cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.6662999
title: Delay adaptation in VR

description: This dataset includes the experimental data for investigating the effects of visuomotor delay-adaptation on the subjective experiences of virtual reality. The study was approved by the Ethics Committee of the Department of Psychology at the University of Essex prior to data collection. <br>
DOI: 10.6084/m9.figshare.6662987
title: Calls emitted by bats

description: For a series of capture dates, we provide results on the number of inquiry (sheet 1) and response calls (sheet 2) for each one of the individuals studied. We also provide an additional sheet (3) with data on group id for each bat studied.<br>
DOI: 10.6084/m9.figshare.6662987.v1
title: Calls emitted by bats

description: For a series of capture dates, we provide results on the number of inquiry (sheet 1) and response calls (sheet 2) for each one of the individuals studied. We also provide an additional sheet (3) with data on group id for each bat studied.<br>
DOI: 10.6084/m9.figshare.6662984
title: Land Acquisition and Ecosystem Carbon in Coastal California

description: Report produced for the California State Coastal Conservancy; Project of the Climate Readiness Institute, UC Berkeley. See accompanying webinar recording: https://doi.org/10.6084/m9.figshare.5594437
DOI: 10.6084/m9.figshare.6662984.v1
title: Land Acquisition and Ecosystem Carbon in Coastal California

description: Report produced for the California State Coastal Conservancy; Project of the Climate Readiness Institute, UC Berkeley. See accompanying webinar recording: https://doi.org/10.6084/m9.figshare.5594437
DOI: 10.6084/m9.figshare.6662981
title: Number of calls per group

description: Data include the average number of inquiry (sheet 1) or response calls (sheet 2) for group members, and the group's identification number. <br>
DOI: 10.6084/m9.figshare.6662972
title: EEG Raw Data

description: The raw EEG data recorded during the working memory test.5000: Comparison Group;6000-8000: ADHD Group
DOI: 10.6084/m9.figshare.6662963
title: Model simulation data for the climate impacts of large-scale wind and solar farms in the Sahara and the world's deserts

description: <br>This is the model simulation data for paper by Yan Li, Eugenia Kalnay, Safa Motesharrei, Jorge Rivas, Fred Kucharski, Daniel Kirk-Davidoff, Eviatar Bach, and Ning Zeng "Climate model shows large-scale wind and solar farms in the Sahara increase rain and vegetation" in Science, 2018. DOI: 10.1126/science.aar5629<br><br><br>File description:<br>1. The "experiment_diff" folderThis folder includes data for the climate impacts of different wind and solar farms experiments. The impacts are the differences in climate averaged from 1951 to 2010. These experiments include:<br><b>ExpWind0</b>: impacts of wind farm in the Sahara<b>ExpSolar0</b>: impacts of solar farm in the Sahara<b>ExpWindSolar0</b>: impacts of wind and solar farms in the Sahara<br><b>ExpWindG</b>: impacts of wind farm in the global desert <b>ExpSolarG</b>: impacts of solar farm in the global desert <b>ExpWindSolarG</b>: impacts of wind and solar farms in the global desert <br><b>ExpWind1: </b>impacts of wind farm in the northwest quarter of the Sahara<br><b>ExpWind2: </b>impacts of wind farm in the northeast quarter of the Sahara<b><br></b><b>ExpWind3: </b>impacts of wind farm in the southeast quarter of the Sahara<b>ExpWind4: </b>impacts of wind farm in the southwest quarter of the Sahara<br><b>ExpWindmosaic0: </b>impacts of checkerboard A wind farm in the Sahara<b>ExpWindmosaic1: </b>impacts of checkerboard B wind farm in the Sahara<b><br></b><br>wmask.nc: wind or solar farm locations in the Sahara wmask_g.nc: wind or solar farm location in the global desert<br><br>Contact: Yan Li, yanli.geo@gmail.com
DOI: 10.6084/m9.figshare.6662963.v1
title: Model simulation data for the climate impacts of large-scale wind and solar farms in the Sahara and the world's deserts

description: <br>This is the model simulation data for paper by Yan Li, Eugenia Kalnay, Safa Motesharrei, Jorge Rivas, Fred Kucharski, Daniel Kirk-Davidoff, Eviatar Bach, and Ning Zeng "Climate model shows large-scale wind and solar farms in the Sahara increase rain and vegetation" in Science, 2018. DOI: 10.1126/science.aar5629<br><br><br>File description:<br>1. The "experiment_diff" folderThis folder includes data for the climate impacts of different wind and solar farms experiments. The impacts are the differences in climate averaged from 1951 to 2010. These experiments include:<br><b>ExpWind0</b>: impacts of wind farm in the Sahara<b>ExpSolar0</b>: impacts of solar farm in the Sahara<b>ExpWindSolar0</b>: impacts of wind and solar farms in the Sahara<br><b>ExpWindG</b>: impacts of wind farm in the global desert <b>ExpSolarG</b>: impacts of solar farm in the global desert <b>ExpWindSolarG</b>: impacts of wind and solar farms in the global desert <br><b>ExpWind1: </b>impacts of wind farm in the northwest quarter of the Sahara<br><b>ExpWind2: </b>impacts of wind farm in the northeast quarter of the Sahara<b><br></b><b>ExpWind3: </b>impacts of wind farm in the southeast quarter of the Sahara<b>ExpWind4: </b>impacts of wind farm in the southwest quarter of the Sahara<br><b>ExpWindmosaic0: </b>impacts of checkerboard A wind farm in the Sahara<b>ExpWindmosaic1: </b>impacts of checkerboard B wind farm in the Sahara<b><br></b><br>wmask.nc: wind or solar farm locations in the Sahara wmask_g.nc: wind or solar farm location in the global desert<br><br>Contact: Yan Li, yanli.geo@gmail.com
DOI: 10.6084/m9.figshare.6662939
title: Data for Figure 6,7 - Wright, Elsden & Takahashi 2018

description: Data for Figure 6,7 - Wright, Elsden & Takahashi 2018
DOI: 10.6084/m9.figshare.6662939.v1
title: Data for Figure 6,7 - Wright, Elsden & Takahashi 2018

description: Data for Figure 6,7 - Wright, Elsden & Takahashi 2018
DOI: 10.6084/m9.figshare.6662936
title: Data for Figure 5, Wright, Elsden & Takahashi 2018

description: Data for Figure 5, Wright, Elsden & Takahashi 2018
DOI: 10.6084/m9.figshare.6662936.v1
title: Data for Figure 2, Wright, Elsden & Takahashi 2018

description: Data for Figure 5, Wright, Elsden & Takahashi 2018
DOI: 10.6084/m9.figshare.6662936.v2
title: Data for Figure 5, Wright, Elsden & Takahashi 2018

description: Data for Figure 5, Wright, Elsden & Takahashi 2018
DOI: 10.6084/m9.figshare.6662933
title: Data for Figure 4, Wright, Elsden & Takahashi 2018

description: Data for Figure 4, Wright, Elsden & Takahashi 2018
DOI: 10.5517/ccdc.csd.cc25t359
title: CCDC 2020120: Experimental Crystal Structure Determination

description: Related Article: M. Koudad, C. El Hamouti, A. Elaatiaoui, S. Dadou, A. Oussaid, F. Abrigach, G. Pilet, N. Benchat, M. Allali|2020|J.Iran.Chem.Soc.|17|297|doi:10.1007/s13738-019-01766-4
DOI: 10.6084/m9.figshare.6662933.v1
title: Data for Figure 4, Wright, Elsden & Takahashi 2018

description: Data for Figure 4, Wright, Elsden & Takahashi 2018
DOI: 10.5517/ccdc.csd.cc25t517
title: CCDC 2020178: Experimental Crystal Structure Determination

description: Related Article: Kazunori Takanashi, Vladimir Ya. Lee, Masaaki Ichinohe, Akira Sekiguchi|2007|Chem.Lett.|36|1158|doi:10.1246/cl.2007.1158
DOI: 10.6084/m9.figshare.6662930
title: Data for Figure 3, Wright, Elsden & Takahashi 2018

description: Data for Figure 3, Wright, Elsden & Takahashi 2018
DOI: 10.6084/m9.figshare.6662930.v1
title: Data for Figure 3, Wright, Elsden & Takahashi 2018

description: Data for Figure 3, Wright, Elsden & Takahashi 2018
DOI: 10.6084/m9.figshare.6662927
title: Data for Figure 2, Wright, Elsden & Takahashi 2018

description: Data for Figure 2, Wright, Elsden & Takahashi 2018
DOI: 10.6084/m9.figshare.6662927.v1
title: Data for Figure 2, Wright, Elsden & Takahashi 2018

description: Data for Figure 2, Wright, Elsden & Takahashi 2018
DOI: 10.6084/m9.figshare.6662894
title: Complete data and PTMs identified in detected peptides present in the potato protein hydrolysate from Peptidomics of potato protein hydrolysates: implications of post-translational modifications in food peptide structure and behaviour

description: Table S1
DOI: 10.6084/m9.figshare.6662894.v1
title: Complete data and PTMs identified in detected peptides present in the potato protein hydrolysate from Peptidomics of potato protein hydrolysates: implications of post-translational modifications in food peptide structure and behaviour

description: Table S1
DOI: 10.6084/m9.figshare.6662462
title: Artificial Light Effects on Orb-Web Prey Capture Rates

description: Data describing basic web architecture and prey capture rates for orb-webs constructed in the lab and placed in the field to capture prey under different lighting conditions<br>
DOI: 10.6084/m9.figshare.6662462.v1
title: Artificial Light Effects on Orb-Web Prey Capture Rates

description: Data describing basic web architecture and prey capture rates for orb-webs constructed in the lab and placed in the field to capture prey under different lighting conditions<br>
DOI: 10.6084/m9.figshare.6662450
title: Metabarcoding of PalEnDNA as An Efficient Tool to Recover Ancient Bacterial Diversity

description: Past bacterial diversity of a paleosol was reconstructed using metabarcoding of paleo environmental DNA (PalEnDNA). The paleosol was subsampled from a sediment core which was excavated from a palaeo beach-ridge located 2.6 km away from present sea shore and identified that it was deposited under marine influence ∼6000 years ago, using geological proxies. The bacterial community contained 37 bacterial phyla and dominated by <i>Proteobacteria</i>, followed by <i>Bacteroidetes, Firmicutes, Actinobacteria, Verrucomicrobia</i>, and <i>Chloroflexi</i>. The bacterial community was a mix-up of marine and terrestrial population, and thereby diversity was higher than marine populations. The result shows metabarcoding of PalEnDNA can effectively reconstruct past bacterial community structure.
DOI: 10.6084/m9.figshare.6662450.v1
title: Metabarcoding of PalEnDNA as An Efficient Tool to Recover Ancient Bacterial Diversity

description: Past bacterial diversity of a paleosol was reconstructed using metabarcoding of paleo environmental DNA (PalEnDNA). The paleosol was subsampled from a sediment core which was excavated from a palaeo beach-ridge located 2.6 km away from present sea shore and identified that it was deposited under marine influence ∼6000 years ago, using geological proxies. The bacterial community contained 37 bacterial phyla and dominated by <i>Proteobacteria</i>, followed by <i>Bacteroidetes, Firmicutes, Actinobacteria, Verrucomicrobia</i>, and <i>Chloroflexi</i>. The bacterial community was a mix-up of marine and terrestrial population, and thereby diversity was higher than marine populations. The result shows metabarcoding of PalEnDNA can effectively reconstruct past bacterial community structure.
DOI: 10.6084/m9.figshare.6662447
title: Global profiling of hnRNP A2/B1-RNA binding on chromatin highlights LncRNA interactions

description: Long noncoding RNAs (lncRNAs) often carry out their functions through associations with adaptor proteins. We recently identified heterogeneous ribonucleoprotein (hnRNP) A2/B1 as an adaptor of the human HOTAIR lncRNA. hnRNP A2 and B1 are splice isoforms of the same gene. The spliced version of HOTAIR preferentially associates with the B1 isoform, which we hypothesize contributes to RNA-RNA matching between HOTAIR and transcripts of target genes in breast cancer. Here we used enhanced cross-linking immunoprecipitation (eCLIP) to map the direct interactions between A2/B1 and RNA in breast cancer cells. Despite differing by only twelve amino acids, the A2 and B1 splice isoforms associate preferentially with distinct populations of RNA <i>in vivo</i>. Through cellular fractionation experiments we characterize the pattern of RNA association in chromatin, nucleoplasm, and cytoplasm. We find that a majority of interactions occur on chromatin, even those that do not contribute to co-transcriptional splicing. A2/B1 binding site locations on multiple RNAs hint at a contribution to the regulation and function of lncRNAs. Surprisingly, the strongest A2/B1 binding site occurs in a retained intron of HOTAIR, which interrupts an RNA-RNA interaction hotspot. I<i>n vitro</i> eCLIP experiments highlight additional exonic B1 binding sites in HOTAIR which also surround the RNA-RNA interaction hotspot. Interestingly, a version of HOTAIR with the intron retained is still capable of making RNA-RNA interactions <i>in vitro</i> through the hotspot region. Our data further characterize the multiple functions of a repurposed splicing factor with isoform-biased interactions, and highlight that the majority of these functions occur on chromatin-associated RNA.
DOI: 10.6084/m9.figshare.6662447.v2
title: Global profiling of hnRNP A2/B1-RNA binding on chromatin highlights LncRNA interactions

description: Long noncoding RNAs (lncRNAs) often carry out their functions through associations with adaptor proteins. We recently identified heterogeneous ribonucleoprotein (hnRNP) A2/B1 as an adaptor of the human HOTAIR lncRNA. hnRNP A2 and B1 are splice isoforms of the same gene. The spliced version of HOTAIR preferentially associates with the B1 isoform, which we hypothesize contributes to RNA-RNA matching between HOTAIR and transcripts of target genes in breast cancer. Here we used enhanced cross-linking immunoprecipitation (eCLIP) to map the direct interactions between A2/B1 and RNA in breast cancer cells. Despite differing by only twelve amino acids, the A2 and B1 splice isoforms associate preferentially with distinct populations of RNA <i>in vivo</i>. Through cellular fractionation experiments we characterize the pattern of RNA association in chromatin, nucleoplasm, and cytoplasm. We find that a majority of interactions occur on chromatin, even those that do not contribute to co-transcriptional splicing. A2/B1 binding site locations on multiple RNAs hint at a contribution to the regulation and function of lncRNAs. Surprisingly, the strongest A2/B1 binding site occurs in a retained intron of HOTAIR, which interrupts an RNA-RNA interaction hotspot. I<i>n vitro</i> eCLIP experiments highlight additional exonic B1 binding sites in HOTAIR which also surround the RNA-RNA interaction hotspot. Interestingly, a version of HOTAIR with the intron retained is still capable of making RNA-RNA interactions <i>in vitro</i> through the hotspot region. Our data further characterize the multiple functions of a repurposed splicing factor with isoform-biased interactions, and highlight that the majority of these functions occur on chromatin-associated RNA.
DOI: 10.6084/m9.figshare.6662447.v1
title: Global Profiling of hnRNP A2/B1-RNA Binding on Chromatin Highlights LncRNA Interactions

description: Long noncoding RNAs (lncRNAs) often carry out their functions through associations with adaptor proteins. We recently identified heterogeneous ribonucleoprotein (hnRNP) A2/B1 as an adaptor of the human HOTAIR lncRNA. hnRNP A2 and B1 are splice isoforms of the same gene. The spliced version of HOTAIR preferentially associates with the B1 isoform, which we hypothesize contributes to RNA-RNA matching between HOTAIR and transcripts of target genes in breast cancer. Here we used enhanced cross-linking immunoprecipitation (eCLIP) to map the direct interactions between A2/B1 and RNA in breast cancer cells. Despite differing by only twelve amino acids, the A2 and B1 splice isoforms associate preferentially with distinct populations of RNA <i>in vivo</i>. Through cellular fractionation experiments we characterize the pattern of RNA association in chromatin, nucleoplasm, and cytoplasm. We find that a majority of interactions occur on chromatin, even those that do not contribute to co-transcriptional splicing. A2/B1 binding site locations on multiple RNAs hint at a contribution to the regulation and function of lncRNAs. Surprisingly, the strongest A2/B1 binding site occurs in a retained intron of HOTAIR, which interrupts an RNA-RNA interaction hotspot. I<i>n vitro</i> eCLIP experiments highlight additional exonic B1 binding sites in HOTAIR which also surround the RNA-RNA interaction hotspot. Interestingly, a version of HOTAIR with the intron retained is still capable of making RNA-RNA interactions <i>in vitro</i> through the hotspot region. Our data further characterize the multiple functions of a repurposed splicing factor with isoform-biased interactions, and highlight that the majority of these functions occur on chromatin-associated RNA.
DOI: 10.6084/m9.figshare.6662441
title: Carbon nanotubes, but not spherical nanoparticles, block autophagy by a shape-related targeting of lysosomes in murine macrophages

description: Nanoparticles (NPs) can be toxic, depending on their physico-chemical characteristics. Macroautophagy/autophagy could represent a potential underlying mechanism of this toxicity. We therefore set up a study aimed to characterize in depth the effects, on autophagy, of macrophage exposure to NPs, with a particular attention paid to the role of NP physico-chemical characteristics (specifically chemical composition, shape, size, length, crystal phase, and/or surface properties). We demonstrate that exposure to carbon nanotubes (CNT) but not to spherical NPs leads to the blockage of the autophagic flux. We further identified lysosomal dysfunction, in association with the downregulation of SNAPIN expression, as the underlying mechanism responsible for the CNT-induced autophagy blockade. These results identify for the first time the shape as a major determinant of the interaction of NPs with the autophagy pathway. Moreover, identifying the lysosomes and SNAPIN as primary targets of MWCNT toxicity opens new directions in the interpretation and understanding of nanomaterial toxicity.
DOI: 10.6084/m9.figshare.6662441.v1
title: Carbon nanotubes, but not spherical nanoparticles, block autophagy by a shape-related targeting of lysosomes in murine macrophages

description: Nanoparticles (NPs) can be toxic, depending on their physico-chemical characteristics. Macroautophagy/autophagy could represent a potential underlying mechanism of this toxicity. We therefore set up a study aimed to characterize in depth the effects, on autophagy, of macrophage exposure to NPs, with a particular attention paid to the role of NP physico-chemical characteristics (specifically chemical composition, shape, size, length, crystal phase, and/or surface properties). We demonstrate that exposure to carbon nanotubes (CNT) but not to spherical NPs leads to the blockage of the autophagic flux. We further identified lysosomal dysfunction, in association with the downregulation of SNAPIN expression, as the underlying mechanism responsible for the CNT-induced autophagy blockade. These results identify for the first time the shape as a major determinant of the interaction of NPs with the autophagy pathway. Moreover, identifying the lysosomes and SNAPIN as primary targets of MWCNT toxicity opens new directions in the interpretation and understanding of nanomaterial toxicity.
DOI: 10.6084/m9.figshare.6662432
title: Data on use of a computer-marked on-line assessment in engineering education over the course of five years

description: This fileset presents the data obtained though implementing an on-line computer-marked assessment in a second-year core civil engineering course in an Australian university. The data is gathered on-line in five years from the first year of implementing this quiz in 2011 to 2015. Students were allowed to attempt the quiz multiple times over a certain period of time, longer than a week. A large question bank was constructed with several variants for each question. Students faced randomly selected variants of questions in each attempt. At the end of assessment period, the best score of each student was recorded as their mark for the quiz. An anonymous on-line survey was conducted every year after the end of the assessment period in which students were asked to answer a number of questions and provide their opinion about the quiz.<br> The data presented here includes students’ marks in each attempt, the time they spent on each attempt, and anonymous survey results.<br>
DOI: 10.6084/m9.figshare.6662420
title: A new journal ranking method: the reputation analysis of citation behavior model

description: The raw data are divided into two parts: one is the ten year citation data of the top journal (D1.rar), and the other is the citation data of the ten selected journals (D2.rar). These raw data are downloaded from Web of Science. The remaining data include the matrix after the word frequency extraction of top journal (D3.rar), the preprocessing results of top journal data (D4.xlsx), the matrix after the word frequency extraction of ten journals (D5. rar), the JR and IF of ten journals (D6.xlsx), the ACR values of ten journals (D7.xlsx), the CDI and ACR of ten journals (D8.xlsx), RCR values under different p values (D9.xlsx), the inter-citation data of ten journals (D10.xlsx), and the ranking results of various ranking methods (D11.xlsx).
DOI: 10.6084/m9.figshare.6662420.v1
title: Raw data

description: The raw data is divided into two parts: one is the ten year citation data of the top journal, and the other is the citation data of the ten selected journals. These raw data are downloaded from Web of Scinece.
DOI: 10.6084/m9.figshare.6662420.v2
title: A new journal ranking method: the reputation analysis of citation behavior model

description: The raw data is divided into two parts: one is the ten year citation data of the top journal, and the other is the citation data of the ten selected journals. These raw data are downloaded from Web of Scinece.
DOI: 10.6084/m9.figshare.6662420.v3
title: A new journal ranking method: the reputation analysis of citation behavior model.

description: The raw data is divided into two parts: one is the ten year citation data of the top journal (D1.rar), and the other is the citation data of the ten selected journals (D2.rar). These raw data are downloaded from Web of Scinece.The remaining data include the matrix after the word frequency extraction of top journal (D3.rar), the preprocessing results of top journal data (D4.xlsx), the matrix after the word frequency extraction of ten journals (D5. rar), the JR and IF of ten journals (D6.xlsx), the ACR values of ten journals (D7.xlsx), the CDI and ACR of ten journals (D8.xlsx), RCR values under different p values (D9.xlsx), the inter-citation data of ten journals (D10.xlsx), the ranking results of various ranking methods (D11.xlsx).