cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11353043.v1
title: Additional file 1. Datasets and comparative analyses of Senticaudata mitogenomes. from Disentangling the interplay of positive and negative selection forces that shaped mitochondrial genomes of <i>Gammarus pisinnus</i> and <i>Gammarus lacustris</i>

description: We hypothesized that the mitogenome of <i>Gammarus lacustris</i> (GL), native to the Qinghai-Tibet Plateau, might exhibit genetic adaptations to the extreme environmental conditions associated with high altitudes (greater than 3000 m). To test this, we also sequenced the mitogenome of <i>Gammarus pisinnus</i> (GP), whose native range is close to the Tibetan plateau, but at a much lower altitude (200–1500 m). The two mitogenomes exhibited conserved mitochondrial architecture, but low identity between genes (55% <i>atp8</i> to 76.1% <i>cox1</i>). Standard (homogeneous) phylogenetic models resolved Gammaridae as paraphyletic, but ‘heterogeneous’ CAT-GTR model as monophyletic. In indirect support of our working hypothesis, GL, <i>pisinnus</i> and <i>fossarum</i> exhibit evidence of episodic diversifying selection within the studied Gammaroidea dataset. The mitogenome of GL generally evolves under a strong purifying selection, whereas GP evolves under directional (especially pronounced in <i>atp8</i>) and/or relaxed selection. This is surprising, as GP does not inhabit a unique ecological niche compared to other gammarids. We propose that this rapid evolution of the GP mitogenome may be a reflection of its relatively recent speciation and heightened non-adaptive (putatively metabolic rate-driven) mutational pressures. To test these hypotheses, we urge sequencing mitogenomes of remaining <i>Gammarus</i> species populating the same geographical range as GP.
DOI: 10.6084/m9.figshare.17120249
title: Additional file 10 of Identification and expression analysis of the glycosyltransferase GT43 family members in bamboo reveal their potential function in xylan biosynthesis during rapid growth

description: Additional file 10: Table S7. Primers used for RT-qPCR.
DOI: 10.6084/m9.figshare.6634424.v1
title: A clinical carepath for obese pregnant women: a pragmatic pilot cluster randomized controlled trial

description: <b>Objectives:</b> Obese women are at increased risks for complications during pregnancy, birth, and in their infants. Although guidelines have been established for the clinical care of obese pregnant women, management is sometimes suboptimal. Our goal was to determine the feasibility of implementing and testing a clinical carepath for obese pregnant women compared to standard care, in a pilot cluster randomized controlled trial (RCT). <b>Methods:</b> A pragmatic pilot cluster RCT was conducted, randomly allocating eight clinics to the carepath or standard care for obese pregnant women. Women were eligible if they had a pre-pregnancy body mass index (BMI) of ≥30 kg/m<sup>2</sup> and a viable singleton &lt;21 weeks. The primary outcomes were the feasibility of conducting a full-scale cluster RCT (defined as &gt;80%: randomization of clinics, use in eligible women, and completeness of follow-up) and of the intervention (defined as &gt;80%: compliance with each step in the carepath and recommendation of the carepath by clinicians to a colleague). <b>Results:</b> All eight approached clinics agreed to participate and were randomized. Half of the intervention clinics used the carepath, resulting in &lt;80% uptake of eligible women. High follow-up (99.5%) was achieved, in 188 of 189 women. The carepath was feasible for numerous guideline-directed recommendations for screening, but less so for counseling topics. When the carepath was used in the majority of women, all clinicians, most of whom were midwives, reported they would recommend it to a colleague. The intervention group had significantly higher overall adherence to the guideline recommendations compared to control (relative risk: 1.71, 95% confidence interval: 1.57–1.87). <b>Conclusions:</b> In this pragmatic pilot cluster RCT, a guideline-directed clinical carepath improved some aspects of care of obese pregnant women and was recommended by clinicians, particularly midwives. A cluster RCT may not be feasible in a mix of obstetric and midwifery clinics, but may be feasible in midwifery clinics.
DOI: 10.6084/m9.figshare.11353046.v1
title: Additional file 4. Partitioning scheme, evolutionary models and alignments used for phylogenetic and selection analyses. from Disentangling the interplay of positive and negative selection forces that shaped mitochondrial genomes of <i>Gammarus pisinnus</i> and <i>Gammarus lacustris</i>

description: We hypothesized that the mitogenome of <i>Gammarus lacustris</i> (GL), native to the Qinghai-Tibet Plateau, might exhibit genetic adaptations to the extreme environmental conditions associated with high altitudes (greater than 3000 m). To test this, we also sequenced the mitogenome of <i>Gammarus pisinnus</i> (GP), whose native range is close to the Tibetan plateau, but at a much lower altitude (200–1500 m). The two mitogenomes exhibited conserved mitochondrial architecture, but low identity between genes (55% <i>atp8</i> to 76.1% <i>cox1</i>). Standard (homogeneous) phylogenetic models resolved Gammaridae as paraphyletic, but ‘heterogeneous’ CAT-GTR model as monophyletic. In indirect support of our working hypothesis, GL, <i>pisinnus</i> and <i>fossarum</i> exhibit evidence of episodic diversifying selection within the studied Gammaroidea dataset. The mitogenome of GL generally evolves under a strong purifying selection, whereas GP evolves under directional (especially pronounced in <i>atp8</i>) and/or relaxed selection. This is surprising, as GP does not inhabit a unique ecological niche compared to other gammarids. We propose that this rapid evolution of the GP mitogenome may be a reflection of its relatively recent speciation and heightened non-adaptive (putatively metabolic rate-driven) mutational pressures. To test these hypotheses, we urge sequencing mitogenomes of remaining <i>Gammarus</i> species populating the same geographical range as GP.
DOI: 10.6084/m9.figshare.8207324.v1
title: Additional file 3: of Phylogeny, Divergent Evolution, and Speciation of Sulfur-Oxidizing Acidithiobacillus Populations

description: Table S5. Statistics for puative transposases in Acidithiobacillus genomes using the online tool ISFinder. (XLSX 13 kb)
DOI: 10.6084/m9.figshare.17120267.v1
title: Additional file 2 of Identification and expression analysis of the glycosyltransferase GT43 family members in bamboo reveal their potential function in xylan biosynthesis during rapid growth

description: Additional file 2: Table S2. Ka / Ks and divergence time analysis of PeGT43s pairs.
DOI: 10.6084/m9.figshare.8207330.v1
title: Additional file 4: of Phylogeny, Divergent Evolution, and Speciation of Sulfur-Oxidizing Acidithiobacillus Populations

description: Table S6. Prediction of putative phage-associated genes in the genomes of Acidithiobacillus strains, including A. caldus strains SM-1, MTH-04, and ATCC 51756, A. ferrooxidans ATCC 23270, and A. thiooxidans DSM 14366. (XLSX 38 kb)
DOI: 10.6084/m9.figshare.12923031.v1
title: Additional file 1 of Exploring the protective effect of Modified Xiaochaihu Decoction against hepatic steatosis and inflammation by network pharmacology and validation in ageing rats

description: Additional file 1: Table S1. List of active ingredients in Modified Xiaochaihu Decoction (MXD).
DOI: 10.5281/zenodo.6058147
title: Salt Induced Transitions in Structural Ensemble of Intrinsically Disordered Proteins

description: Simulation data and the corrosponding analysis script for the work "<strong>Salt Induced Transitions in Conformational Ensemble of Intrinsically Disordered Proteins</strong> " by <em>Hiranmay Maity, Lipika Baidya </em>and<em> Govardhan reddy</em> are deposited here. Analysis Scripts: The scripts for analysing the simulation data are in analysis_script.zip. The folder contains: autocorrelation.c : code for calculating end_to_end distance autocorrelation function with time in C. average_property.cpp: code for calculating average property such as radius of gyration (R<sub>g</sub>) from trajectory files in C++. calculate_saxs_kratky.c: code for calculating scattering profile (SAXS and Kratky) from simulation data in C. compute_contact_map.cpp: code for calculating contact map in C. probablity_distribution.c: code for calculating probablity distribution of Rg in C. structure_factor.c: code for calculating structure factor in C.
DOI: 10.5281/zenodo.6058148
title: Salt Induced Transitions in Structural Ensemble of Intrinsically Disordered Proteins

description: Simulation data and the corrosponding analysis script for the work "<strong>Salt Induced Transitions in Conformational Ensemble of Intrinsically Disordered Proteins</strong> " by <em>Hiranmay Maity, Lipika Baidya </em>and<em> Govardhan reddy</em> are deposited here. Analysis Scripts: The scripts for analysing the simulation data are in analysis_script.zip. The folder contains: autocorrelation.c : code for calculating end_to_end distance autocorrelation function with time in C. average_property.cpp: code for calculating average property such as radius of gyration (R<sub>g</sub>) from trajectory files in C++. calculate_saxs_kratky.c: code for calculating scattering profile (SAXS and Kratky) from simulation data in C. compute_contact_map.cpp: code for calculating contact map in C. probablity_distribution.c: code for calculating probablity distribution of Rg in C. structure_factor.c: code for calculating structure factor in C.
DOI: 10.5281/zenodo.7953667
title: Salt Induced Transitions in Structural Ensemble of Intrinsically Disordered Proteins

description: Simulation data and the corrosponding analysis script for the work "<strong>Salt Induced Transitions in Conformational Ensemble of Intrinsically Disordered Proteins</strong> " by <em>Hiranmay Maity, Lipika Baidya </em>and<em> Govardhan reddy</em> are deposited here. Analysis Scripts: The scripts for analysing the simulation data are in analysis_script.zip. The folder contains: autocorrelation.c : code for calculating end_to_end distance autocorrelation function with time in C. average_property.cpp: code for calculating average property such as radius of gyration (R<sub>g</sub>) from trajectory files in C++. calculate_saxs_kratky.c: code for calculating scattering profile (SAXS and Kratky) from simulation data in C. compute_contact_map.cpp: code for calculating contact map in C. probablity_distribution.c: code for calculating probablity distribution of Rg in C. structure_factor.c: code for calculating structure factor in C.
DOI: 10.6084/m9.figshare.22993208
title: Dataset used for CSY3025 assignment 2

description: Dataset used for CSY3025 assignment 2
DOI: 10.6084/m9.figshare.22993208.v1
title: Dataset used for CSY3025 assignment 2

description: Dataset used for CSY3025 assignment 2
DOI: 10.34740/kaggle/dsv/5731521
title: Balochi Language to English Translation Dataset
DOI: 10.5281/zenodo.7953734
title: micropsitta_pusio_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.7953733
title: micropsitta_pusio_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.7953749
title: micropsitta_pusio_decontaminated_gx.fasta.gz

description: .
DOI: 10.5281/zenodo.7953748
title: micropsitta_pusio_decontaminated_gx.fasta.gz

description: .
DOI: 10.34740/kaggle/dsv/5731764
title: "Tripadvisor_New Zealand_Hotels_3k dataset"
DOI: 10.5517/ccdc.csd.cc2c3n9g
title: CCDC 2178255: Experimental Crystal Structure Determination

description: Related Article: Luyao Wang, Mengting Li, Zhitao Ning, Xi Zhang, Ying Fu, Zhengyin Du|2022|J.Org.Chem.|88|6374|doi:10.1021/acs.joc.2c01713
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.3761562.v2591
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.22996313
title: Do Conditional Cash Transfers Reduce Fertility? Nationwide Evidence from Mexico

description: (1) README file, (2) do-file for STATA which produces tables in the paper, and (3) two datasets (Progresa beneficiary data and municipality characteristics data) are included. Datasets for registered births and the number of women by five-year age group are available in INEGI (Instituto Nacional de Estadistica y Geografia).
DOI: 10.6084/m9.figshare.22997099
title: dataset.zip

description: dataset
DOI: 10.6084/m9.figshare.22997147
title: <strong>Molecular confirmation of HIV-1 and HIV-2 coinfections among initially serologically dually-reactive samples from patients living in West Africa</strong>

description: this dataset contains data describing the population of patient used for retesting and molecular diagnosis of HIV-1 and HIV-2 dually reactive cleints in West Africa.
DOI: 10.6084/m9.figshare.22997147.v1
title: <strong>Molecular confirmation of HIV-1 and HIV-2 coinfections among initially serologically dually-reactive samples from patients living in West Africa</strong>

description: this dataset contains data describing the population of patient used for retesting and molecular diagnosis of HIV-1 and HIV-2 dually reactive cleints in West Africa.
DOI: 10.6084/m9.figshare.22927658
title: Population and screening analysis of Salt/gypsum strains

description: Here we included all the population analysis, as well as the screening thest to select the best candidates
DOI: 10.6084/m9.figshare.22927658.v4
title: Population and screening analysis of Salt/gypsum strains

description: Here we included all the population analysis, as well as the screening thest to select the best candidates
DOI: 10.6084/m9.figshare.22927658.v3
title: Population and screening analysis of Salt/gypsum strains

description: Here we included all the population analysis, as well as the screening thest to select the best candidates
DOI: 10.34740/kaggle/dsv/5732321
title: arXiv Dataset
DOI: 10.34740/kaggle/dsv/5732429
title: Youtube Trending Video - Taiwan & Hong Kong
DOI: 10.22002/cc6t2-5e805
title: River floc data extracted from a river suspended sediment concentration-depth profile data compilation

description: The file "riverfloc_datacompilation.csv" contains the data in csv format. The file "metadata.txt" contains the metadata describing the data in the csv file.
This version corrects an error in which the depth-averaged total and mud concentrations (variables 37 and 38) tended to be overestimated. In addition, the total near-bed concentration (variables 15-17) is updated to include the near-bed concentrations of grain size classes that were removed during data filtering.
description: Related Publication:
A Mechanistic Model for Mud Flocculation in Freshwater Rivers

Justin Nghiem Caltech

Woodward W. Fischer Caltech

Gen K. Li Caltech

Michael P. Lamb Caltech
eng
DOI: 10.6084/m9.figshare.22998800
title: biomass_grasslands_pantanal.xlsx

description: We distributed 36 sample sites among the three main types of flooded grasslands in the northern region: (1) small tussock grassland (<em>campo de murundu</em>) with a short flooding period of up to 1 month; (2) tall tussock grassland (<em>campo rabo-de-burro</em>) with flooding up to 3 months; and (3) shortgrass floodplain (<em>campo limpo</em>) with long-lasting flooding up to 6 months. In these grassland types ee quantified vegetation biomass for the following compartments: herbaceous aboveground standing biomass; woody aboveground biomass, accumulated litter and belowground total matter.
DOI: 10.6084/m9.figshare.22998800.v1
title: biomass_grasslands_pantanal.xlsx

description: We distributed 36 sample sites among the three main types of flooded grasslands in the northern region: (1) small tussock grassland (<em>campo de murundu</em>) with a short flooding period of up to 1 month; (2) tall tussock grassland (<em>campo rabo-de-burro</em>) with flooding up to 3 months; and (3) shortgrass floodplain (<em>campo limpo</em>) with long-lasting flooding up to 6 months. In these grassland types ee quantified vegetation biomass for the following compartments: herbaceous aboveground standing biomass; woody aboveground biomass, accumulated litter and belowground total matter.
DOI: 10.5281/zenodo.7903160
title: Large-scale Docking Datasets for Machine Learning

description: <strong>Large-scale virtual screening has become a valuable tool for early-phase drug discovery. Recent expansions of commercial chemical space have made it computationally intractable to evaluate all compounds in the libraries. Machine learning is one of the methods that aim to prioritize specific subsets of these vast libraries. In order to put these methods to the test, access to large-scale datasets is beneficial. To help the community benchmark their work, we share the docking scores of several ultralarge virtual screening campaigns.</strong> <strong>The datasets we provide contain canonical SMILES, compound identifiers, and docking scores. We docked two different chemical libraries against eight different biological targets with therapeutic relevance. The first dataset contained approximately 15.5 million molecules adhering to the "Rule-of-Four", whereas the second datasets consists of approximately 235 million "lead-like" molecules. The biological targets represent different classes of proteins and binding sites.</strong> <strong>More details on the datasets and our methods can be found on (https://github.com/carlssonlab/conformalpredictor) and our pre-print (https://doi.org/10.26434/chemrxiv-2023-w3x36). </strong> <strong>Please feel free to download and use these datasets for your own research purposes. We only ask that you cite our pre-print and datasets appropriately if you use it in your work. Thank you for your interest in our research!</strong>
DOI: 10.5281/zenodo.7903161
title: Large-scale Docking Datasets for Machine Learning

description: <strong>Large-scale virtual screening has become a valuable tool for early-phase drug discovery. Recent expansions of commercial chemical space have made it computationally intractable to evaluate all compounds in the libraries. Machine learning is one of the methods that aim to prioritize specific subsets of these vast libraries. In order to put these methods to the test, access to large-scale datasets is beneficial. To help the community benchmark their work, we share the docking scores of several ultralarge virtual screening campaigns.</strong> <strong>The datasets we provide contain canonical SMILES, compound identifiers, and docking scores. We docked two different chemical libraries against eight different biological targets with therapeutic relevance. The first dataset contained approximately 15.5 million molecules adhering to the "Rule-of-Four", whereas the second datasets consists of approximately 235 million "lead-like" molecules. The biological targets represent different classes of proteins and binding sites.</strong> <strong>More details on the datasets and our methods can be found on (https://github.com/carlssonlab/conformalpredictor) and our pre-print (https://doi.org/10.26434/chemrxiv-2023-w3x36). </strong> <strong>Please feel free to download and use these datasets for your own research purposes. We only ask that you cite our pre-print and datasets appropriately if you use it in your work. Thank you for your interest in our research!</strong>
DOI: 10.5281/zenodo.7953917
title: Large-scale Docking Datasets for Machine Learning

description: <strong>Large-scale virtual screening has become a valuable tool for early-phase drug discovery. Recent expansions of commercial chemical space have made it computationally intractable to evaluate all compounds in the libraries. Machine learning is one of the methods that aim to prioritize specific subsets of these vast libraries. In order to put these methods to the test, access to large-scale datasets is beneficial. To help the community benchmark their work, we share the docking scores of several ultralarge virtual screening campaigns.</strong> <strong>The datasets we provide contain canonical SMILES, compound identifiers, and docking scores. We docked two different chemical libraries against eight different biological targets with therapeutic relevance. The first dataset contained approximately 15.5 million molecules adhering to the "Rule-of-Four", whereas the second datasets consists of approximately 235 million "lead-like" molecules. The biological targets represent different classes of proteins and binding sites.</strong> <strong>More details on the datasets and our methods can be found on (https://github.com/carlssonlab/conformalpredictor) and our pre-print (https://doi.org/10.26434/chemrxiv-2023-w3x36). </strong> <strong>Please feel free to download and use these datasets for your own research purposes. We only ask that you cite our pre-print and datasets appropriately if you use it in your work. Thank you for your interest in our research!</strong>
DOI: 10.12755/hitap.res.2021.1
title: คู่มือการประเมินเทคโนโลยีด้านสุขภาพสําหรับประเทศไทย ฉบับปรับปรุง พ.ศ. 2564

description: การประเมินเทคโนโลยีด้านสุขภาพ (Health Technology Assessment หรือ HTA) เป็นการวิเคราะห์อย่างเป็นระบบ เพื่อให้ได้มาซึ่งข้อมูลสำหรับใช้สนับสนุนการตัดสินใจเชิงนโยบายเกี่ยวกับการนำเทคโนโลยีดังกล่าวมาใช้ในระบบสุขภาพ การประเมินต้องคำนึงถึงหลายปัจจัยร่วมกัน เช่น ความปลอดภัย ประสิทธิผล ต้นทุน ความคุ้มค่า ผลกระทบต่อสังคมและจริยธรรม ที่อาจเกิดขึ้น ซึ่งรวมทั้งผลกระทบทางตรง และผลกระทบทางอ้อมของการใช้เทคโนโลยีด้านสุขภาพนั้น คู่มือการประเมินเทคโนโลยีด้านสุขภาพถือเป็นรากฐานสำคัญและเป็นแนวทางด้านเทคนิคสำหรับการประเมินเทคโนโลยีในระบบสุขภาพ โดยสมัชชาอนามัยโลก (World Health Assembly) ได้ระบุถึงความสำคัญของการมีแนวทางการประเมินเทคโนโลยีด้านสุขภาพและการประเมินความคุ้มค่าด้านเศรษฐศาสตร์สาธารณสุข (health economic evaluation) ซึ่งจำเป็นต้องกำหนดให้กระบวนการต่าง ๆ ไม่ว่าจะเป็นการออกแบบการดำเนินงาน รวมถึงการรายงานผล เป็นมาตรฐานและสอดคล้องกับบริบทของแต่ละประเทศ ประเทศไทย มีการพัฒนาคู่มือการประเมินเทคโนโลยีด้านสุขภาพมาแล้ว 2 ฉบับ ฉบับที่ 1 เมื่อ พ.ศ. 2551 และฉบับที่ 2 เมื่อ พ.ศ. 2557 คู่มือฯ ทั้งสองฉบับนี้ เสนอแนะแนวทางการเลือกวิธีวิเคราะห์และแหล่งข้อมูลสำหรับใช้ในการประเมินอย่างเป็นระบบ จึงทำให้การประเมินที่ดำเนินการตามคำแนะนำในคู่มือๆ ดังกล่าวมีความโปร่งใสและสามารถตรวจสอบได้ คู่มือฯ ทั้งสองฉบับเป็นคู่มือหลักของคณะทำงานด้านเศรษฐศาสตร์สาธารณสุข คณะอนุกรรมการพัฒนาบัญชียาหลักแห่งชาติ และคณะอนุกรรมการพัฒนาชุดสิทธิประโยชน์และระบบบริการ สำนักงานหลักประกันสุขภาพถ้วนหน้า โดยกำหนดให้นักวิจัยที่ประเมินเทคโนโลยีสุขภาพเพื่อใช้เป็นข้อมูลประกอบการพิจารณาคัดเลือกยาเข้าสู่บัญชียาหลักแห่งชาติหรือคัดเลือกเทคโนโลยีอื่น ๆ เข้าเป็นชุดสิทธิประโยชน์ นอกจากนี้ คู่มือฯ ยังสามารถใช้เป็นตำราทางวิชาการของการประเมินเทคโนโลยีด้านสุขภาพและการประเมินความคุ้มค่าทางเศรษฐศาสตร์สาธารณสุข ในการเรียนการสอนสำหรับผู้ที่สนใจ นักศึกษาระดับปริญญาตรีและบัณฑิตศึกษา หรือผู้ที่เกี่ยวข้องและมีส่วนได้ส่วนเสียกับเทคโนโลยีหรือการประเมินเทคโนโลยีด้านสุขภาพ เช่น สมาคมวิจัยเภสัชภัณฑ์ และหน่วยงานไม่แสวงหาผลกำไร เช่น กลุ่มผู้ป่วยที่ได้รับผลกระทบจากเทคโนโลยีด้านสุขภาพนั้น ทั้งนี้ คู่มือฯ ควรมีการพัฒนาอย่างต่อเนื่อง เพื่อให้สอดคล้องกับองค์ความรู้ด้านเทคโนโสยีสุขภาพที่เปลี่ยนแปลงไป ซึ่งสร้างความท้าทายใหม่ให้แก่นักวิชาการด้านการประเมินเทคโนโลยีด้านสุขภาพ
DOI: 10.25582/data.2022-05.2519236/1931000
title: Data for EMSL Project 51596 from May 2022