cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21922752.v3
title: Raw and mapped small RNA seq reads post oral delivery of dsRNAs to Psylliodes chrysocephala

description: Raw and trimmed small RNA seq reads obtained from <em>Psylliodes chrysocephala </em>treated with double-stranded (ds) RNAs (dsSec23, dsVatpG, or dsGPF). The".bam" files are the output of Bowtie2, representing the coverage of reads perfectly mapping onto the respective dsRNA treatment. "_reads" files are the raw reads obtained by sequencing on a DNBSEQ-G400 platform and the "_trimmed.fq.gz" files represent the reads cleaned from adaptor sequences as well as reads having quality scores below 30 and lengths below 18 nt and above 25. The bam files could be investigated by, for instance, R package "viRome" which can output the coverages per position on the respective dsRNA sequence (see viROME code example.R file for the code used in our paper ) or directly using a software such as Tablet for which the dsRNA index files are required and also provided here ("_index" files). Of note the dsRNA sequence in the index file dsVatpG_index is antisense of its target gene whereas for dsSec23_index it is in sense direction. <br> Further details regarding the methodology used during the sampling and sequencing is as follows: We performed small RNA sequencing to investigate siRNAs generated from the delivered dsRNAs. The whole bodies of three male and three female pre-aestivation beetles that had received the 200 ng/cm2 <em>Sec23</em>, <em>VatpG</em>, or GFP dsRNA treatment were sampled between 2 DPT and 3 DPT by placing them in liquid nitrogen (n = 3). Specifically, beetles that had consumed at least 50% of the dsRNA-containing leaf disk were selected. The total RNA from the beetles was extracted using the Quick-RNA Tissue/Insect Kit (Zymo Research EUROPE GMBH, Freiburg, Germany) and the RNA integrity was checked via RNA fragment analysis using Agilent 2100 Bioanalyzer (Agilent Technologies). The NEBNext® Multiplex Small RNA Library Prep Set for Illumina® (New England Biolabs GmbH, Frankfurt, Germany) kit was used for the library preparation according to the manufacturers’ instructions and all libraries were pooled based on their concentrations (see Tab. S5 for indexing information). The cDNAs within the range of 141 to 172 bp were size‑selected through gel electrophoresis, and a final concentration of 6.4 ng/µL was obtained. The small RNA sequencing was performed by BGI Tech Solutions Co. Ltd. (Hong Kong) on a DNBSEQ-G400 platform. The number and quality of the raw reads were checked using FastQC (v0.11.9) (Tab. S6). The adapters and low-quality reads were removed from the raw reads using Trim Galore! (v0.6.7), while reads within the range of 18-25 nt were retained. Cleaned and filtered reads were mapped onto the dsRNA sequences via Bowtie2 (v2.4.5) using the very sensitive end‑to-end alignment option and allowing zero mismatches. The small RNA reads from GFP dsRNA treated beetles were mapped onto <em>Sec23</em> and <em>VatpG</em> dsRNA sequences as background control.<br>
DOI: 10.48567/atag-tx02
title: Whitlock Automatic Weather Station, 2004 quality-controlled observational data.
DOI: 10.48567/rxyj-wn38
title: Willie Field Automatic Weather Station, 2004 quality-controlled observational data.
DOI: 10.48567/8hfx-sd12
title: Windless Bight Automatic Weather Station, 2004 quality-controlled observational data.
DOI: 10.6084/m9.figshare.21636710
title: Deciphering the causal relationship between blood pressure and regional white matter integrity: a two-sample Mendelian randomization study

description: Table S1. A list of 39 regional white matter (WM) integrity measured by fractional antitropy (FA). Table S2. Results of two-sample t-test for each confounder separately comparing the FA values of those with versus without missing confounder values (univariate version of little MCAR test). Table S3. Numbers of candidate instrumental variables (IVs) determined at each step and numbers of significant causal effects detected implemented with our statistical method by separately using different LD clumping settings. Table S4. Self-report neurological characteristics of UK Biobank's participants from two independent association sample sets for the two-sample Mendelian randomization (MR) analysis. Table S5. Numbers of candidate instrumental variables (IVs) determined at each step of our statistical method by separately using UK Biobank (UKB) GWAS and existing meta-analyzed GWAS. Table S6. Gene annotations for determined valid instrumental variables (IVs) carried into the two-sample Mendelian Randomization (MR) analysis by separately using UK Biobank (UKB) GWAS and existing meta-analyzed GWAS. Table S7. Results of association analysis and Mendelian Randomization analysis (implemented with generalized version of inverse-variance weighted (gen-IVW) approach) in the study samples. Table S8. Results of alternative Mendelian randomization (MR) approach and sensitivity analyses by using data from UK Biobank (UKB) cohort. Table S9. Results of adaptively weighted (AW) Fisher's method and mendelian randomization analysis implemented with a generalized version of inverse-variance weights method (gen-IVW) Table S10. Results and numbers of candidate instrumental variables (IVs) in each step of our two reverse two-sample Mendelian Randomization (MR) analyses using UK Biobank (UKB) cohort. Supplmentary File. Results of leave-one-out analyses STROBE-MR checklist. STrengthening the Reporting of OBservational studies in Epidemiology - Molecular Epidemiology (STROBE-ME) checklist
DOI: 10.6084/m9.figshare.21636710.v5
title: Deciphering the causal relationship between blood pressure and regional white matter integrity: a two-sample Mendelian randomization study

description: Table S1. A list of 39 regional white matter (WM) integrity measured by fractional antitropy (FA). Table S2. Self-report neurological characteristics of UK Biobank's participants from two independent association sample sets for the two-sample Mendelian randomization (MR) analysis. Table S3. Numbers of candidate instrumental variables (IVs) determined at each step of our statistical method by separately using UK Biobank (UKB) GWAS and existing meta-analyzed GWAS. Table S4. Gene annotations for determined valid instrumental variables (IVs) carried into the two-sample Mendelian Randomization (MR) analysis by separately using UK Biobank (UKB) GWAS and existing meta-analyzed GWAS. Table S5. Results of association analysis and Mendelian Randomization analysis (implemented with generalized version of inverse-variance weighted (gen-IVW) approach) in the study samples. Table S6. Results of alternative Mendelian randomization (MR) approach and sensitivity analyses by using data from UK Biobank (UKB) cohort. Table S7. Results of adaptively weighted (AW) Fisher's method and mendelian randomization analysis implemented with a generalized version of inverse-variance weights method (gen-IVW) Table S8. Results and numbers of candidate instrumental variables (IVs) in each step of our two reverse two-sample Mendelian Randomization (MR) analyses using UK Biobank (UKB) cohort. Supplmentary File. Results of leave-one-out analyses STROBE-MR checklist. STrengthening the Reporting of OBservational studies in Epidemiology - Molecular Epidemiology (STROBE-ME) checklist
DOI: 10.6084/m9.figshare.20481603.v1
title: File 1. OMZ Compendium Metadata

description: OMZ SAG biosamples with associated cruise and geolocation metadata. This table includes the accessions for each recovered amplicon and whole genome assembly.
DOI: 10.6084/m9.figshare.20539005
title: File 2. Number of SAGs generated with each DNA amplification method

description: <em> Number of SAGs generated with each DNA amplification method, and how many recoverable SSU sequences were recovered from each dataset. Note that there are some samples that had both amplicons and whole genome derived SSU sequences </em>
DOI: 10.6084/m9.figshare.20539005.v1
title: File 2: Number of SAGs generated with each DNA amplification method

description: <em> Number of SAGs generated with each DNA amplification method, and how many recoverable SSU sequences were recovered from each dataset. Note that there are some samples that had both amplicons and whole genome derived SSU sequences </em>
DOI: 10.6084/m9.figshare.20539056
title: File 3. Taxonomy and Quality Counts

description: <em>Number of SAGs that were assigned a taxonomy with SILVA v138.1 and GTDB-tk v1.5.0 and their summary CheckM % completeness and % contamination estimates. </em>
DOI: 10.6084/m9.figshare.20539056.v2
title: File 3. Taxonomy and Quality Counts

description: <em>Number of SAGs that were assigned a taxonomy with SILVA v138.1 and GTDB-tk v1.5.0 and their summary CheckM % completeness and % contamination estimates. </em>
DOI: 10.6084/m9.figshare.20539056.v1
title: File 3. Taxonomy and Quality Counts

description: <em>Number of SAGs that were assigned a taxonomy with SILVA v138.1 and GTDB-tk v1.5.0 and their summary CheckM % completeness and % contamination estimates. </em>
DOI: 10.5519/qd.zxs5sm9s
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (24 records)
DOI: 10.6084/m9.figshare.20537919
title: File 6. OMZ Compendium SSU rRNA Sequences

description: We have included all of the recovered SSU sequences. Here you will find 4 files, one containing all of the amplicon sequences, one file containing all of the whole genome sequnce derived SSUs from the JGI's workflow, one containing the SSU sequences whole genome sequence derived SSUs from the GORG dataset that were generated from Anvio, as well as a file containing all of the amplicon sequences that span the V4-V5 region from all of the anonymously sorted SAGs.
DOI: 10.6084/m9.figshare.20537919.v1
title: File 6. OMZ Compendium SSU rRNA Sequences

description: We have included all of the recovered SSU sequences. Here you will find 4 files, one containing all of the amplicon sequences, one file containing all of the whole genome sequnce derived SSUs from the JGI's workflow, one containing the SSU sequences whole genome sequence derived SSUs from the GORG dataset that were generated from Anvio, as well as a file containing all of the amplicon sequences that span the V4-V5 region from all of the anonymously sorted SAGs.
DOI: 10.5519/qd.8pvhmiyr
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (7 records)
DOI: 10.48567/yhdw-fa03
title: Butler Island Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.48567/9sb6-ag62
title: Byrd Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.5281/zenodo.7501160
title: Transcription of pp. 425-477 from the probate inventory of the house of Pieter de Graeff (1638-1707), Lord of Zuid-Polsbroek, Purmerland and Ilpendam, and VOC director in the Amsterdam chamber, at Herengracht 573

description: Transcription of pp. 425-477 from the probate inventory of the house of Pieter de Graeff (1638-1707), Lord of Zuid-Polsbroek, Purmerland and Ilpendam, and VOC director in the Amsterdam chamber, at Herengracht 573 (Amsterdam City Archives, Inventaris van het Archief van de Notarissen ter Standplaats Amsterdam (nr. 5075), inv. nr. 5001, pp. 425-477, notary Michiel Servaes (nr. 199), 8 March 1709). The transcription was made as part of the NWO-funded Virtual Interiors project (2018-2022; https://www.virtualinteriorsproject.nl/).
DOI: 10.5281/zenodo.7501159
title: Transcription of pp. 425-477 from the probate inventory of the house of Pieter de Graeff (1638-1707), Lord of Zuid-Polsbroek, Purmerland and Ilpendam, and VOC director in the Amsterdam chamber, at Herengracht 573

description: Transcription of pp. 425-477 from the probate inventory of the house of Pieter de Graeff (1638-1707), Lord of Zuid-Polsbroek, Purmerland and Ilpendam, and VOC director in the Amsterdam chamber, at Herengracht 573 (Amsterdam City Archives, Inventaris van het Archief van de Notarissen ter Standplaats Amsterdam (nr. 5075), inv. nr. 5001, pp. 425-477, notary Michiel Servaes (nr. 199), 8 March 1709). The transcription was made as part of the NWO-funded Virtual Interiors project (2018-2022; https://www.virtualinteriorsproject.nl/).
DOI: 10.48567/ds5q-c453
title: Cape Bird Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.48567/vb9s-d585
title: Cape Spencer Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.48567/fac1-ax29
title: Clean Air Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.48567/py6v-5107
title: D-10 Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.48567/1vgx-vc43
title: Dismal Island Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.48567/9sqk-t802
title: Dome C II Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.48567/x3nx-ea33
title: Dome Fuji Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.48567/1qk2-3x17
title: Emilia Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.34740/kaggle/dsv/5332920
title: Balochi Language to English Translation Dataset
DOI: 10.48567/k5t0-s193
title: Erin Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.48567/mmr7-rp75
title: Ferrell Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.48567/rnjn-kf27
title: Gill Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.48567/kfsq-rs37
title: Harry Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.48567/5717-w405
title: Henry Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.48567/wd4a-xh61
title: Herbie Alley Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.48567/t5w9-2705
title: Kirkwood Island Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.48567/r14b-ta12
title: Larsen Ice Shelf Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.48567/kc09-rb81
title: Laurie II Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.48567/br4b-js49
title: Lettau Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.48567/2r9x-3a95
title: Linda Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.48567/dn9w-5409
title: Manuela Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.48567/2dnh-m410
title: Marble Point Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.5281/zenodo.7584327
title: Current and future risk of unprecedented GB droughts

description: This dataset contains GR6J modelled daily river flow time series for 95 river catchments in Great Britain driven by precipitation and temperature (used to calculate potential evapotranspiration) data from the EC-Earth Single Model Initial Condition Large Ensemble (SMILE) climate model. The large ensemble is based on the EC-Earth Global Climate Model (GCM) v2.3 and is run for present day (equivalent to present day climate with observed global mean surface temperature for the period 2011-2015) and pre-industrial plus 2°C and 3°C global warming conditions. For each ensemble member, 25 new realizations are created through variations to the initial conditions through stochastic parameterizations and run for 5 years. In total, they make up 2000 years of data for each global warming level (i.e. 16 ensemble members x 25 realizations x 5 years = 2000 years). All ensemble members are pooled to form a continuous 2000-year time series of meteorological input and daily modelled river flows. Simulated river flows is obtained from using the best performing model run for each river catchment to drive GR6J hydrological models for each catchment. Each file presented is in the following format: Naming convention “LE_Flow_&lt;NRFA catchment ID&gt;.csv where &lt;NRFA catchment ID&gt; refers to the identification number for each catchment defined by the National River Flow Archive (NRFA). Each file contain 730400 rows of continuous 2000-years of daily simulated river flows for each catchment over five columns: Qsim – simulated river flow (in m3/s) of the top ranked parameter set driven by the large ensemble data Catch_id – NRFA catchment id Ens_member – ensemble member of the EC-Earth large ensemble Realisation – realization number of the EC-Earth large ensemble Three files are provided for each catchment for present-day (PD), 2°C and 3°C conditions.
DOI: 10.48567/w042-jr07
title: Marilyn Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.5281/zenodo.7584326
title: Current and future risk of unprecedented GB droughts

description: This dataset contains GR6J modelled daily river flow time series for 95 river catchments in Great Britain driven by precipitation and temperature (used to calculate potential evapotranspiration) data from the EC-Earth Single Model Initial Condition Large Ensemble (SMILE) climate model. The large ensemble is based on the EC-Earth Global Climate Model (GCM) v2.3 and is run for present day (equivalent to present day climate with observed global mean surface temperature for the period 2011-2015) and pre-industrial plus 2°C and 3°C global warming conditions. For each ensemble member, 25 new realizations are created through variations to the initial conditions through stochastic parameterizations and run for 5 years. In total, they make up 2000 years of data for each global warming level (i.e. 16 ensemble members x 25 realizations x 5 years = 2000 years). All ensemble members are pooled to form a continuous 2000-year time series of meteorological input and daily modelled river flows. Simulated river flows is obtained from using the best performing model run for each river catchment to drive GR6J hydrological models for each catchment. Each file presented is in the following format: Naming convention “LE_Flow_&lt;NRFA catchment ID&gt;.csv where &lt;NRFA catchment ID&gt; refers to the identification number for each catchment defined by the National River Flow Archive (NRFA). Each file contain 730400 rows of continuous 2000-years of daily simulated river flows for each catchment over five columns: Qsim – simulated river flow (in m3/s) of the top ranked parameter set driven by the large ensemble data Catch_id – NRFA catchment id Ens_member – ensemble member of the EC-Earth large ensemble Realisation – realization number of the EC-Earth large ensemble Three files are provided for each catchment for present-day (PD), 2°C and 3°C conditions.
DOI: 10.48567/68y1-q404
title: Minna Bluff Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.48567/rapg-p717
title: Mizuho Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.6084/m9.figshare.22141350.v2
title: Fast, Approximate Maximum Likelihood Estimation of Log-Gaussian Cox Processes

description: Log-Gaussian Cox processes (LGCPs) offer a framework for regression-style modeling of point patterns that can accommodate spatial latent effects. These latent effects can be used to account for missing predictors or other sources of clustering that could not be explained by a Poisson process. Fitting LGCP models can be challenging because the marginal likelihood does not have a closed form and it involves a high dimensional integral to account for the latent Gaussian field. We propose a novel methodology for fitting LGCP models that addresses these challenges using a combination of variational approximation and reduced rank interpolation. Additionally, we implement automatic differentiation to obtain exact gradient information, for computationally efficient optimization and to consider integral approximation using the Laplace method. We demonstrate the method’s performance through both simulations and a real data application, with promising results in terms of computational speed and accuracy compared to that of existing approaches. Supplementary material for this article is available online.
DOI: 10.6084/m9.figshare.22141350
title: Fast, Approximate Maximum Likelihood Estimation of Log-Gaussian Cox Processes

description: Log-Gaussian Cox processes (LGCPs) offer a framework for regression-style modeling of point patterns that can accommodate spatial latent effects. These latent effects can be used to account for missing predictors or other sources of clustering that could not be explained by a Poisson process. Fitting LGCP models can be challenging because the marginal likelihood does not have a closed form and it involves a high dimensional integral to account for the latent Gaussian field. We propose a novel methodology for fitting LGCP models that addresses these challenges using a combination of variational approximation and reduced rank interpolation. Additionally, we implement automatic differentiation to obtain exact gradient information, for computationally efficient optimization and to consider integral approximation using the Laplace method. We demonstrate the method’s performance through both simulations and a real data application, with promising results in terms of computational speed and accuracy compared to that of existing approaches. Supplementary material for this article is available online.
DOI: 10.6084/m9.figshare.22141350.v1
title: Fast, approximate maximum likelihood estimation of log-Gaussian Cox processes

description: Log-Gaussian Cox processes (LGCPs) offer a framework for regression-style modeling of point patterns that can accommodate spatial latent effects. These latent effects can be used to account for missing predictors or other sources of clustering that could not be explained by a Poisson process. Fitting LGCP models can be challenging because the marginal likelihood does not have a closed form and it involves a high dimensional integral to account for the latent Gaussian field. We propose a novel methodology for fitting LGCP models that addresses these challenges using a combination of variational approximation and reduced rank interpolation. Additionally, we implement automatic differentiation to obtain exact gradient information, for computationally efficient optimization and to consider integral approximation using the Laplace method. We demonstrate the method’s performance through both simulations and a real data application, with promising results in terms of computational speed and accuracy compared to that of existing approaches. Supplementary material for this article is available online.
DOI: 10.17632/38444g7rzn.1
title: The data

description: The data concerning the study.
DOI: 10.48567/h2ea-c088
title: Nico Automatic Weather Station, 2004 Reader format three-hour observational data.
DOI: 10.17632/w9scvcckh4.1
title: Data for Who goes first? A distributed simulator study of vehicle-pedestrian interaction

description: The following dataset provides all the data that was used for analysis and modelling in the paper entitled 'Who goes first? A distributed simulator study of vehicle-pedestrian interaction'. The file comes with a description so one can replicate the analyses using the information here and in the paper itself.
DOI: 10.17632/w9scvcckh4.2
title: Data for Who goes first? A distributed simulator study of vehicle-pedestrian interaction

description: The following dataset provides all the data that was used for analysis and modelling in the paper entitled 'Who goes first? A distributed simulator study of vehicle-pedestrian interaction'. The file comes with a description so one can replicate the analyses using the information here and in the paper itself. 

If you use this dataset, please cite: Kalantari, A. H., Yang, Y., Pedro, J. G. de, Lee, Y. M., Horrobin, A., Solernou, A., Holmes, C., Merat, N., & Markkula, G. (2023). Who goes first? A distributed simulator study of vehicle–pedestrian interaction. Accident Analysis & Prevention, 186, 107050. https://doi.org/10.1016/j.aap.2023.107050.