastructure at Dews Farm, Colne Valley, South Embankment, Hillingdon between 2019 and 2022.
DOI: 10.15468/dl.zgcfax
title: Occurrence Download

description: A dataset containing 9781 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Procambarus clarkii (Girard, 1852), Procambarus virginalis Lyko, 2017, Procambarus acutus (Girard, 1852), Faxonius limosus (Rafinesque, 1817), Pacifastacus leniusculus (Dana, 1852), Faxonius virilis (Hagen, 1870), Faxonius immunis (Hagen, 1870), Faxonius juvenilis (Hagen, 1870), Faxonius rusticus (Girard, 1852), Pontastacus leptodactylus (Eschscholtz, 1823))",
 "Country is one of (Belgium)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 9781 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012398-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wjnjtz
title: Occurrence Download

description: A dataset containing 182 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cemolobus ipomoeae (Robertson, 1891)"
 ]
}

 The dataset includes 182 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052317-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.77xue8
title: Occurrence Download

description: A dataset containing 10534 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "TaxonKey is Ipomoea pandurata (L.) G.Mey."
 ]
}

 The dataset includes 10534 records from 147 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023431-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13140/rg.2.2.20970.03528
title: FIRST PUNIC WAR
DOI: 10.13140/rg.2.2.14862.08007
title: اسئلة مهمة ٢‬
DOI: 10.15468/dl.pujgt8
title: Occurrence Download

description: A dataset listing the 569 species recorded in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Material sample, Human Observation)",
 "Geometry POLYGON((21.982808022922683 -4.522385386819494,25.982808022922683 -4.522385386819494,25.982808022922683 -0.5223853868194936,21.982808022922683 -0.5223853868194936,21.982808022922683 -4.522385386819494))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae",
 "Year 1990-2025"
 ]
}

 The dataset's 569 records were derived from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041816-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14216603
title: The PODER cross-ancestry long-read RNA-seq dataset

description: Table legends:



column_descriptions.docx: Extended description of columns in selected tables.

00_sample_metadata: Sample and experimental metadata pertaining to each LR-RNA-seq sample.

01_uma_gtf: Unfiltered merged annotation (UMA) GTF for all spliced intron chains discovered by each of the tools.

02_uma_mt: Metadata table for SQANTI QC, Recount3 support, protein prediction, and other relevant characteristics for each transcript in the UMA annotation used to filter transcripts.

03_poder_gtf: Final PODER GTF, including predicted CDSs for novel transcripts and annotated CDSs for known transcripts.

04_poder_mt: Metadata table for SQANTI QC, Recount3 support, protein prediction, and other relevant characteristics for each transcript in PODER.

06_poder_t_counts: PODER transcript counts in each sample as quantified by lr-kallisto.

07_poder_g_counts: PODER gene counts in each sample as quantified by lr-kallisto.

08_mage_t_counts_poder: Transcript counts for the MAGE RNA-seq dataset computed using the PODER annotation with kallisto.

09_mage_t_counts_gencode: Transcript counts for the MAGE RNA-seq dataset computed using the GENCODE annotation with kallisto.

10_mage_t_counts_enh: Transcript counts for the MAGE RNA-seq dataset computed using the Enhanced GENCODE annotation with kallisto.

11_astu: Allele-specific transcript usage results.

12_ase: Allele-specific expression results.

13_mage_tau: Tau values for Enhanced GENCODE transcripts computed on the MAGE RNA-seq dataset.

14_gwas_enrichments: GWAS enrichment results for ASTU genes overlapping GWAS genes.

16_inter_catalog_overlap: Boolean detection of transcripts across PODER, annotations (GENCODE, RefSeq) and other RNA-seq / LR-RNA-seq-derived transcript catalogs (CHESS, GTEx, ENCODE4).

17_personalized_hg38: Isoforms and their intron chains detected using personalized-GRCh38s.

18_enh_gencode: Enhanced GENCODE GTF with novel transcripts from PODER added to all annotated transcripts from GENCODE v47
DOI: 10.5281/zenodo.16759000
title: The PODER cross-ancestry long-read RNA-seq dataset

description: Table legends:



column_descriptions.docx: Extended description of columns in selected tables.

00_sample_metadata: Sample and experimental metadata pertaining to each LR-RNA-seq sample.

01_uma_gtf: Unfiltered merged annotation (UMA) GTF for all spliced intron chains discovered by each of the tools.

02_uma_mt: Metadata table for SQANTI QC, Recount3 support, protein prediction, and other relevant characteristics for each transcript in the UMA annotation used to filter transcripts.

03_poder_gtf: Final PODER GTF, including predicted CDSs for novel transcripts and annotated CDSs for known transcripts.

04_poder_mt: Metadata table for SQANTI QC, Recount3 support, protein prediction, and other relevant characteristics for each transcript in PODER.

06_poder_t_counts: PODER transcript counts in each sample as quantified by lr-kallisto.

07_poder_g_counts: PODER gene counts in each sample as quantified by lr-kallisto.

08_mage_t_counts_poder: Transcript counts for the MAGE RNA-seq dataset computed using the PODER annotation with kallisto.

09_mage_t_counts_gencode: Transcript counts for the MAGE RNA-seq dataset computed using the GENCODE annotation with kallisto.

10_mage_t_counts_enh: Transcript counts for the MAGE RNA-seq dataset computed using the Enhanced GENCODE annotation with kallisto.

11_astu: Allele-specific transcript usage results.

12_ase: Allele-specific expression results.

13_mage_tau: Tau values for Enhanced GENCODE transcripts computed on the MAGE RNA-seq dataset.

14_gwas_enrichments: GWAS enrichment results for ASTU genes overlapping GWAS genes.

16_inter_catalog_overlap: Boolean detection of transcripts across PODER, annotations (GENCODE, RefSeq) and other RNA-seq / LR-RNA-seq-derived transcript catalogs (CHESS, GTEx, ENCODE4).

17_personalized_hg38: Isoforms and their intron chains detected using personalized-GRCh38s.

18_enh_gencode: Enhanced GENCODE GTF with novel transcripts from PODER added to all annotated transcripts from GENCODE v47
DOI: 10.13140/rg.2.2.29070.28486
title: Countries that have more than 3 extreme poverty and threat of hunger
DOI: 10.15468/dl.nz4wnc
title: Occurrence Download

description: A dataset listing the 23 species recorded in GBIF matching the query:
{
 "and" : [
 "GadmGid is ITA.6.8.14_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Aves"
 ]
}

 The dataset's 23 records were derived from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0041829-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13140/rg.2.2.15320.83202
title: BRITISH GENERAL GOTTS EXTREME NAIEVETTE IN ORDERING HIS MOST INEXPERIENCED TANK BRIGADE TO INVESTIGATE ITALIANS
DOI: 10.13140/rg.2.2.13957.68322
title: Insects disease in solapur Maharashtra, 2020-21
DOI: 10.7910/dvn/xzg1dl
title: Replication Data for: Philippines - Regional (4-B, 6, 7, 8) Households under the 4Ps

description: This data provides information on the households under the 4P (Pantawid Pamilyang Pilipino Program) - a conditional cash transfer scheme run by the government of the Philippines. All personally information on individuals has been removed. Data has been aggregated at the household level. For each household in the database, the geographic information is provided as is data on the approximate income of the the household, whether they are identified as "poor" and whether or not they are receiving 4P assistance. This is a master dataset.
DOI: 10.15156/bio/sh0184500.10fu
title: SH0184500.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0184501.10fu
title: SH0184501.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0184502.10fu
title: SH0184502.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0184503.10fu
title: SH0184503.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0184504.10fu
title: SH0184504.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0184505.10fu
title: SH0184505.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0184506.10fu
title: SH0184506.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.