cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6075/j01n81b7
title: Nourish: A Knowledge-Driven Recommender System for Food Entrepreneurs. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: The process of operating a food-related business is complex and requires in-depth knowledge of many factors, including local policies/regulations, supply chains, sources of funding, and more. These complicated factors have made the business of food a very difficult field to work in, for both new and existing professionals. To help with this effort, a comprehensive food business knowledge graph and chart based user interface, Nourish, was created in an attempt to reduce the barrier of finding the right information to successfully operate in the food industry. The knowledge graph was developed by integrating multiple data sources to address each component of the food industry. These data sources included geographic, document, ontological and relational data. For geographic data, the knowledge graph utilized Arcgis online via Esri to pinpoint optimal business locations (based on an opportunity scoring calculation) which could be suggested to the end user. For the document data, document stores were created to provide users with funding information from various government institutions, such as the Small Business Administration (SBA) and the United States Department of Agriculture (USDA). For the ontological data, various foundational ontologies including FOODON and FIBO were integrated into a graph database. For relational data, the USDA Food Data Central database was also incorporated to better understand nutritional alternatives and increase accessibility to healthy options. To enable widespread access to the knowledge graph, Nourish was connected to Open AI large language model (llm) GPT-3.5, which provided a user-friendly way to query the knowledge graph. To transform user queries with GPT-3.5 into responses, the ReAct conversational agent chain was implemented using the Langchain framework as an interface. The agent was composed of several tools to address user inputs. For example, the location tool was used to suggest optimal locations for a business. Another tool queried the document indexes created on the document stores to address loan eligibility and general loan inquiries. The Nourish chatbot was displayed through a Dash app to facilitate conversation with the user. Overall, the Nourish chatbot effectively queried the integrated knowledge graph to provide users with personalized recommendations on vital information such as where to open their business, what loans they could apply for, and where they could find additional support.
DOI: 10.6075/j0t72hns
title: A Streamlined Workflow to Facilitate Genome-Scale AI. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects

description: Since the first draft of the human genome was published in the early 2000s, the drastic reduction in the costs of sequencing technologies and speed by which such technology is able to sequence genomes has defied Moore’s Law and allowed scientists to collect a wealth of data regarding the code of life. When extending genomic sequencing to large populations of humans or other animals, the genomic data of each individual, also known as the genotype, can be paired with observable or measurable traits of the individual, also known as the phenotype, and allow for relationships between genotypes and phenotypes to be analyzed. This type of work has powered numerous Genome-Wide Association Studies (GWAS) that have been able to associate disease with underlying genetic causes that can root to the specific position on the genome. In this study we have collected genotypes for a heterogenous population of about 13,000 rats using unphased Whole-Genome Sequencing. Such data for each rat is represented as a constellation of point mutations, also known as Single Nucleotide Polymorphisms (SNP), where each mutation is characterized as a positional variation with respect to a rat reference genome, the rn6 reference genome, following an assembly and alignment of nucleotide reads guided by the reference. In conjunction with collecting rat genotypes by sequencing, we have also collected quantitative behavioral phenotypes, such as locomotor activity in response to various external stimuli. With this comprehensive collection of genotype data represented by millions of SNPs per rat paired with behavioral phenotype data, this work seeks to assess the capabilities of Machine Learning for predicting rat behavior on a whole-genome scale. However, considering the complexity of genomic data, such as the sheer dimensionality of SNPs or dependent relationships that deduce phenotypes, as well as the challenges due to a lack of phenotype data compared to the genotype data, there are many different ways to go about a Machine Learning solution for predicting genotype to phenotype. These methods all leverage different data processing and reduction techniques, different models, and different tuned hypermeters. To handle the complexity in devising Machine Learning models that predict phenotypes from millions of SNPs, we propose framework that leverages powerful ML workflows, namely hydra, optuna, and MLFlow combined with a highly scalable genomics toolkit, namely sgkit, to devise a streamlined pipeline meant to facilitate genome-scale AI.
DOI: 10.18430/m37uyi
title: X-ray diffraction data for the crystal structure of the computationally optimized broadly reactive H1 influenza hemagglutinin P1 (7UYI)
DOI: 10.34740/kaggle/dsv/6288956
title: US counties COVID 19 dataset
DOI: 10.6084/m9.figshare.23935389
title: TMEM16A dataset

description: Individual data points of the graphs in the main figures.
DOI: 10.6084/m9.figshare.23935389.v1
title: Untitled Item

description: Individual data points of the graphs in the main figures.
DOI: 10.6084/m9.figshare.23935389.v2
title: TMEM16A dataset

description: Individual data points of the graphs in the main figures.
DOI: 10.6084/m9.figshare.23929680
title: Multitargets compounds from literature

description: 91 multitarget compounds with reported <i>in vivo</i> activity in csv format. The database includes ADME-Tox properties calculated in ADMET.lab 2.0
DOI: 10.6084/m9.figshare.23929680.v1
title: Multitargets compounds from literature

description: 91 multitarget compounds with reported <i>in vivo</i> activity in csv format. The database includes ADME-Tox properties calculated in ADMET.lab 2.0
DOI: 10.6084/m9.figshare.23929677
title: Antidiabetic compounds approved for clinical use

description: 42 approved drugs for T2DM in csv format. The database includes ADME-Tox properties calculated in ADMET.lab 2.0
DOI: 10.5281/zenodo.8122939
title: Data for Campus SLL

description: This is the data generated from the C2SMART Project "Digital Twin Technologies Towards Understanding the Interactions Between Transportation and Other Civil Infrastructure Systems: Phase 2"
DOI: 10.5281/zenodo.8122940
title: Data for Campus SLL

description: This is the data generated from the C2SMART Project "Digital Twin Technologies Towards Understanding the Interactions Between Transportation and Other Civil Infrastructure Systems: Phase 2"
DOI: 10.5281/zenodo.8240212
title: Data for Campus SLL

description: This is the data generated from the C2SMART Project "Digital Twin Technologies Towards Understanding the Interactions Between Transportation and Other Civil Infrastructure Systems: Phase 2"
DOI: 10.6084/m9.figshare.23935383
title: 816 multi-target compounds for AR and PTP1B

description: Dataset of 816 multi-target composites designed for AR and PTP1B in csv format. The database includes MOE-calculated Score values for AR and PTP1b and ADME-Tox properties
DOI: 10.25345/c57m0498f
title: MassIVE MSV000092644 - Stable-isotope analysis of TFA and MRFA using ESI-Orbitrap IRMS
title: data analyzed within Procedure 1 in Kantnerova et al. 202X in Nature protocols, doi:
DOI: 10.5281/zenodo.8034239
title: Evaluation of Massive Education in Prison Health: a perspective of health care for the person deprived of freedom in Brazil

description: <strong>Dataset name</strong>: data_survey.xlsx <br> <strong>Version</strong>: 1.0 <br> <strong>Dataset period</strong>: 03/23/2022 - 06/30/2022<br> <strong>Dataset Characteristics</strong>: Multivalued <br> <strong>Number of Instances</strong>: 270<br> <strong>Number of Attributes</strong>: 88<br> <strong>Missing Values</strong>: Yes<br> <strong>Area(s)</strong>: Health and education <br> <strong>Description</strong>: The data contained in the "data_survey.xlsx" dataset (Table 1, in the README) originate from a questionnaire (available in the download files section) composed of 37 questions administered to students who completed the course "Health Care for People Deprived of Freedom" from the learning path "Prison System." This course is a strategy for mass education in prison health in Brazil and is available in the Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2023). This dataset provides information about the course from the perspective of health professionals and other staff who work or wish to work in the Brazilian prison system.<br> <strong>Note</strong>: The dataset's content is provided in Brazilian Portuguese (pt-br) from native speakers.
DOI: 10.5281/zenodo.8000113
title: Evaluation of Massive Education in Prison Health: a perspective of health care for the person deprived of freedom in Brazil

description: <strong>Dataset name</strong>: data_survey.xlsx <br> <strong>Version</strong>: 1.0 <br> <strong>Dataset period</strong>: 03/23/2022 - 06/30/2022<br> <strong>Dataset Characteristics</strong>: Multivalued <br> <strong>Number of Instances</strong>: 270<br> <strong>Number of Attributes</strong>: 88<br> <strong>Missing Values</strong>: Yes<br> <strong>Area(s)</strong>: Health and education <br> <strong>Description</strong>: The data contained in the "data_survey.xlsx" dataset (Table 1, in the README) originate from a questionnaire (available in the download files section) composed of 37 questions administered to students who completed the course "Health Care for People Deprived of Freedom" from the learning path "Prison System." This course is a strategy for mass education in prison health in Brazil and is available in the Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2023). This dataset provides information about the course from the perspective of health professionals and other staff who work or wish to work in the Brazilian prison system.<br> <strong>Note</strong>: The dataset's content is provided in Brazilian Portuguese (pt-br) from native speakers.
DOI: 10.5281/zenodo.8239702
title: SIF and CLM5 outpfile files to support Kunik et al "Satellite-based solar-induced fluorescence tracks seasonal and elevational patterns of photosynthesis in California's Sierra Nevada mountains"

description: These files contain 0.04° monthly sampled TROPOMI SIF, corrected for length of day and topography ("SIFdc_dem") over the Sierra Nevada region of California, along with Community Land Model (CLM) v5.0 point and regional simulation output. CLM5.0 simulations with prognostic vegetation state (CLM5.0-BGC, files begninning with "clm5_") and with satellite phenology (CLM5.0-SP, files beginning with "clm5_SP_") are provided.
DOI: 10.5281/zenodo.8239701
title: SIF and CLM5 outpfile files to support Kunik et al "Satellite-based solar-induced fluorescence tracks seasonal and elevational patterns of photosynthesis in California's Sierra Nevada mountains"

description: These files contain 0.04° monthly sampled TROPOMI SIF, corrected for length of day and topography ("SIFdc_dem") over the Sierra Nevada region of California, along with Community Land Model (CLM) v5.0 point and regional simulation output. CLM5.0 simulations with prognostic vegetation state (CLM5.0-BGC, files begninning with "clm5_") and with satellite phenology (CLM5.0-SP, files beginning with "clm5_SP_") are provided.
DOI: 10.5281/zenodo.8240315
title: Research data from the article "The Populist Ambivalence. Presidents and Democracy in Latin America"

description: Research data from the article "The Populist Ambivalence. Presidents and Democracy in Latin America": dataset, truth table, prime implicants and list of populist presidents with sources supporting that they are populist
DOI: 10.5281/zenodo.8240316
title: Research data from the article "The Populist Ambivalence. Presidents and Democracy in Latin America"

description: Research data from the article "The Populist Ambivalence. Presidents and Democracy in Latin America": dataset, truth table, prime implicants and list of populist presidents with sources supporting that they are populist
DOI: 10.7910/dvn/ttvips
title: test

description: test
DOI: 10.34740/kaggle/dsv/6289179
title: Youtube Trending Video - Taiwan & Hong Kong
DOI: 10.15156/bio/sh1576055.08fu
title: SH1576055.08FU

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/sh1576056.08fu
title: SH1576056.08FU

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/sh1576057.08fu
title: SH1576057.08FU

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: 10.25549/one-c4-59323
title: Jack M., letters (1961)

description: Jack M., envelope, note (1961)
DOI: 10.15156/bio/sh1369430.08fu
title: SH1369430.08FU

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/sh1369431.08fu
title: SH1369431.08FU

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/sh1576058.08fu
title: SH1576058.08FU

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/sh1576059.08fu
title: SH1576059.08FU

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/sh1369432.08fu
title: SH1369432.08FU

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/sh1369433.08fu
title: SH1369433.08FU

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.