cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3691687
title: Supplement of manuscript 521330

description: This folder contains analysis data from the study submitted as manuscript (Manuscript ID 521330): TITLE: Comparative metabarcoding and metatranscriptomic analysis of microeukaryotes within coastal surface waters of West Greenland and Northwest Iceland AUTHORS: Stephanie Elferink, Stephanie.westphal@awi.de, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research<sup> </sup> Sylke Wohlrab, Sylke.wohlrab@awi.de, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, and Helmholtz Institute for Functional Marine Biodiversity<sup> </sup> Stefan Neuhaus, Stephan.neuhaus@awi.de, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research<sup> </sup> Allan Cembella, Allan.Cembella@awi.de, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research<sup> </sup> Lars Harms, Lars.Harms@awi.de, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, and Helmholtz Institute for Functional Marine Biodiversity<sup> </sup> Uwe John, Uwe.John@awi.de, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, and Helmholtz Institute for Functional Marine Biodiversity JOURNAL: Frontiers in Marine Science, section Marine Molecular Biology and Ecology MS-ID: 521330 HOWTO: Sequences identified as Alveloates or Stramenopiles (description in manuscript) had been were classified more accurately by PhyloAssigner version 6.166 (https://github.com/jungbluth/phyloassigner, Vergin et al., 2013, DOI:10.1038/ismej.2013.32) with a phylogenetic placement onto reference trees based on 18S/28S concatenated alignments, according to Elferink et al. 2017 (DOI: 10.1016/j.dsr.2016.11.002). CONTENT: reference databases: - Alveolata_SSU-LSU-concat_310715_636.phyloassignerdb - Stramenopiles_SSU_LSU_concat_030815_1777.phyloassignerdb query sequence files: - Alveolata_seqtab_SIGN_dada2.fasta - Alveolata_seqtab_SIGN_dada2.fasta created out put folder including the taxonomic annotation: - Alveolata_seqtab_SIGN_dada2.place.out - Stramenopiles_seqtab_SIGN_dada2.place.out text file containing the used commands: - commands
DOI: 10.5281/zenodo.3691688
title: Supplement of manuscript 521330

description: This folder contains analysis data from the study submitted as manuscript (Manuscript ID 521330): TITLE: Comparative metabarcoding and metatranscriptomic analysis of microeukaryotes within coastal surface waters of West Greenland and Northwest Iceland AUTHORS: Stephanie Elferink, Stephanie.westphal@awi.de, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research<sup> </sup> Sylke Wohlrab, Sylke.wohlrab@awi.de, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, and Helmholtz Institute for Functional Marine Biodiversity<sup> </sup> Stefan Neuhaus, Stephan.neuhaus@awi.de, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research<sup> </sup> Allan Cembella, Allan.Cembella@awi.de, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research<sup> </sup> Lars Harms, Lars.Harms@awi.de, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, and Helmholtz Institute for Functional Marine Biodiversity<sup> </sup> Uwe John, Uwe.John@awi.de, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, and Helmholtz Institute for Functional Marine Biodiversity JOURNAL: Frontiers in Marine Science, section Marine Molecular Biology and Ecology MS-ID: 521330 HOWTO: Sequences identified as Alveloates or Stramenopiles (description in manuscript) had been were classified more accurately by PhyloAssigner version 6.166 (https://github.com/jungbluth/phyloassigner, Vergin et al., 2013, DOI:10.1038/ismej.2013.32) with a phylogenetic placement onto reference trees based on 18S/28S concatenated alignments, according to Elferink et al. 2017 (DOI: 10.1016/j.dsr.2016.11.002). CONTENT: reference databases: - Alveolata_SSU-LSU-concat_310715_636.phyloassignerdb - Stramenopiles_SSU_LSU_concat_030815_1777.phyloassignerdb query sequence files: - Alveolata_seqtab_SIGN_dada2.fasta - Alveolata_seqtab_SIGN_dada2.fasta created out put folder including the taxonomic annotation: - Alveolata_seqtab_SIGN_dada2.place.out - Stramenopiles_seqtab_SIGN_dada2.place.out text file containing the used commands: - commands
DOI: 10.18716/nmrshiftdb2/75290
title: NMR data for InChI=1S/C11H24FO7PS/c1-9(2)18-20(13,19-10(3)4)8-16-11(6-12)7-17-21(5,14)15/h9-11H,6-8H2,1-5H3
DOI: 10.18716/nmrshiftdb2/75296
title: NMR data for diethyl 2-(methoxymethylidene)propanedioate
DOI: 10.18716/nmrshiftdb2/75294
title: NMR data for InChI=1S/C52H79N5O12/c1-31-16-12-11-13-17-32(2)43(65-8)28-39-21-19-37(7)52(64,69-39)49(61)50(62)56-23-15-14-18-41(56)51(63)68-44(34(4)26-38-20-22-40(45(27-38)66-9)57-30-53-54-55-57)29-42(58)33(3)25-36(6)47(60)48(67-10)46(59)35(5)24-31/h11-13,16-17,25,30-31,33-35,37-41,43-45,47-48,60,64H,14-15,18-24,26-29H2,1-10H3/b13-11+,16-12+,32-17+,36-25+
DOI: 10.6085/aa/scpxxx_015mtbd003r00_20100825.40.1
title: (:unkn)
DOI: 10.25387/g3.11864358
title: Supplemental Material for Bricker et al., 2020

description: Supplementary figures and tables for Bricker et al.
DOI: 10.25387/g3.11864358.v1
title: Supplemental Material for Bricker et al., 2020

description: Supplementary figures and tables for Bricker et al.
DOI: 10.6085/aa/scpxxx_015mtbd003r00_20101123.40.1
title: (:unkn)
DOI: 10.21227/yy89-xj45
title: Open Repository for the Evaluation of Ransomware Detection Tools

description: This repository contains the results of running more than 70 samples of ransomware, from different families, dating since 2015. It contains the network traffic (DNS and TCP) and the Input/Output (I/O) operations generated by the malware while encrypting a network shared directory. These data are contained in three files for each ransomware sample: one with the information from the DNS requests, other with the TCP connections another one containing the I/O operations. This information can be useful for testing new and old ransomware detection tools and compare their results.
DOI: 10.6085/aa/scpxxx_015mtbd003r00_20110928.40.1
title: (:unkn)
DOI: 10.6085/aa/scpxxx_015mtbd003r00_20111207.40.1
title: (:unkn)
DOI: 10.25386/genetics.11889099
title: Supplemental Material for Teterina, Willis, and Phillips, 2020

description: Supplementary figures and tables reporting features of whole-genome sequence assembly of the nematode Caenorhabditis remanei<br><br>
DOI: 10.25386/genetics.11889099.v1
title: Supplemental Material for Teterina, Willis, and Phillips, 2020

description: Supplementary figures and tables reporting features of whole-genome sequence assembly of the nematode Caenorhabditis remanei<br><br>
DOI: 10.5281/zenodo.3691872
title: MiRoR7-P1- Disagreements in risk of bias assessment for randomised controlled trials included in more than one Cochrane systematic reviews: a research on research study using cross-sectional design

description: dataset referring to <strong>Disagreements in risk of bias assessment for randomised controlled trials included in more than one Cochrane systematic reviews: a research on research study using cross-sectional design</strong> Lorenzo Bertizzolo<sup>1</sup>, Patrick M Bossuyt<sup>2</sup>, Ignacio Atal<sup>1, 5</sup>, Philippe Ravaud<sup>1, 3-6</sup>, Agnès Dechartres<sup>7</sup> <sup>1</sup> INSERM, U1153 Epidemiology and Biostatistics Sorbonne Paris Cité Research Center (CRESS), Methods of therapeutic evaluation of chronic diseases Team (METHODS), Paris, F-75004 France; Paris Descartes University, Sorbonne Paris Cité, France. <sup>2</sup> Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, Netherlands. <sup>3</sup> Centre d’Épidémiologie Clinique, Hôpital Hôtel Dieu, AP-HP (Assistance Publique des Hôpitaux de Paris), Paris, France. <sup>4</sup> Faculté de Médecine, Université Paris Descartes, Sorbonne Paris Cité, Paris, France. <sup>5</sup> Cochrane France, Paris, France <sup>6</sup> Columbia University, Mailman School of Public Health, Department of Epidemiology, New York, USA <sup>7</sup> Sorbonne Université, INSERM, Institut Pierre Louis de Santé Publique, Département Biostatistique, Santé Publique et Information Médicale, AP-HP, Hôpitaux Universitaires Pitié Salpêtrière – Charles Foix, Paris, France
DOI: 10.6084/m9.figshare.11912355.v5
title: Simulated zero-crossing wave period (3-hourly, 2015.01-2100.12) from FIO-ESM v2.0 ssp245 experiment r1i1p1f1

description: Simulated zero-crossing wave period (3-hourly, 2015.01-2100.12) from FIO-ESM v2.0 ssp245 experiment (one of future scenarios) r1i1p1f1. The grid, named gx1v6, is the displaced pole (the North Pole displaced to the Greenland) grid with 1.1 degree in longitude and 0.27-0.54 degree in latitude.
DOI: 10.5281/zenodo.3689240
title: Raw data of RNA-seq and IGV -2

description: Raw data of RNA-seq and IGV of infected samples and mock infected samples
DOI: 10.25386/genetics.11899908
title: Supplemental Material for Ohkuni et al., 2020

description: <b>Table S1. </b><i>S. cerevisiae</i> strains used in this study.<b>Table S2.</b> Plasmids used in this study.<b>Table S3.</b> Primers used in this study.<b>Figure S1. </b>The stability of HA-Cse4 K215/216R/A is not increased when compared to HA-Cse4 in a <i>psh1</i>∆ strain. <b>Figure S2. </b>Levels of chromatin associated Cse4 K215/216R/A are reduced when compared to wild-type Cse4.<b>Figure S3. </b>Protein levels of Cse4 and Cse4 K215/216R under the copper-inducible promoter.<b>Figure S4. </b><i>GAL-cse4 K65R</i> exhibits SDL in <i>slx5</i>∆ and <i>hir2</i>∆ strains and those are suppressed by <i>cse4 K215/216R</i>.
DOI: 10.25386/genetics.11899908.v1
title: Supplemental Material for Ohkuni et al., 2020

description: <b>Table S1. </b><i>S. cerevisiae</i> strains used in this study.<b>Table S2.</b> Plasmids used in this study.<b>Table S3.</b> Primers used in this study.<b>Figure S1. </b>The stability of HA-Cse4 K215/216R/A is not increased when compared to HA-Cse4 in a <i>psh1</i>∆ strain. <b>Figure S2. </b>Levels of chromatin associated Cse4 K215/216R/A are reduced when compared to wild-type Cse4.<b>Figure S3. </b>Protein levels of Cse4 and Cse4 K215/216R under the copper-inducible promoter.<b>Figure S4. </b><i>GAL-cse4 K65R</i> exhibits SDL in <i>slx5</i>∆ and <i>hir2</i>∆ strains and those are suppressed by <i>cse4 K215/216R</i>.
DOI: 10.3886/e117971
title: Union Bank of Australia, Data and Codebook

description: Personnel data from the Union Bank of Australia. Covers entire careers of all employees at the Bank in 1887 and all entrants through 1900.
DOI: 10.3886/e117971v1
title: Union Bank of Australia, Data and Codebook

description: Personnel data from the Union Bank of Australia. Covers entire careers of all employees at the Bank in 1887 and all entrants through 1900.
DOI: 10.5523/bris.38lc6czmwnp3b281ba3r7l0bbz
title: The Surface Tension of Surfactant-Containing, Finite Volume Droplets

description: This data set provides the data underlying the figures in our publication "The Surface Tension of Surfactant-Containing, Finite Volume Droplets", Proceedings of the National Academy of Sciences of the United States of America, 2020.
DOI: 10.5281/zenodo.3672826
title: Raw data of RNA-seq and IGV -1

description: The raw data of RNA-seq and IGV data of infected samples and bacterial samples.
DOI: 10.6085/aa/scpxxx_015mtbd009r00_20100825.40.1
title: (:unkn)
DOI: 10.6085/aa/scpxxx_015mtbd009r00_20101123.40.1
title: (:unkn)
DOI: 10.6085/aa/scpxxx_015mtbd009r00_20110228.40.1
title: (:unkn)
DOI: 10.6085/aa/scpxxx_015mtbd009r00_20110523.40.1
title: (:unkn)
DOI: 10.21227/3g66-pz79
title: CUPSNBOTTLES

description: CUPSNBOTTLES is an object data set, recorded by a mobile service robot. There are 10 object classes, each with a varying number of samples. Additionally, there is a clutter class, containing samples where the object detector failed. The data set consists of the actual object images, an HDF file with extracted visual features (via a VGG19 deep convolutional net pretrained for the imagenet competition) and a CSV file containing other information about recorded objects like their global spatial position, spatial position of the robot, a timestamp and the object position within the image Settings of recording the data set: We used the robot to approach five way points to record objects that were located on different kind of furniture. On each way point, the robot did a -30 degree followed by a 60 degree turn for acquiring more object samples from different viewing angles. Objects are extracted by YOLO object detector. For simplifying our experiments, we include only object samples which have the YOLO-classes cup or bottle, however we consider these as meta categories which can be divided into different kind of e.g. bottles. Also, we exclude object samples with a YOLO-confidence with less than 20% and a distance greater than 2 meters. There are then still a share of 5\% remaining clutter images in the data set which do not contain a valid centered bottle or mug, but still exceed our YOLO confidence threshold. While the robot is moving, objects are extracted from the camera stream all the time. Since the robot moves quite fast, there are blurry objects within the data set. However, while the robot is turning at a waypoint, we make sure that all objects are detected by YOLO and there are a maximum of 3-4 frames in a row where an object is missed. The total number of frames is 515 with 2179 resulting object samples in the dataset and 121 clutter samples.
DOI: 10.6085/aa/scpxxx_015mtbd009r00_20111207.40.1
title: (:unkn)
DOI: 10.5255/ukda-sn-854204
title: Perceptions of fishing, fire, and changes in livelihoods in Indonesia 2019
DOI: 10.3886/e112081v1
title: Replication Archive for: The Welfare Effects of Social Media

description: The rise of social media has provoked both optimism about potential societal benefits and concern about harms such as addiction, depression, and political polarization. In a randomized experiment, we find that deactivating Facebook for the four weeks before the 2018 US midterm election (i) reduced online activity, while increasing offline activities such as watching TV alone and socializing with family and friends; (ii) reduced both factual news knowledge and political polarization; (iii) increased subjective well-being; and (iv) caused a large persistent reduction in post-experiment Facebook use. Deactivation reduced post-experiment valuations of Facebook, suggesting that traditional metrics may overstate consumer surplus.<br>
DOI: 10.3886/e112081
title: Replication Archive for: The Welfare Effects of Social Media

description: The rise of social media has provoked both optimism about potential societal benefits and concern about harms such as addiction, depression, and political polarization. In a randomized experiment, we find that deactivating Facebook for the four weeks before the 2018 US midterm election (i) reduced online activity, while increasing offline activities such as watching TV alone and socializing with family and friends; (ii) reduced both factual news knowledge and political polarization; (iii) increased subjective well-being; and (iv) caused a large persistent reduction in post-experiment Facebook use. Deactivation reduced post-experiment valuations of Facebook, suggesting that traditional metrics may overstate consumer surplus.<br>
DOI: 10.5525/gla.researchdata.982
title: Functional colour genes and signals of selection in colour polymorphic salamanders
DOI: 10.21227/efxc-9r53
title: CUPSNBOTTLES

description: CUPSNBOTTLES is an object data set, recorded by a mobile service robot. There are 10 object classes, each with a varying number of samples. Additionally, there is a clutter class, containing samples where the object detector failed. The data set consists of the actual object images, an HDF file with extracted visual features (via a VGG19 deep convolutional net pretrained for the imagenet competition) and a CSV file containing other information about recorded objects like their global spatial position, spatial position of the robot, a timestamp and the object position within the image Settings of recording the data set: We used the robot to approach five way points to record objects that were located on different kind of furniture. On each way point, the robot did a -30 degree followed by a 60 degree turn for acquiring more object samples from different viewing angles. Objects are extracted by YOLO object detector. For simplifying our experiments, we include only object samples which have the YOLO-classes cup or bottle, however we consider these as meta categories which can be divided into different kind of e.g. bottles. Also, we exclude object samples with a YOLO-confidence with less than 20% and a distance greater than 2 meters. There are then still a share of 5\% remaining clutter images in the data set which do not contain a valid centered bottle or mug, but still exceed our YOLO confidence threshold. While the robot is moving, objects are extracted from the camera stream all the time. Since the robot moves quite fast, there are blurry objects within the data set. However, while the robot is turning at a waypoint, we make sure that all objects are detected by YOLO and there are a maximum of 3-4 frames in a row where an object is missed. The total number of frames is 515 with 2179 resulting object samples in the dataset and 121 clutter samples.
DOI: 10.3886/e109703v1
title: Replication data and code for: Rural Roads and Local Economic Development

description: These files contain the data and code for the journal article "Rural Roads and Local Economic Development," American Economic Review. Please start with the readme, which contains a detailed description of all files in this repository and instructions for the replication of results in the article. <br><br>Abstract<br>Nearly one billion people worldwide live in rural areas without access to national paved road networks. We estimate the impacts of India’s $40 billion national rural road construction program using a fuzzy regression discontinuity design and comprehensive household and firm census microdata. Four years after road construction, the main effect of new feeder roads is to facilitate the movement of workers out of agriculture. However, there are no major changes in agricultural outcomes, income or assets. Employment in village firms expands only slightly. Even with better market connections, remote areas may continue to lack economic opportunities.<br><br>
description: Presence of Common Scales: Indian National Classification of Occupations (2004)
description: Villages in India without paved roads in 2000. Smallest Geographic Unit: Village
description: The main analysis sample is comprised of villages that did not have a paved approach road at the start of the road construction program and whose baseline populations were close to the cutoffs used for prioritization.
DOI: 10.21227/qv09-j444
title: CUPSNBOTTLES

description: CUPSNBOTTLES is an object data set, recorded by a mobile service robot. There are 10 object classes, each with a varying number of samples. Additionally, there is a clutter class, containing samples where the object detector failed. The data set consists of the actual object images, an HDF file with extracted visual features (via a VGG19 deep convolutional net pretrained for the imagenet competition) and a CSV file containing other information about recorded objects like their global spatial position, spatial position of the robot, a timestamp and the object position within the image Settings of recording the data set: We used the robot to approach five way points to record objects that were located on different kind of furniture. On each way point, the robot did a -30 degree followed by a 60 degree turn for acquiring more object samples from different viewing angles. Objects are extracted by YOLO object detector. For simplifying our experiments, we include only object samples which have the YOLO-classes cup or bottle, however we consider these as meta categories which can be divided into different kind of e.g. bottles. Also, we exclude object samples with a YOLO-confidence with less than 20% and a distance greater than 2 meters. There are then still a share of 5\% remaining clutter images in the data set which do not contain a valid centered bottle or mug, but still exceed our YOLO confidence threshold. While the robot is moving, objects are extracted from the camera stream all the time. Since the robot moves quite fast, there are blurry objects within the data set. However, while the robot is turning at a waypoint, we make sure that all objects are detected by YOLO and there are a maximum of 3-4 frames in a row where an object is missed. The total number of frames is 515 with 2179 resulting object samples in the dataset and 121 clutter samples.
DOI: 10.21227/ywwz-cb26
title: CUPSNBOTTLES

description: CUPSNBOTTLES is an object data set, recorded by a mobile service robot. There are 10 object classes, each with a varying number of samples. Additionally, there is a clutter class, containing samples where the object detector failed. The data set consists of the actual object images, an HDF file with extracted visual features (via a VGG19 deep convolutional net pretrained for the imagenet competition) and a CSV file containing other information about recorded objects like their global spatial position, spatial position of the robot, a timestamp and the object position within the image Settings of recording the data set: We used the robot to approach five way points to record objects that were located on different kind of furniture. On each way point, the robot did a -30 degree followed by a 60 degree turn for acquiring more object samples from different viewing angles. Objects are extracted by YOLO object detector. For simplifying our experiments, we include only object samples which have the YOLO-classes cup or bottle, however we consider these as meta categories which can be divided into different kind of e.g. bottles. Also, we exclude object samples with a YOLO-confidence with less than 20% and a distance greater than 2 meters. There are then still a share of 5\% remaining clutter images in the data set which do not contain a valid centered bottle or mug, but still exceed our YOLO confidence threshold. While the robot is moving, objects are extracted from the camera stream all the time. Since the robot moves quite fast, there are blurry objects within the data set. However, while the robot is turning at a waypoint, we make sure that all objects are detected by YOLO and there are a maximum of 3-4 frames in a row where an object is missed. The total number of frames is 515 with 2179 resulting object samples in the dataset and 121 clutter samples.