cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/24ycmj22jw.1
title: The Role of Indigenous Elders in Maintaining Cultural Heritage: Transmitting Traditions, Oral Histories, and Spiritual Practices in Kidapawan City

description: Indigenous communities around the world have long maintained rich cultural traditions, oral histories, and spiritual practices that are passed down from generation to generation. Central to the preservation and transmission of this knowledge are Indigenous elders, who serve as the custodians of cultural heritage within their communities. Elders are respected not only for their age and experience but also for their deep understanding of language, ceremonies, community values, and ecological knowledge. They play a crucial role in maintaining the social fabric of Indigenous societies by ensuring that cultural practices are preserved and adapted as needed for future generations.

For many Indigenous groups, cultural knowledge is not stored in books or written records, but rather in the lived experiences, stories, and teachings of elders. These elders are often seen as the bridge between the past and the present, connecting younger generations with the traditions, values, and wisdom of their ancestors.

However, in contemporary times, the role of elders is increasingly challenged by external factors such as globalization, urbanization, and the erosion of traditional knowledge systems. The rapid pace of technological change, along with the loss of language and land, has made it difficult for younger generations to fully connect with their cultural heritage. As a result, there is growing concern about the potential loss of Indigenous knowledge and the role of elders in safeguarding it.

This concept paper explores the important role of Indigenous elders in the transmission of cultural heritage, focusing on their role in preserving traditions, oral histories, and spiritual practices. It also examines the challenges they face in an increasingly modernized world and offers recommendations for strengthening their position as cultural leaders.
DOI: 10.7483/opendata.cms.nscc.pjcr
title: /TprimeBToTH_TLep_Hbb_LH_MT2500_MH75_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17044/scilifelab.14743425.v1
title: Dataset of SARS-CoV-2 wastewater data from Östhammar, Sweden

description: This is a metadata record for a continuously updated dataset of SARS-CoV-2 RNA data in wastewater in Östhammar, Sweden. <br>The dataset is part of a research study led associate professor Anna J. Székely (SLU, Swedish University of Agricultural Sciences) and her research groups in collaboration with Uppsala Vatten. The research group is part of the Environmental Virus Profiling Research Area of the SciLifeLab National COVID-19 Research Program.<br>The viral content is concentrated according to the protocol from Ahmed et al. https://doi.org/10.1016/j.scitotenv.2020.139960according to the modified electronegative filtration method. Viral RNA is extracted and SARS-CoV-2 gene copy nr quantified using R-qPCR. Recovery efficiency is measured using Bovine coronavirus (BCoV) as a virus surrogate. Pepper mild mottle virus (PPMoV) is used to correct for variations in water size and flow. For more information see https://www.covid19dataportal.se/data_types/environment/wastewater/#uppsala<br><br>The dataset for Östhammar started week 14 2021. <br>The dataset is available as part of the Environmental Virus Profiling data section. The amount of SARS-CoV-2 virus in wastewater across Sweden" https://www.covid19dataportal.se/data_types/environment/wastewater/#uppsala on the Swedish COVID-19 Data Portal (https://covid19dataportal.se). The dataset is also available as part of the CRUSH Covid project https://crush-covid.shinyapps.io/crush_covid/<br>Funded by SciLifeLab/KAW National COVID-19 Research Program https://www.scilifelab.se/covid-19/national-program/<br>
DOI: 10.17044/scilifelab.14743431
title: Dataset of SARS-CoV-2 wastewater data from Älvkarleby, Sweden

description: This is a metadata record for a continuously updated dataset of SARS-CoV-2 RNA data in wastewater in Älvkarleby, Sweden. <br>The dataset is part of a research study led associate professor Anna J. Székely (SLU, Swedish University of Agricultural Sciences) and her research groups in collaboration with Uppsala Vatten. The research group is part of the Environmental Virus Profiling Research Area of the SciLifeLab National COVID-19 Research Program.<br>The viral content is concentrated according to the protocol from Ahmed et al. https://doi.org/10.1016/j.scitotenv.2020.139960according to the modified electronegative filtration method. Viral RNA is extracted and SARS-CoV-2 gene copy nr quantified using R-qPCR. Recovery efficiency is measured using Bovine coronavirus (BCoV) as a virus surrogate. Pepper mild mottle virus (PPMoV) is used to correct for variations in water size and flow. For more information see https://www.covid19dataportal.se/data_types/environment/wastewater/#uppsala<br><br>The dataset for Älvkarleby started week 14 2021.<br>The dataset is available as part of the Environmental Virus Profiling data section. The amount of SARS-CoV-2 virus in wastewater across Sweden" https://www.covid19dataportal.se/data_types/environment/wastewater/#uppsala on the Swedish COVID-19 Data Portal (https://covid19dataportal.se). The dataset is also available as part of the CRUSH Covid project https://crush-covid.shinyapps.io/crush_covid/<br>Funded by SciLifeLab/KAW National COVID-19 Research Program https://www.scilifelab.se/covid-19/national-program/<br>
DOI: 10.17044/scilifelab.14743431.v1
title: Dataset of SARS-CoV-2 wastewater data from Älvkarleby, Sweden

description: This is a metadata record for a continuously updated dataset of SARS-CoV-2 RNA data in wastewater in Älvkarleby, Sweden. <br>The dataset is part of a research study led associate professor Anna J. Székely (SLU, Swedish University of Agricultural Sciences) and her research groups in collaboration with Uppsala Vatten. The research group is part of the Environmental Virus Profiling Research Area of the SciLifeLab National COVID-19 Research Program.<br>The viral content is concentrated according to the protocol from Ahmed et al. https://doi.org/10.1016/j.scitotenv.2020.139960according to the modified electronegative filtration method. Viral RNA is extracted and SARS-CoV-2 gene copy nr quantified using R-qPCR. Recovery efficiency is measured using Bovine coronavirus (BCoV) as a virus surrogate. Pepper mild mottle virus (PPMoV) is used to correct for variations in water size and flow. For more information see https://www.covid19dataportal.se/data_types/environment/wastewater/#uppsala<br><br>The dataset for Älvkarleby started week 14 2021.<br>The dataset is available as part of the Environmental Virus Profiling data section. The amount of SARS-CoV-2 virus in wastewater across Sweden" https://www.covid19dataportal.se/data_types/environment/wastewater/#uppsala on the Swedish COVID-19 Data Portal (https://covid19dataportal.se). The dataset is also available as part of the CRUSH Covid project https://crush-covid.shinyapps.io/crush_covid/<br>Funded by SciLifeLab/KAW National COVID-19 Research Program https://www.scilifelab.se/covid-19/national-program/<br>
DOI: 10.7483/opendata.cms.js3c.pscm
title: /TprimeBToTH_TLep_Hbb_LH_MT2500_MH75_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17044/scilifelab.14743434
title: Dataset of SARS-CoV-2 wastewater data from Uppsala, Sweden

description: This is a metadata record for a continuously updated dataset of SARS-CoV-2 RNA data in wastewater in Uppsala, Sweden. <br>The dataset is part of a research study led associate professor Anna J. Székely (SLU, Swedish University of Agricultural Sciences) and her research groups in collaboration with Uppsala Vatten. The research group is part of the Environmental Virus Profiling Research Area of the SciLifeLab National COVID-19 Research Program.<br>The viral content is concentrated according to the protocol from Ahmed et al. https://doi.org/10.1016/j.scitotenv.2020.139960according to the modified electronegative filtration method. Viral RNA is extracted and SARS-CoV-2 gene copy nr quantified using R-qPCR. Recovery efficiency is measured using Bovine coronavirus (BCoV) as a virus surrogate. Pepper mild mottle virus (PPMoV) is used to correct for variations in water size and flow. For more information see https://www.covid19dataportal.se/data_types/environment/wastewater/#uppsala<br><br>The dataset for Uppsala started week 38 2020 and updates weekly. <br>The dataset is available as part of the Environmental Virus Profiling data section. The amount of SARS-CoV-2 virus in wastewater across Sweden" https://www.covid19dataportal.se/data_types/environment/wastewater/#uppsala on the Swedish COVID-19 Data Portal (https://covid19dataportal.se). The dataset is also available as part of the CRUSH Covid project https://crush-covid.shinyapps.io/crush_covid/<br>Funded by SciLifeLab/KAW National COVID-19 Research Program https://www.scilifelab.se/covid-19/national-program/<br>
DOI: 10.17632/8h64npjttr.1
title: Modified Functional Fitness Test and Mental Health Relationship

description: The reemergence of primitive reflexes (PRs) in older adults is linked to dementia and cognitive decline. This study investigated whether physical activity (PA) and functional fitness indices are inversely related to PRs in 52 older adults from Hungarian nursing homes. Results showed that all functional fitness measures, except upper and lower body flexibility, and PA were negatively associated with PRs, with PA being the strongest predictor, explaining 41% of the variance. Hand-grip strength and four functional fitness elements showed weaker negative associations. These findings suggest that maintaining PA may help prevent or delay PR resurgence, offering potential strategies to support mental health in older adults.
DOI: 10.17044/scilifelab.14743434.v1
title: Dataset of SARS-CoV-2 wastewater data from Uppsala, Sweden

description: This is a metadata record for a continuously updated dataset of SARS-CoV-2 RNA data in wastewater in Uppsala, Sweden. <br>The dataset is part of a research study led associate professor Anna J. Székely (SLU, Swedish University of Agricultural Sciences) and her research groups in collaboration with Uppsala Vatten. The research group is part of the Environmental Virus Profiling Research Area of the SciLifeLab National COVID-19 Research Program.<br>The viral content is concentrated according to the protocol from Ahmed et al. https://doi.org/10.1016/j.scitotenv.2020.139960according to the modified electronegative filtration method. Viral RNA is extracted and SARS-CoV-2 gene copy nr quantified using R-qPCR. Recovery efficiency is measured using Bovine coronavirus (BCoV) as a virus surrogate. Pepper mild mottle virus (PPMoV) is used to correct for variations in water size and flow. For more information see https://www.covid19dataportal.se/data_types/environment/wastewater/#uppsala<br><br>The dataset for Uppsala started week 38 2020 and updates weekly. <br>The dataset is available as part of the Environmental Virus Profiling data section. The amount of SARS-CoV-2 virus in wastewater across Sweden" https://www.covid19dataportal.se/data_types/environment/wastewater/#uppsala on the Swedish COVID-19 Data Portal (https://covid19dataportal.se). The dataset is also available as part of the CRUSH Covid project https://crush-covid.shinyapps.io/crush_covid/<br>Funded by SciLifeLab/KAW National COVID-19 Research Program https://www.scilifelab.se/covid-19/national-program/<br>
DOI: 10.17632/pvrfvc43cp.1
title: KurdSum Dataset

description: The KurdSum dataset stands as a comprehensive and invaluable resource for the development and enhancement of Kurdish language summarization models. With a vast collection of over 40,000 news articles, each meticulously distilled by proficient Kurdish journalists, the dataset provides an unparalleled corpus that encapsulates the myriad facets of human knowledge and experience.
Encompassing an extensive spectrum of subjects, the KurdSum dataset spans across diverse domains including politics, sports, science, society, religion, health, art, and more. This encompassing variety ensures that the dataset mirrors the rich tapestry of topics that captivate the interests and concerns of Kurdish speakers.
At the heart of KurdSum lies the diligent effort of skilled journalists who have skillfully distilled the essence of each article into concise summaries. This human touch brings a layer of contextual understanding, nuance, and linguistic finesse to the dataset. The inclusion of manually generated summaries not only aids in constructing coherent and coherent summaries but also serves as a source of inspiration for generating high-quality abstractions in Kurdish text.
Researchers, developers, and language enthusiasts seeking to delve into the realm of Kurdish summarization stand to gain significantly from the KurdSum dataset. With its vast volume of diverse content and journalist-crafted summaries, the dataset provides a robust foundation for training and fine-tuning summarization models tailored to the nuances of the Kurdish language. This resource not only empowers the creation of efficient and accurate summarization algorithms but also nurtures the growth of natural language understanding within the Kurdish linguistic landscape.
In conclusion, the KurdSum dataset emerges as a treasure trove of knowledge, thoughtfully curated by Kurdish journalists, that serves as a cornerstone for the development of Kurdish summarization models. Its extensive coverage of topics, precision-crafted summaries, and commitment to the Kurdish language make it an indispensable asset for researchers and developers striving to unlock the potential of automated summarization in the Kurdish linguistic context.
DOI: 10.17632/pvrfvc43cp.2
title: KurdSum Dataset

description: The KurdSum dataset stands as a comprehensive and invaluable resource for the development and enhancement of Kurdish language summarization models. With a vast collection of over 40,000 news articles, each meticulously distilled by proficient Kurdish journalists, the dataset provides an unparalleled corpus that encapsulates the myriad facets of human knowledge and experience.
Encompassing an extensive spectrum of subjects, the KurdSum dataset spans across diverse domains including politics, sports, science, society, religion, health, art, and more. This encompassing variety ensures that the dataset mirrors the rich tapestry of topics that captivate the interests and concerns of Kurdish speakers.
At the heart of KurdSum lies the diligent effort of skilled journalists who have skillfully distilled the essence of each article into concise summaries. This human touch brings a layer of contextual understanding, nuance, and linguistic finesse to the dataset. The inclusion of manually generated summaries not only aids in constructing coherent and coherent summaries but also serves as a source of inspiration for generating high-quality abstractions in Kurdish text.
Researchers, developers, and language enthusiasts seeking to delve into the realm of Kurdish summarization stand to gain significantly from the KurdSum dataset. With its vast volume of diverse content and journalist-crafted summaries, the dataset provides a robust foundation for training and fine-tuning summarization models tailored to the nuances of the Kurdish language. This resource not only empowers the creation of efficient and accurate summarization algorithms but also nurtures the growth of natural language understanding within the Kurdish linguistic landscape.
In conclusion, the KurdSum dataset emerges as a treasure trove of knowledge, thoughtfully curated by Kurdish journalists, that serves as a cornerstone for the development of Kurdish summarization models. Its extensive coverage of topics, precision-crafted summaries, and commitment to the Kurdish language make it an indispensable asset for researchers and developers striving to unlock the potential of automated summarization in the Kurdish linguistic context.
In this, version , we have removed the categories column in the dataset as we are currently in the process of developing and adding more accurate categories to the headlines news. Once process is completed, we will upload the final version.
DOI: 10.7483/opendata.cms.kezy.scjn
title: /TprimeBToTH_TLep_Hbb_LH_MT2600_MH100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16MiniAODv2-106X_mcRun2_asymptotic_v17-v2/MINIAODSIM
DOI: 10.17632/y9x53dzyhg.1
title: ISAO-cec2017-main

description: This data compression package contains a total of two folders, one being the test-cec2017-plot folder, which is used to evaluate the independent contributions of the three improvement modules. The other is the ISAO-cec2017-main folder to test the ISAO algorithm against the other five classical optimization algorithms.
DOI: 10.17044/scilifelab.14869077.v1
title: SBDI Sativa curated 16S GTDB database

description: The data in this repository is the result of vetting 16S sequences from the GTDB database release R06-RS202 (https://gtdb.ecogenomic.org/; Parks et al. 2018) with the Sativa program (Kozlov et al. 2016).<br><br>Files for the DADA2 (Callahan et al. 2016) methods `assignTaxonomy` and `addSpecies` are available.<br>Both files are gzipped fasta files with 16S sequences, the assignTaxonomy associated with taxonomy hierarchies from domain to genus whereas the addSpecies file have sequence identities and species names.<br><br>Taxonomical annotation of 16S amplicons using this data is available as an optional argument to the nf-core/ampliseq Nextflow workflow from version 2.1: --dada_ref_taxonomy sbdi-gtdb (https://nf-co.re/ampliseq; Straub et al. 2020).<br><br>The data will be updated circa yearly, when the GTDB database is updated.<br><br><b>Curation</b><br><br>After download, sequences longer than 2000 basepairs and sequences containing undetermined bases ('N') were removed. Subsequently, sequences, as well as the reverse-complements of these, were aligned to the archaeal and bacterial SSU profiles from Barrnap (https://github.com/tseemann/barrnap) with hmmalign from HMMER (Eddy 2011). Sequences aligning to fewer than 1000 bases of their respective profile in both forward and reverse-complementary direction were deleted. For the sequences passing the above filters, the longest sequence in each genome was kept. <br>For each species, a maximum of 5 sequences was selected, prioritizing sequences from GTDB species-representative genomes, and longer sequences before shorter. The remaining sequences were then analyzed with Sativa (Kozlov et al. 2016) and sequences misclassified at genus to phylum level were removed. A Perl script for conducting filtering of sequences prior to and after Sativa analysis can be found in the `scripts` folder in the GitHub repo: https://github.com/biodiversitydata-se/sbdi-gtdb. Run perl select_seq_sativa.pl --h for documentation.<br>
DOI: 10.17632/8mz7mfrttp
title: Data for Artificial vegetation restoration patterns regulated soil multi-functionality via co-occurrence network complexity of multitrophic biology in degraded karst region

description: Soil Organism,diversity,co-occurrence work
DOI: 10.17632/8mz7mfrttp.1
title: Data for Artificial vegetation restoration patterns regulated soil multi-functionality via co-occurrence network complexity of multitrophic biology in degraded karst region

description: Soil Organism,diversity,co-occurrence work
DOI: 10.7483/opendata.cms.lh5j.m9rs
title: /TprimeBToTH_TLep_Hbb_LH_MT2600_MH100_TuneCP5_13TeV-madgraph-pythia8/RunIISummer20UL16NanoAODv9-106X_mcRun2_asymptotic_v17-v1/NANOAODSIM
DOI: 10.17044/scilifelab.14869077.v2
title: SBDI Sativa curated 16S GTDB database

description: The data in this repository is the result of vetting 16S sequences from the GTDB database release R06-RS202 (https://gtdb.ecogenomic.org/; Parks et al. 2018) with the Sativa program (Kozlov et al. 2016).<br><br>Files for the DADA2 (Callahan et al. 2016) methods `assignTaxonomy` and `addSpecies` are available: gtdb-sbdi-sativa.r06rs202.assignTaxonomy.fna.gz and gtdb-sbdi-sativa.r06rs202.addSpecies.fna.gz.<br>There is also a fasta file with the original GTDB sequence names: gtdb-sbdi-sativa.r06rs202.fna.gz<br>All three files are gzipped fasta files with 16S sequences, the assignTaxonomy associated with taxonomy hierarchies from domain to genus whereas the addSpecies file have sequence identities and species names.<br><br>Taxonomical annotation of 16S amplicons using this data is available as an optional argument to the nf-core/ampliseq Nextflow workflow from version 2.1: --dada_ref_taxonomy sbdi-gtdb (https://nf-co.re/ampliseq; Straub et al. 2020).<br><br>The data will be updated circa yearly, when the GTDB database is updated.<br><br><b>Curation</b><br><br>After download, sequences longer than 2000 basepairs and sequences containing undetermined bases ('N') were removed. Subsequently, sequences, as well as the reverse-complements of these, were aligned to the archaeal and bacterial SSU profiles from Barrnap (https://github.com/tseemann/barrnap) with hmmalign from HMMER (Eddy 2011). Sequences aligning to fewer than 1000 bases of their respective profile in both forward and reverse-complementary direction were deleted. For the sequences passing the above filters, the longest sequence in each genome was kept. <br>For each species, a maximum of 5 sequences was selected, prioritizing sequences from GTDB species-representative genomes, and longer sequences before shorter. The remaining sequences were then analyzed with Sativa (Kozlov et al. 2016) and sequences misclassified at genus to phylum level were removed. A Perl script for conducting filtering of sequences prior to and after Sativa analysis can be found in the `scripts` folder in the GitHub repo: https://github.com/biodiversitydata-se/sbdi-gtdb. Run perl select_seq_sativa.pl --h for documentation.<br>
DOI: 10.17044/scilifelab.14869077.v3
title: SBDI Sativa curated 16S GTDB database

description: The data in this repository is the result of vetting 16S sequences from the GTDB database release R06-RS202 (https://gtdb.ecogenomic.org/; Parks et al. 2018) with the Sativa program (Kozlov et al. 2016).<br><br>Files for the DADA2 (Callahan et al. 2016) methods `assignTaxonomy` and `addSpecies` are available: gtdb-sbdi-sativa.r06rs202.assignTaxonomy.fna.gz and gtdb-sbdi-sativa.r06rs202.addSpecies.fna.gz.<br>There is also a fasta file with the original GTDB sequence names: gtdb-sbdi-sativa.r06rs202.fna.gz<br>All three files are gzipped fasta files with 16S sequences, the assignTaxonomy associated with taxonomy hierarchies from domain to genus whereas the addSpecies file have sequence identities and species names.<br><br>Taxonomical annotation of 16S amplicons using this data is available as an optional argument to the nf-core/ampliseq Nextflow workflow from version 2.1: --dada_ref_taxonomy sbdi-gtdb (https://nf-co.re/ampliseq; Straub et al. 2020).<br><br>The data will be updated circa yearly, when the GTDB database is updated.<br><br><b>Curation</b><br><br>After download, sequences longer than 2000 basepairs and sequences containing undetermined bases ('N') were removed. Subsequently, sequences, as well as the reverse-complements of these, were aligned to the archaeal and bacterial SSU profiles from Barrnap (https://github.com/tseemann/barrnap) with hmmalign from HMMER (Eddy 2011). Sequences aligning to fewer than 1000 bases of their respective profile in both forward and reverse-complementary direction were deleted. For the sequences passing the above filters, the longest sequence in each genome was kept. <br>For each species, a maximum of 5 sequences was selected, prioritizing sequences from GTDB species-representative genomes, and longer sequences before shorter. The remaining sequences were then analyzed with Sativa (Kozlov et al. 2016) and sequences misclassified at genus to phylum level were removed. A Perl script for conducting filtering of sequences prior to and after Sativa analysis can be found in the `scripts` folder in the GitHub repo: https://github.com/biodiversitydata-se/sbdi-gtdb. Run perl select_seq_sativa.pl --h for documentation.<br>