cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7284/137829
title: Meteorological Station data collected during research cruise AT42-02 using a Vaisala WXT520 instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7910/dvn/v1rjpf/q3zl7b
title: mea20160213_03.txt.gz

description: :unav
DOI: 10.7284/137832
title: Radiometer data collected during research cruise AT42-02 using a Eppley RAD instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7910/dvn/v1rjpf/qmmfdp
title: mea20150314_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/15z1sw
title: mea20150808_03.txt.gz

description: :unav
DOI: 10.7284/137824
title: Speedlog data collected during research cruise AT42-02 using a Furuno DS-50 instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7910/dvn/v1rjpf/edydbt
title: mea20151003_03.txt.gz

description: :unav
DOI: 10.7284/137830
title: Water Temperature data collected during research cruise AT42-02 using a Sea-Bird SBE-48 instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7910/dvn/v1rjpf/wpuxpq
title: mea20160727_03.txt.gz

description: :unav
DOI: 10.7284/137831
title: TSG (Thermosalinograph) data collected during research cruise AT42-02 using a Sea-Bird SBE-45 instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7910/dvn/v1rjpf/0ccops
title: mea20160627_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/cezmqt
title: mea20161203_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/peic9z
title: mea20150815_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/brfpbz
title: mea20160804_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/jtyvl9
title: mea20160221_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/fe6g8y
title: mea20150705_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/dgwybk
title: mea20151227_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/sfeeyv
title: mea20161009_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/xsv21r
title: mea20160609_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3858504
title: Machine learning-, rule- and pharmacophore-based classification on the inhibition of P-glycoprotein and NorA

description: The efflux pumps P-glycoprotein (P-gp) in humans and NorA in <i>Staphylococcus aureus</i> are of great interest for medicinal chemists because of their important roles in multidrug resistance (MDR). The high polyspecificity as well as the unavailability of high-resolution X-ray crystal structures of these transmembrane proteins lead us to combining ligand-based approaches, which in the case of this study were machine learning, perceptual mapping and pharmacophore modelling. For P-gp inhibitory activity, individual models were developed using different machine learning algorithms and subsequently combined into an ensemble model which showed a good discrimination between inhibitors and noninhibitors (acc<sub>train-diverse</sub> = 84%; acc<sub>internal-test</sub> = 92% and acc<sub>external-test</sub> = 100%). For ligand promiscuity between P-gp and NorA, perceptual maps and pharmacophore models were generated for the detection of rules and features. Based on these <i>in silico</i> tools, hit compounds for reversing MDR were discovered from the in-house and DrugBank databases through virtual screening in an attempt to restore drug sensitivity in cancer cells and bacteria.
DOI: 10.7910/dvn/v1rjpf/peclju
title: mea20151222_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3858504.v1
title: Machine learning-, rule- and pharmacophore-based classification on the inhibition of P-glycoprotein and NorA

description: The efflux pumps P-glycoprotein (P-gp) in humans and NorA in <i>Staphylococcus aureus</i> are of great interest for medicinal chemists because of their important roles in multidrug resistance (MDR). The high polyspecificity as well as the unavailability of high-resolution X-ray crystal structures of these transmembrane proteins lead us to combining ligand-based approaches, which in the case of this study were machine learning, perceptual mapping and pharmacophore modelling. For P-gp inhibitory activity, individual models were developed using different machine learning algorithms and subsequently combined into an ensemble model which showed a good discrimination between inhibitors and noninhibitors (acc<sub>train-diverse</sub> = 84%; acc<sub>internal-test</sub> = 92% and acc<sub>external-test</sub> = 100%). For ligand promiscuity between P-gp and NorA, perceptual maps and pharmacophore models were generated for the detection of rules and features. Based on these <i>in silico</i> tools, hit compounds for reversing MDR were discovered from the in-house and DrugBank databases through virtual screening in an attempt to restore drug sensitivity in cancer cells and bacteria.
DOI: 10.7910/dvn/v1rjpf/waivgo
title: mea20150526_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/k8gcb1
title: mea20160706_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3858489
title: Carbonate alteration of ophiolitic rocks in the Arabian–Nubian Shield of Egypt: sources and compositions of the carbonating fluid and implications for the formation of Au deposits

description: Ultramafic portions of ophiolitic fragments in the Arabian–Nubian Shield (ANS) show pervasive carbonate alteration forming various degrees of carbonated serpentinites and listvenitic rocks. Notwithstanding the extent of the alteration, little is known about the processes that caused it, the source of the CO<sub>2</sub> or the conditions of alteration. This study investigates the mineralogy, stable (O, C) and radiogenic (Sr) isotope composition, and geochemistry of suites of variably carbonate altered ultramafics from the Meatiq area of the Central Eastern Desert (CED) of Egypt. The samples investigated include least-altered lizardite (Lz) serpentinites, antigorite (Atg) serpentinites and listvenitic rocks with associated carbonate and quartz veins. The C, O and Sr isotopes of the vein samples cluster between −8.1‰ and −6.8‰ for δ<sup>13</sup>C, +6.4‰ and +10.5‰ for δ<sup>18</sup>O, and <sup>87</sup>Sr/<sup>86</sup>Sr of 0.7028–0.70344, and plot within the depleted mantle compositional field. The serpentinites isotopic compositions plot on a mixing trend between the depleted-mantle and sedimentary carbonate fields. The carbonate veins contain abundant carbonic (CO<sub>2</sub>±CH<sub>4</sub>±N<sub>2</sub>) and aqueous-carbonic (H<sub>2</sub>O-NaCl-CO<sub>2</sub>±CH<sub>4</sub>±N<sub>2</sub>) low salinity fluid, with trapping conditions of 270–300°C and 0.7–1.1 kbar. The serpentinites are enriched in Au, As, S and other fluid-mobile elements relative to primitive and depleted mantle. The extensively carbonated Atg-serpentinites contain significantly lower concentrations of these elements than the Lz-serpentinites suggesting that they were depleted during carbonate alteration. Fluid inclusion and stable isotope compositions of Au deposits in the CED are similar to those from the carbonate veins investigated in the study and we suggest that carbonation of ANS ophiolitic rocks due to influx of mantle-derived CO<sub>2</sub>-bearing fluids caused break down of Au-bearing minerals such as pentlandite, releasing Au and S to the hydrothermal fluids that later formed the Au-deposits. This is the first time that gold has been observed to be remobilized from rocks during the lizardite–antigorite transition.
DOI: 10.7910/dvn/v1rjpf/0auyhf
title: mea20160209_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3858489.v1
title: Carbonate alteration of ophiolitic rocks in the Arabian–Nubian Shield of Egypt: sources and compositions of the carbonating fluid and implications for the formation of Au deposits

description: Ultramafic portions of ophiolitic fragments in the Arabian–Nubian Shield (ANS) show pervasive carbonate alteration forming various degrees of carbonated serpentinites and listvenitic rocks. Notwithstanding the extent of the alteration, little is known about the processes that caused it, the source of the CO<sub>2</sub> or the conditions of alteration. This study investigates the mineralogy, stable (O, C) and radiogenic (Sr) isotope composition, and geochemistry of suites of variably carbonate altered ultramafics from the Meatiq area of the Central Eastern Desert (CED) of Egypt. The samples investigated include least-altered lizardite (Lz) serpentinites, antigorite (Atg) serpentinites and listvenitic rocks with associated carbonate and quartz veins. The C, O and Sr isotopes of the vein samples cluster between −8.1‰ and −6.8‰ for δ<sup>13</sup>C, +6.4‰ and +10.5‰ for δ<sup>18</sup>O, and <sup>87</sup>Sr/<sup>86</sup>Sr of 0.7028–0.70344, and plot within the depleted mantle compositional field. The serpentinites isotopic compositions plot on a mixing trend between the depleted-mantle and sedimentary carbonate fields. The carbonate veins contain abundant carbonic (CO<sub>2</sub>±CH<sub>4</sub>±N<sub>2</sub>) and aqueous-carbonic (H<sub>2</sub>O-NaCl-CO<sub>2</sub>±CH<sub>4</sub>±N<sub>2</sub>) low salinity fluid, with trapping conditions of 270–300°C and 0.7–1.1 kbar. The serpentinites are enriched in Au, As, S and other fluid-mobile elements relative to primitive and depleted mantle. The extensively carbonated Atg-serpentinites contain significantly lower concentrations of these elements than the Lz-serpentinites suggesting that they were depleted during carbonate alteration. Fluid inclusion and stable isotope compositions of Au deposits in the CED are similar to those from the carbonate veins investigated in the study and we suggest that carbonation of ANS ophiolitic rocks due to influx of mantle-derived CO<sub>2</sub>-bearing fluids caused break down of Au-bearing minerals such as pentlandite, releasing Au and S to the hydrothermal fluids that later formed the Au-deposits. This is the first time that gold has been observed to be remobilized from rocks during the lizardite–antigorite transition.
DOI: 10.7910/dvn/v1rjpf/u1tyx6
title: mea20150514_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/0qk4ff
title: mea20151209_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/sznn8a
title: mea20150421_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/hshag0
title: mea20151202_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/tkrvc7
title: mea20160619_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/wsalga
title: mea20160127_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3858450
title: Sitting time and obesity in a sample of adults from Europe and the USA

description: <b>Background:</b> Obesity is a risk factor for many chronic diseases and the prevalence is increasing worldwide. Research suggests that sedentary behaviour (sitting) may be related to obesity. <b>Aim:</b> To examine the association between sitting time and obesity, while controlling for physical activity, in a large international sample. <b>Subjects and methods:</b> In total, 5338 adults from the UK, USA, Germany, Spain, Italy, France, Portugal, Austria and Switzerland self-reported their total daily sitting time, physical activity, age, height and weight. BMI (kg/m<sup>2</sup>), total physical activity (MET-minutes/week) and sitting time (hours/day) were derived. Participants were grouped into quartiles based on their daily sitting time (&lt;4, 4–≤6, 6–≤8 and &gt;8 hours/day) and logistic regression models explored the odds of being obese vs normal weight for each sitting time quartile. <b>Results:</b> Participants in the highest sitting time quartile (≥8 hours/day) had 62% higher odds of obesity compared to participants in the lowest quartile (&lt;4 hours/day) after adjustment for physical activity and other confounding variables (OR = 1.62, 95% CI = 1.24–2.12, <i>p</i> <b>Conclusion:</b> Sitting time is associated with obesity in adults, independent of physical activity. Future research should clarify this association using objective measures of sitting time and physical activity to further inform health guidelines.
DOI: 10.7910/dvn/v1rjpf/wzgsw9
title: mea20150305_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3858450.v1
title: Sitting time and obesity in a sample of adults from Europe and the USA

description: <b>Background:</b> Obesity is a risk factor for many chronic diseases and the prevalence is increasing worldwide. Research suggests that sedentary behaviour (sitting) may be related to obesity. <b>Aim:</b> To examine the association between sitting time and obesity, while controlling for physical activity, in a large international sample. <b>Subjects and methods:</b> In total, 5338 adults from the UK, USA, Germany, Spain, Italy, France, Portugal, Austria and Switzerland self-reported their total daily sitting time, physical activity, age, height and weight. BMI (kg/m<sup>2</sup>), total physical activity (MET-minutes/week) and sitting time (hours/day) were derived. Participants were grouped into quartiles based on their daily sitting time (&lt;4, 4–≤6, 6–≤8 and &gt;8 hours/day) and logistic regression models explored the odds of being obese vs normal weight for each sitting time quartile. <b>Results:</b> Participants in the highest sitting time quartile (≥8 hours/day) had 62% higher odds of obesity compared to participants in the lowest quartile (&lt;4 hours/day) after adjustment for physical activity and other confounding variables (OR = 1.62, 95% CI = 1.24–2.12, <i>p</i> <b>Conclusion:</b> Sitting time is associated with obesity in adults, independent of physical activity. Future research should clarify this association using objective measures of sitting time and physical activity to further inform health guidelines.
DOI: 10.7910/dvn/v1rjpf/friq8s
title: mea20151001_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/5xeufm
title: mea20150428_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ve6cov
title: mea20160419_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/cixrew
title: mea20160730_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/7t2il0
title: mea20150427_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/u3ru2d
title: mea20161019_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/fqtnwv
title: mea20151017_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/62zbld
title: mea20150218_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/91zfoc
title: mea20150422_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/h9v13g
title: mea20150309_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/oxl5al
title: mea20150826_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/qrsz8t
title: mea20160305_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/fjpb4a
title: mea20160913_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/8wvif8
title: mea20160112_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/p1pnpw
title: mea20160427_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/2urr3e
title: mea20150108_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/nf5y6p
title: mea20150313_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/o0zlnd
title: mea20150809_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3858384
title: Quantitative microscopy uncovers ploidy changes during mitosis in live Drosophila embryos and their effect on nuclear size

description: Supplementary Data for manuscript"Quantitative microscopy uncovers ploidy changes during mitosis in live Drosophila embryos and their effect on nuclear size".- 4 Movies- Custom image analysis software: einSTA, DyVis3D- manuals and sample data sets for image analysis software <b></b>
DOI: 10.7910/dvn/v1rjpf/wksd3u
title: mea20160603_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3858384.v1
title: Quantitative microscopy uncovers ploidy changes during mitosis in live Drosophila embryos and their effect on nuclear size

description: Supplementary Data for manuscript"Quantitative microscopy uncovers ploidy changes during mitosis in live Drosophila embryos and their effect on nuclear size".- 4 Movies- Custom image analysis software: einSTA, DyVis3D- manuals and sample data sets for image analysis software <b></b>
DOI: 10.7910/dvn/v1rjpf/u3tlhk
title: mea20150102_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/6j3ebt
title: mea20150323_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/s2etjz
title: mea20151010_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/zqyp2p
title: mea20160801_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/jfge7k
title: mea20150804_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/u0mrba
title: mea20160922_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/qsduxb
title: mea20160323_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/z5p5xu
title: mea20160808_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/ck2isw
title: mea20150719_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3858351
title: The Origin of Mars' Crater Names

description: This is a list of small (&lt;60km) craters on Mars named after cities, towns, and villages around the world aimed specifically at finding the origin or meaning of the name.
DOI: 10.7910/dvn/v1rjpf/vfmxpp
title: mea20161106_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3858342
title: Data underlying figs in Singh & Levy, 2016: A Consensus Layer V Pyramidal Neuron Can Sustain Interpulse-Interval Coding

description: Each FigX_data.csv file contains data with headers corresponding to Figure X in the manuscript.
DOI: 10.7910/dvn/v1rjpf/xaamet
title: mea20160523_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3858339
title: The Origin of Mars' Crater Names

description: This is a list of small (&lt;60km) craters on Mars named after cities, towns, and villages around the world aimed specifically at finding the origin or meaning of the name.
DOI: 10.6084/m9.figshare.3858339.v1
title: The Origin of Mars' Crater Names

description: This is a list of small (&lt;60km) craters on Mars named after cities, towns, and villages around the world aimed specifically at finding the origin or meaning of the name.
DOI: 10.6084/m9.figshare.3858342.v2
title: Data underlying figs in Singh & Levy, 2016: A Consensus Layer V Pyramidal Neuron Can Sustain Interpulse-Interval Coding

description: Each FigX_data.csv file contains data with headers corresponding to Figure X in the manuscript.
DOI: 10.6084/m9.figshare.3858342.v1
title: Data underlying Fig 1

description: Times (in ms) for 120 trials of different excitations.
DOI: 10.7910/dvn/v1rjpf/fi2ocu
title: mea20151107_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/8cmvkd
title: mea20150423_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/zu3gjs
title: mea20160229_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3858291
title: PERSON Dataset

description: PERSON dataset:<br>Dataset created for paper "PERSON: Personalized Information Retrieval Evaluation Based on Citation Networks." Please cite the paper for any usage.<br><br>The dataset is produced by data cleaning of AMiner's citation network V2 dataset (https://aminer.org/citation). Anyone who wants to use PERSON dataset must cite Aminer's dataset (as explained in its homepage: Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang, and Zhong Su. ArnetMiner: Extraction and Mining of Academic Social Networks. In <i>Proceedings of the Fourteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining</i> (SIGKDD'2008). pp.990-998) as well as the aforementioned paper.<br><br>It includes two files: <br> 1- authors_giant.txt: the information of authors and their co-authors. The format is as follows:<br> author ID<br> author name<br> the list of coauthors delimited by "," (Each entry contains the ID of the coauthor followed by the number of times they co-authored a paper)<br> ...<br><br> 2- papers_giant.txt: the information of papers and references. The format is as follows:<br> paper ID<br> Is paper merged (See the first paper for details)<br> original paper ID (in Aminer's dataset)<br> blank<br> blank<br> blank<br> blank<br> title<br> abstract<br> time (only the year part is important)<br> blank<br> references to papers out of the PERSON dataset (indicated by Aminer's IDs)<br> references to papers inside the PERSON dataset (indicated by PERSON's IDs)<br> author IDs<br> ...<br><br>
DOI: 10.7910/dvn/v1rjpf/vtu5a7
title: mea20160424_03.txt.gz

description: :unav
DOI: 10.7910/dvn/v1rjpf/zzeqbw
title: mea20150905_03.txt.gz

description: :unav
DOI: 10.6084/m9.figshare.3858291.v1
title: PERSON Dataset

description: PERSON dataset:<br>Dataset created for paper "PERSON: Personalized Information Retrieval Evaluation Based on Citation Networks." Please cite the paper for any usage.<br><br>The dataset is produced by data cleaning of AMiner's citation network V2 dataset (https://aminer.org/citation). Anyone who wants to use PERSON dataset must cite Aminer's dataset (as explained in its homepage: Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang, and Zhong Su. ArnetMiner: Extraction and Mining of Academic Social Networks. In <i>Proceedings of the Fourteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining</i> (SIGKDD'2008). pp.990-998) as well as the aforementioned paper.<br><br>It includes two files: <br> 1- authors_giant.txt: the information of authors and their co-authors. The format is as follows:<br> author ID<br> author name<br> the list of coauthors delimited by "," (Each entry contains the ID of the coauthor followed by the number of times they co-authored a paper)<br> ...<br><br> 2- papers_giant.txt: the information of papers and references. The format is as follows:<br> paper ID<br> Is paper merged (See the first paper for details)<br> original paper ID (in Aminer's dataset)<br> blank<br> blank<br> blank<br> blank<br> title<br> abstract<br> time (only the year part is important)<br> blank<br> references to papers out of the PERSON dataset (indicated by Aminer's IDs)<br> references to papers inside the PERSON dataset (indicated by PERSON's IDs)<br> author IDs<br> ...<br><br>