cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e183346
title: Data and Code for: Private Input Suppliers as Information Agents for Technology Adoption in Agriculture

description: Information frictions limit the adoption of new agricultural technologies in developing countries. Efforts to improve learning involve spreading information from government agents to farmers. We show that when compared to this government approach, informing private input suppliers in India about a new seed variety increases farmer-level adoption by over 50 percent. Suppliers become more proactive in informing potential customers and carrying the new variety. They induce increased adoption by those with higher returns from the technology. Being motivated by expanded sales offers the most likely motive for these results.
description: Smallholder farmers in Odisha India.
DOI: 10.3886/e183346v1
title: Data and Code for: Private Input Suppliers as Information Agents for Technology Adoption in Agriculture

description: Information frictions limit the adoption of new agricultural technologies in developing countries. Efforts to improve learning involve spreading information from government agents to farmers. We show that when compared to this government approach, informing private input suppliers in India about a new seed variety increases farmer-level adoption by over 50 percent. Suppliers become more proactive in informing potential customers and carrying the new variety. They induce increased adoption by those with higher returns from the technology. Being motivated by expanded sales offers the most likely motive for these results.
description: Smallholder farmers in Odisha India.
DOI: 10.48597/4qu7-94a4
title: Measurement of Susp_part_matter at Meinerzhagen

description: Measurement of Susp_part_matter at Meinerzhagen
DOI: 10.48597/4us9-jqt9
title: Measurement of Sulphate_total at Bassum

description: Measurement of Sulphate_total at Bassum
DOI: 10.48597/56yk-spg8
title: Measurement of Sulphate_total at Meinerzhagen

description: Measurement of Sulphate_total at Meinerzhagen
DOI: 10.48597/5g3w-tk3k
title: Measurement of Sulphate_total at Rottenburg

description: Measurement of Sulphate_total at Rottenburg
DOI: 10.48597/5z3b-44qv
title: Measurement of Sulphate_total at Ansbach

description: Measurement of Sulphate_total at Ansbach
DOI: 10.48597/66fb-3war
title: Measurement of Susp_part_matter at Bassum

description: Measurement of Susp_part_matter at Bassum
DOI: 10.48597/6pbj-j7jf
title: Measurement of Susp_part_matter at Bassum

description: Measurement of Susp_part_matter at Bassum
DOI: 10.48597/6sd9-rmgb
title: Measurement of Susp_part_matter at Bassum

description: Measurement of Susp_part_matter at Bassum
DOI: 10.48597/77zz-99p4
title: Measurement of Sulphate_total at Ansbach

description: Measurement of Sulphate_total at Ansbach
DOI: 10.48597/7e9b-jtjt
title: Measurement of Sulphate_total at Meinerzhagen

description: Measurement of Sulphate_total at Meinerzhagen
DOI: 10.48597/7jdc-jrj8
title: Measurement of Sulphate_total at Bassum

description: Measurement of Sulphate_total at Bassum
DOI: 10.48597/7qyp-f4tg
title: Measurement of Sulphate_total at Bassum

description: Measurement of Sulphate_total at Bassum
DOI: 10.48597/7t86-45gw
title: Measurement of Sulphate_total at Rottenburg

description: Measurement of Sulphate_total at Rottenburg
DOI: 10.48597/8cc9-g24q
title: Measurement of Sulphate_total at Meinerzhagen

description: Measurement of Sulphate_total at Meinerzhagen
DOI: 10.48597/8h4q-ehqa
title: Measurement of Sulphate_total at Meinerzhagen

description: Measurement of Sulphate_total at Meinerzhagen
DOI: 10.48597/8h9k-8aj2
title: Measurement of Sulphate_total at Ansbach

description: Measurement of Sulphate_total at Ansbach
DOI: 10.48597/8j2h-zv3r
title: Measurement of Sulphate_total at Bassum

description: Measurement of Sulphate_total at Bassum
DOI: 10.48597/8njk-6s8s
title: Measurement of Sulphate_total at Ansbach

description: Measurement of Sulphate_total at Ansbach
DOI: 10.48597/8wvw-7t7x
title: Measurement of Sulphate_total at Ansbach

description: Measurement of Sulphate_total at Ansbach
DOI: 10.48597/97m3-3d7p
title: Measurement of Sulphate_total at Rottenburg

description: Measurement of Sulphate_total at Rottenburg
DOI: 10.48597/9cnd-xyf2
title: Measurement of Sulphate_total at Ansbach

description: Measurement of Sulphate_total at Ansbach
DOI: 10.48597/9tkw-srm7
title: Measurement of Susp_part_matter at Meinerzhagen

description: Measurement of Susp_part_matter at Meinerzhagen
DOI: 10.48597/a4bg-t9au
title: Measurement of Sulphate_total at Meinerzhagen

description: Measurement of Sulphate_total at Meinerzhagen
DOI: 10.48597/a9d4-szdh
title: Measurement of Sulphate_total at Bassum

description: Measurement of Sulphate_total at Bassum
DOI: 10.48597/a9s2-r66x
title: Measurement of Susp_part_matter at Bassum

description: Measurement of Susp_part_matter at Bassum
DOI: 10.48597/aeeh-mepp
title: Measurement of Sulphate_total at Rottenburg

description: Measurement of Sulphate_total at Rottenburg
DOI: 10.48597/az29-e37h
title: Measurement of Sulphate_total at Bassum

description: Measurement of Sulphate_total at Bassum
DOI: 10.48597/b3q5-dz2n
title: Measurement of Susp_part_matter at Meinerzhagen

description: Measurement of Susp_part_matter at Meinerzhagen
DOI: 10.48597/ca38-65gm
title: Measurement of Sulphate_total at Rottenburg

description: Measurement of Sulphate_total at Rottenburg
DOI: 10.48597/cfw6-x5np
title: Measurement of Susp_part_matter at Meinerzhagen

description: Measurement of Susp_part_matter at Meinerzhagen
DOI: 10.48597/cg33-zyn8
title: Measurement of Susp_part_matter at Bassum

description: Measurement of Susp_part_matter at Bassum
DOI: 10.48597/cqmv-ja3t
title: Measurement of Sulphate_total at Rottenburg

description: Measurement of Sulphate_total at Rottenburg
DOI: 10.48597/ctt7-peb2
title: Measurement of Sulphate_total at Rottenburg

description: Measurement of Sulphate_total at Rottenburg
DOI: 10.48597/d5zh-pb6r
title: Measurement of Susp_part_matter at Meinerzhagen

description: Measurement of Susp_part_matter at Meinerzhagen
DOI: 10.48597/dj6w-vpu9
title: Measurement of Sulphate_total at Ansbach

description: Measurement of Sulphate_total at Ansbach
DOI: 10.48597/dmuy-bb62
title: Measurement of Susp_part_matter at Meinerzhagen

description: Measurement of Susp_part_matter at Meinerzhagen
DOI: 10.48597/dqy2-ecsf
title: Measurement of Sulphate_total at Bassum

description: Measurement of Sulphate_total at Bassum
DOI: 10.48597/dtr8-4t2d
title: Measurement of Sulphate_total at Ansbach

description: Measurement of Sulphate_total at Ansbach
DOI: 10.48597/e9f5-nypv
title: Measurement of Susp_part_matter at Bassum

description: Measurement of Susp_part_matter at Bassum
DOI: 10.48597/e9k5-259f
title: Measurement of Sulphate_total at Ansbach

description: Measurement of Sulphate_total at Ansbach
DOI: 10.48597/efuj-35jv
title: Measurement of Susp_part_matter at Bassum

description: Measurement of Susp_part_matter at Bassum
DOI: 10.48597/f4wr-9trw
title: Measurement of Sulphate_total at Rottenburg

description: Measurement of Sulphate_total at Rottenburg
DOI: 10.48597/fr75-wdec
title: Measurement of Sulphate_total at Rottenburg

description: Measurement of Sulphate_total at Rottenburg
DOI: 10.48597/gee5-rcba
title: Measurement of Sulphate_total at Ansbach

description: Measurement of Sulphate_total at Ansbach
DOI: 10.48597/gqrs-xkts
title: Measurement of Sulphate_total at Bassum

description: Measurement of Sulphate_total at Bassum
DOI: 10.48597/gubd-uuxd
title: Measurement of Sulphate_total at Meinerzhagen

description: Measurement of Sulphate_total at Meinerzhagen
DOI: 10.48597/h6qj-xztq
title: Measurement of Sulphate_total at Meinerzhagen

description: Measurement of Sulphate_total at Meinerzhagen
DOI: 10.48597/hexy-4uup
title: Measurement of Sulphate_total at Bassum

description: Measurement of Sulphate_total at Bassum
DOI: 10.48597/hmfu-k4ys
title: Measurement of Sulphate_total at Rottenburg

description: Measurement of Sulphate_total at Rottenburg
DOI: 10.48597/hn77-sx9p
title: Measurement of Sulphate_total at Rottenburg

description: Measurement of Sulphate_total at Rottenburg
DOI: 10.48597/jed5-u88x
title: Measurement of Sulphate_total at Meinerzhagen

description: Measurement of Sulphate_total at Meinerzhagen
DOI: 10.48597/k34u-5pms
title: Measurement of Sulphate_total at Meinerzhagen

description: Measurement of Sulphate_total at Meinerzhagen
DOI: 10.48597/n5eh-k9dc
title: Measurement of Sulphate_total at Meinerzhagen

description: Measurement of Sulphate_total at Meinerzhagen
DOI: 10.48597/n82c-r5gn
title: Measurement of Sulphate_total at Rottenburg

description: Measurement of Sulphate_total at Rottenburg
DOI: 10.48597/ncyh-kh32
title: Measurement of Sulphate_total at Meinerzhagen

description: Measurement of Sulphate_total at Meinerzhagen
DOI: 10.48597/ndf5-pj7s
title: Measurement of Sulphate_total at Rottenburg

description: Measurement of Sulphate_total at Rottenburg
DOI: 10.48597/p39r-hjtx
title: Measurement of Sulphate_total at Rottenburg

description: Measurement of Sulphate_total at Rottenburg
DOI: 10.25387/g3.11494371
title: Supplemental Material for Gan et al., 2020

description: Figure S1 shows the quality distribution of
Illumina paired-end reads. Figure S2 shows the Length distribution of the
PacBio long reads. Figure S3 shows four K-mer analysis result of <i>Floccularia
luteovirens</i> genome. Figure S4 shows BUSCO assessment result of <i>F.
luteovirens</i> genome assembly. Figure S5 shows the distribution
of orthologous genes across 30 Basidiomycetes species. Figure S6 shows the time tree of 30 Basidiomycetes
species. Figure S7 shows the Venn diagram of gene
family distribution between<i> </i>four related fungi. Table S1 shows the sequencing data statistics of
<i>F. luteovirens</i>. Table S2 shows the K-mer analysis of <i>F. luteovirens</i>
genome. Table S3 shows the statistics of transposon elements in <i>F.
luteovirens</i>. Table S4 shows the brief genome comparison between <i>F.
luteovirens</i> and other fungi. Table S5 shows the functional annotation of
predicted genes. Table S6 shows the comparative analysis of Carbohydrate-active
enzymes between <i>F. luteovirens</i> and <i>A. bisporus</i>. Table S7 shows the
statistics of 30 Basidiomycetes species in phylogenetic tree. Table S8 shows the
statistics of orthologous genes across 30 Basidiomycetes species.
DOI: 10.48597/pbur-6cg2
title: Measurement of Susp_part_matter at Bassum

description: Measurement of Susp_part_matter at Bassum
DOI: 10.6084/m9.figshare.19358702
title: Readme.txt

description: Readme file for the description of shared files
DOI: 10.48597/pnpu-untz
title: Measurement of Sulphate_total at Ansbach

description: Measurement of Sulphate_total at Ansbach
DOI: 10.25387/g3.11494371.v1
title: Supplemental Material for Gan et al., 2020

description: Figure S1 shows the quality distribution of
Illumina paired-end reads. Figure S2 shows the Length distribution of the
PacBio long reads. Figure S3 shows four K-mer analysis result of <i>Floccularia
luteovirens</i> genome. Figure S4 shows BUSCO assessment result of <i>F.
luteovirens</i> genome assembly. Figure S5 shows the distribution
of orthologous genes across 30 Basidiomycetes species. Figure S6 shows the time tree of 30 Basidiomycetes
species. Figure S7 shows the Venn diagram of gene
family distribution between<i> </i>four related fungi. Table S1 shows the sequencing data statistics of
<i>F. luteovirens</i>. Table S2 shows the K-mer analysis of <i>F. luteovirens</i>
genome. Table S3 shows the statistics of transposon elements in <i>F.
luteovirens</i>. Table S4 shows the brief genome comparison between <i>F.
luteovirens</i> and other fungi. Table S5 shows the functional annotation of
predicted genes. Table S6 shows the comparative analysis of Carbohydrate-active
enzymes between <i>F. luteovirens</i> and <i>A. bisporus</i>. Table S7 shows the
statistics of 30 Basidiomycetes species in phylogenetic tree. Table S8 shows the
statistics of orthologous genes across 30 Basidiomycetes species.
DOI: 10.48597/ps2h-d2sf
title: Measurement of Susp_part_matter at Meinerzhagen

description: Measurement of Susp_part_matter at Meinerzhagen
DOI: 10.48597/ps9j-uk47
title: Measurement of Sulphate_total at Meinerzhagen

description: Measurement of Sulphate_total at Meinerzhagen
DOI: 10.48597/pt2a-8e69
title: Measurement of Susp_part_matter at Meinerzhagen

description: Measurement of Susp_part_matter at Meinerzhagen
DOI: 10.48597/q672-xasp
title: Measurement of Sulphate_total at Meinerzhagen

description: Measurement of Sulphate_total at Meinerzhagen
DOI: 10.48597/q8j8-nht8
title: Measurement of Susp_part_matter at Meinerzhagen

description: Measurement of Susp_part_matter at Meinerzhagen
DOI: 10.48597/qgcn-wc78
title: Measurement of Sulphate_total at Ansbach

description: Measurement of Sulphate_total at Ansbach
DOI: 10.48597/qqqc-7zs7
title: Measurement of Sulphate_total at Ansbach

description: Measurement of Sulphate_total at Ansbach
DOI: 10.6084/m9.figshare.19358702.v1
title: Readme.txt

description: Readme file for the description of shared files
DOI: 10.6084/m9.figshare.19358702.v2
title: Readme.txt

description: Readme file for the description of shared files
DOI: 10.48597/qtcr-a6dg
title: Measurement of Susp_part_matter at Meinerzhagen

description: Measurement of Susp_part_matter at Meinerzhagen
DOI: 10.48597/qxp2-ju43
title: Measurement of Susp_part_matter at Bassum

description: Measurement of Susp_part_matter at Bassum
DOI: 10.48597/rfa5-wd5k
title: Measurement of Susp_part_matter at Bassum

description: Measurement of Susp_part_matter at Bassum
DOI: 10.34740/kaggle/dsv/7731404
title: Progenitus
DOI: 10.6084/m9.figshare.14059870.v1
title: Additional file 6 of Genome-wide association studies detects candidate genes for wool traits by re-sequencing in Chinese fine-wool sheep

description: Additional file 6: Table S6. 460 phenotype data of wool traits.
DOI: 10.6084/m9.figshare.14059870
title: Additional file 6 of Genome-wide association studies detects candidate genes for wool traits by re-sequencing in Chinese fine-wool sheep

description: Additional file 6: Table S6. 460 phenotype data of wool traits.
DOI: 10.6084/m9.figshare.13067348
title: Summary_file2

description: Datasets generated
and/or analyzed during the current study. Summary of patients and samples analyzed.
DOI: 10.6084/m9.figshare.14640212
title: Prioritizing Autism Risk Genes Using Personalized Graphical Models Estimated From Single-Cell RNA-seq Data

description: Hundreds of autism risk genes have been reported recently, mainly based on genetic studies where these risk genes have more de novo mutations in autism subjects than healthy controls. However, as a complex disease, autism is likely associated with more risk genes and many of them may not be identifiable through de novo mutations. We hypothesize that more autism risk genes can be identified through their connections with known autism risk genes in personalized gene–gene interaction graphs. We estimate such personalized graphs using single-cell RNA sequencing (scRNA-seq) while appropriately modeling the cell dependence and possible zero-inflation in the scRNA-seq data. The sample size, which is the number of cells per individual, ranges from 891 to 1241 in our case study using scRNA-seq data in autism subjects and controls. We consider 1500 genes in our analysis. Since the number of genes is larger or comparable to the sample size, we perform penalized estimation. We score each gene’s relevance by applying a simple graph kernel smoothing method to each personalized graph. The molecular functions of the top-scored genes are related to autism diseases. For example, a candidate gene RYR2 that encodes protein ryanodine receptor 2 is involved in neurotransmission, a process that is impaired in ASD patients. While our method provides a systemic and unbiased approach to prioritize autism risk genes, the relevance of these genes needs to be further validated in functional studies. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.14640212.v1
title: Prioritizing Autism Risk Genes using Personalized Graphical Models Estimated from Single Cell RNA-seq Data

description: Hundreds of autism risk genes have been reported recently, mainly based on genetic studies where these risk genes have more de novo mutations in autism subjects than healthy controls. However, as a complex disease, autism is likely associated with more risk genes and many of them may not be identifiable through de novo mutations. We hypothesize that more autism risk genes can be identified through their connections with known autism risk genes in personalized gene-gene interaction graphs. We estimate such personalized graphs using single cell RNA sequencing (scRNA-seq) while appropriately modeling the cell dependence and possible zero-inflation in the scRNA-seq data. The sample size, which is the number of cells per individual, ranges from 891 to 1,241 in our case study using scRNA-seq data in autism subjects and controls. We consider 1,500 genes in our analysis. Since the number of genes is larger or comparable to the sample size, we perform penalized estimation. We score each gene’s relevance by applying a simple graph kernel smoothing method to each personalized graph. The molecular functions of the top-scored genes are related to autism diseases. For example, a candidate gene RYR2 that encodes protein ryanodine receptor 2 is involved in neurotransmission, a process that is impaired in ASD patients. While our method provides a systemic and unbiased approach to prioritize autism risk genes, the relevance of these genes needs to be further validated in functional studies.
DOI: 10.6084/m9.figshare.14640212.v2
title: Prioritizing Autism Risk Genes Using Personalized Graphical Models Estimated From Single-Cell RNA-seq Data

description: Hundreds of autism risk genes have been reported recently, mainly based on genetic studies where these risk genes have more de novo mutations in autism subjects than healthy controls. However, as a complex disease, autism is likely associated with more risk genes and many of them may not be identifiable through de novo mutations. We hypothesize that more autism risk genes can be identified through their connections with known autism risk genes in personalized gene–gene interaction graphs. We estimate such personalized graphs using single-cell RNA sequencing (scRNA-seq) while appropriately modeling the cell dependence and possible zero-inflation in the scRNA-seq data. The sample size, which is the number of cells per individual, ranges from 891 to 1241 in our case study using scRNA-seq data in autism subjects and controls. We consider 1500 genes in our analysis. Since the number of genes is larger or comparable to the sample size, we perform penalized estimation. We score each gene’s relevance by applying a simple graph kernel smoothing method to each personalized graph. The molecular functions of the top-scored genes are related to autism diseases. For example, a candidate gene RYR2 that encodes protein ryanodine receptor 2 is involved in neurotransmission, a process that is impaired in ASD patients. While our method provides a systemic and unbiased approach to prioritize autism risk genes, the relevance of these genes needs to be further validated in functional studies. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.3886/e183316v1
title: DATA AND CODE FOR: The Effects of Roads on Trade and Migration: Evidence from a Planned Capital City

description: This archive provides Data and Code to fully replicate the analysis in "The Effects of Roads on Trade and Migration: Evidence from a Planned Capital City." Please read the README.PDF for instructions.
description: Population resident in Brazil. Some analysis is restricted to working-age men with positive earnings.
DOI: 10.48597/rmkh-mrek
title: Measurement of Sulphate_total at Bassum

description: Measurement of Sulphate_total at Bassum
DOI: 10.48597/rp7z-7fej
title: Measurement of Susp_part_matter at Bassum

description: Measurement of Susp_part_matter at Bassum
DOI: 10.48597/s7y9-3zpf
title: Measurement of Susp_part_matter at Bassum

description: Measurement of Susp_part_matter at Bassum
DOI: 10.48597/skwc-kzd3
title: Measurement of Sulphate_total at Ansbach

description: Measurement of Sulphate_total at Ansbach
DOI: 10.48597/t4y4-9wzx
title: Measurement of Sulphate_total at Rottenburg

description: Measurement of Sulphate_total at Rottenburg
DOI: 10.48597/tjqv-k8d2
title: Measurement of Susp_part_matter at Bassum

description: Measurement of Susp_part_matter at Bassum
DOI: 10.48597/tkm2-5xtm
title: Measurement of Susp_part_matter at Bassum

description: Measurement of Susp_part_matter at Bassum
DOI: 10.48597/u43c-rzwh
title: Measurement of Susp_part_matter at Bassum

description: Measurement of Susp_part_matter at Bassum
DOI: 10.6084/m9.figshare.25148348
title: Evaluating contamination of seafood purchased from U.S. retail stores by persistent environmental pollutants, pesticides and veterinary drugs

description: Studies have reported health risks associated with seafood contamination, but few data exist on levels in commercially available seafood in the US. To better understand, the magnitude of foodborne exposure and identify vulnerable populations in the US, we measured concentrations of veterinary drugs, persistent organic pollutants (POPs) (polycyclic aromatic hydrocarbons [PAHs], polybrominated diphenyl ethers [PBDEs] and polychlorinated biphenyls [PCBs]), and legacy and current-use pesticides in 46 seafood samples purchased from retail outlets. Measured levels were used to estimate risk based on available maximum residue limits (MRLs) and toxic equivalence (TEQ) factors for analytes. Only seventeen of the 445 analytes were detected, at low substance frequencies. However, half of the samples tested positive for one or more analyte, with total concentrations ranging from below the limit of detection (LOD) to as high as 156 µg/kg wet weight. Based on the risk assessment for individual pesticides and veterinary drugs, the hazard quotients (HQ) were all &lt;1, indicating no risk. However, for the sum of PCB126 and PCB167, two dioxin-like PCBs detected in our samples, the TEQ was nearly two orders of magnitude higher than the WHO limits in one catfish sample. Moreover, vulnerable groups with higher rates of consumption of specific fish types may face higher risks.
DOI: 10.6084/m9.figshare.25148348.v1
title: Evaluating contamination of seafood purchased from U.S. retail stores by persistent environmental pollutants, pesticides and veterinary drugs

description: Studies have reported health risks associated with seafood contamination, but few data exist on levels in commercially available seafood in the US. To better understand, the magnitude of foodborne exposure and identify vulnerable populations in the US, we measured concentrations of veterinary drugs, persistent organic pollutants (POPs) (polycyclic aromatic hydrocarbons [PAHs], polybrominated diphenyl ethers [PBDEs] and polychlorinated biphenyls [PCBs]), and legacy and current-use pesticides in 46 seafood samples purchased from retail outlets. Measured levels were used to estimate risk based on available maximum residue limits (MRLs) and toxic equivalence (TEQ) factors for analytes. Only seventeen of the 445 analytes were detected, at low substance frequencies. However, half of the samples tested positive for one or more analyte, with total concentrations ranging from below the limit of detection (LOD) to as high as 156 µg/kg wet weight. Based on the risk assessment for individual pesticides and veterinary drugs, the hazard quotients (HQ) were all &lt;1, indicating no risk. However, for the sum of PCB126 and PCB167, two dioxin-like PCBs detected in our samples, the TEQ was nearly two orders of magnitude higher than the WHO limits in one catfish sample. Moreover, vulnerable groups with higher rates of consumption of specific fish types may face higher risks.