cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6372860
title: Literacy scales

description: This is a measure of literacy needs by occupations. It measure literacy according to the deffinition made by the OECD (2010). In that sense, it includes linguistic skills (reading, writting and speaking) but also numeracy and ICT skills. More in detail, 32 Likert-type questions from the Survey of Adult Skill were used. (8 questions about the frequency of use of reading at work , 4 questions about writing , 7 questions about speech which are titled influence and it is considered a general skill, 6 questions about numeracy and 7 questions about the use of ICT). The measures quantify the intensity of use for each of the five kinds of skills literacy linguistic skill in an occupation based on quantile distributions. A global indicator of literacy is also provided as a simple average of the other 5 skills.
description: For mor details, you can consult: Corbella, T. (in press). Desarrollo de un indicador de uso del lenguaje en el puesto de trabajo basado en cuantiles aplicado a datos del PIAAC. In: Vidal and Alarcón (eds.) Lenguaje y Ocupaciones. Editorial URV
description: {"references": ["https://www.educacionyfp.gob.es/inee/evaluaciones-internacionales/piaac.html", "http://www.publicacions.urv.cat/cataleg/recerca/15-cataleg/recerca/899-ocupaciones-y-lenguaje", "The International Standard Classification of Occupations (ISCO)", "Clasificaci\u00f3n Internacional Uniforme de Ocupaciones (CIUO-08)", "O\u00b4NET - Occupational Information Network"]}
DOI: 10.5281/zenodo.6372859
title: Literacy scales

description: This is a measure of literacy needs by occupations. It measure literacy according to the deffinition made by the OECD (2010). In that sense, it includes linguistic skills (reading, writting and speaking) but also numeracy and ICT skills. More in detail, 32 Likert-type questions from the Survey of Adult Skill were used. (8 questions about the frequency of use of reading at work , 4 questions about writing , 7 questions about speech which are titled influence and it is considered a general skill, 6 questions about numeracy and 7 questions about the use of ICT). The measures quantify the intensity of use for each of the five kinds of skills literacy linguistic skill in an occupation based on quantile distributions. A global indicator of literacy is also provided as a simple average of the other 5 skills.
description: For mor details, you can consult: Corbella, T. (in press). Desarrollo de un indicador de uso del lenguaje en el puesto de trabajo basado en cuantiles aplicado a datos del PIAAC. In: Vidal and Alarcón (eds.) Lenguaje y Ocupaciones. Editorial URV
description: {"references": ["https://www.educacionyfp.gob.es/inee/evaluaciones-internacionales/piaac.html", "http://www.publicacions.urv.cat/cataleg/recerca/15-cataleg/recerca/899-ocupaciones-y-lenguaje", "The International Standard Classification of Occupations (ISCO)", "Clasificaci\u00f3n Internacional Uniforme de Ocupaciones (CIUO-08)", "O\u00b4NET - Occupational Information Network"]}
DOI: 10.5281/zenodo.6337104
title: DocTOR models and cross-validation dataset

description: Dataset necessary for DocTOR utility. DocTOR (Direct fOreCast Target On Reaction), is a utility written in python3.9 (using the conda workframe) that allows the user to upload a list of Uniprot IDs and Adverse reactions (from the available models) in order to study the relationship between the two. On output the program will assign a positive or negative class to the protein, assessing its possible involvement in the selected ADRs onset. DocTOR exploits the data coming from T-ARDIS [https://doi.org/10.1093/database/baab068] to train different Machine Learning approaches (SVM, RF, NN) using network topological measurements as features. The prediction coming from the single trained models are combined in a meta-predictor exploiting three different voting systems. The results of the meta-predictor together with the ones from the single ML method will be available in the output log file (named "predictions_community" or "predictions_curated" based on the database type). The DocTOR utility is avaiable at https://github.com/cristian931/DocTOR
DOI: 10.5281/zenodo.6337103
title: DocTOR models and cross-validation dataset

description: Dataset necessary for DocTOR utility. DocTOR (Direct fOreCast Target On Reaction), is a utility written in python3.9 (using the conda workframe) that allows the user to upload a list of Uniprot IDs and Adverse reactions (from the available models) in order to study the relationship between the two. On output the program will assign a positive or negative class to the protein, assessing its possible involvement in the selected ADRs onset. DocTOR exploits the data coming from T-ARDIS [https://doi.org/10.1093/database/baab068] to train different Machine Learning approaches (SVM, RF, NN) using network topological measurements as features. The prediction coming from the single trained models are combined in a meta-predictor exploiting three different voting systems. The results of the meta-predictor together with the ones from the single ML method will be available in the output log file (named "predictions_community" or "predictions_curated" based on the database type). The DocTOR utility is avaiable at https://github.com/cristian931/DocTOR
DOI: 10.6084/m9.figshare.19027562
title: Transgenerational Data

description: The dataset shows offspring number from an experiment testing for transgenerational effects of pathogen exposure. Explanation of column headers in the dataset are listed below.<br>file = transgen_data_sub.csv Data on offspring number from an experiment testing for transgenerational effects of pathogen exposure. Explanation of column headers in the dataset are listed below. Batch = experimental batch, each letter denotes a separate batch Worm_isotype = genotype of Caenorhabditis elegans used Par_exp = microbial treatment that parental generation was exposed to. “MSSA476 – HK” = exposed to heat killed pathogen; “MSSA476 – LIVE” = exposed to live pathogen; “PY79 – FOOD” = exposed to food control Par_ID = parental identifier F1_exp = microbial treatment that F1 generation was exposed to. “MSSA476 – LIVE” = exposed to live pathogen; “PY79 – FOOD” = exposed to food control Child_ID = F1 individual identifier d1 = tech rep 1 counts of offspring d2 = tech rep 2 counts of offspring d3 = tech rep 3 counts of offspring d4 = tech rep 4 counts of offspring droplet_average = offspring average calculated from d1 to d4 pop_size = total population size of worms formed from the reproduction of F1
DOI: 10.6084/m9.figshare.19027562.v1
title: Transgenerational Data

description: The dataset shows offspring number from an experiment testing for transgenerational effects of pathogen exposure. Explanation of column headers in the dataset are listed below.<br>file = transgen_data_sub.csv Data on offspring number from an experiment testing for transgenerational effects of pathogen exposure. Explanation of column headers in the dataset are listed below. Batch = experimental batch, each letter denotes a separate batch Worm_isotype = genotype of Caenorhabditis elegans used Par_exp = microbial treatment that parental generation was exposed to. “MSSA476 – HK” = exposed to heat killed pathogen; “MSSA476 – LIVE” = exposed to live pathogen; “PY79 – FOOD” = exposed to food control Par_ID = parental identifier F1_exp = microbial treatment that F1 generation was exposed to. “MSSA476 – LIVE” = exposed to live pathogen; “PY79 – FOOD” = exposed to food control Child_ID = F1 individual identifier d1 = tech rep 1 counts of offspring d2 = tech rep 2 counts of offspring d3 = tech rep 3 counts of offspring d4 = tech rep 4 counts of offspring droplet_average = offspring average calculated from d1 to d4 pop_size = total population size of worms formed from the reproduction of F1
DOI: 10.6084/m9.figshare.19027436
title: Cumulative Offspring Data

description: file = offspring_data_sub.csv This dataset is associated with the study: Ordovás-Montañés<sup> </sup>et al (2022) Reproductive consequences of transient pathogen exposure across host genotypes and generations. Ecology & Evolution.<br>The dataset covers the number of offspring produced by different host genotypes across three time points, after exposure to different pathogen treatments. Explanation of column headers in the dataset are listed below. exposure = microbial treatment that worm was exposed to. “MSSA476 – HK” = exposed to heat killed pathogen; “MSSA476 – LIVE” = exposed to live pathogen; “PY79 – FOOD” = exposed to food control run = run identified worm_ID = individual worm identifier TSA_origin_plate = plate identifier Biological.Rep = biological rep identifier Batch strain = genotype of Caenorhabditis elegans used Time = time point, in hours, that offspring was counted at prog_at_time_interval = additional offspring since previous time point total_brood_so_far = total offspring at given time point
DOI: 10.6084/m9.figshare.19027436.v1
title: Cumulative Offspring Data

description: file = offspring_data_sub.csv This dataset is associated with the study: Ordovás-Montañés<sup> </sup>et al (2022) Reproductive consequences of transient pathogen exposure across host genotypes and generations. Ecology & Evolution.<br>The dataset covers the number of offspring produced by different host genotypes across three time points, after exposure to different pathogen treatments. Explanation of column headers in the dataset are listed below. exposure = microbial treatment that worm was exposed to. “MSSA476 – HK” = exposed to heat killed pathogen; “MSSA476 – LIVE” = exposed to live pathogen; “PY79 – FOOD” = exposed to food control run = run identified worm_ID = individual worm identifier TSA_origin_plate = plate identifier Biological.Rep = biological rep identifier Batch strain = genotype of Caenorhabditis elegans used Time = time point, in hours, that offspring was counted at prog_at_time_interval = additional offspring since previous time point total_brood_so_far = total offspring at given time point
DOI: 10.5281/zenodo.6372728
title: INFORE22 sea trial open dataset

description: Data have been generated during the INFORE22 sea trial, run by CMRE from 21st February to 1st March 2022 in the Gulf of La Spezia to experiment and validate the CMRE hybrid robotic network in support of the INFORE maritime use case [*]. The dataset, which covers two days of experiments (28 February and 1 March 2022), includes: - GPS NMEA positions of targets - Target contact messages from marine autonomous robots - Thermal camera video streams with metadata on detected targets - AIS data (log of NMEA messages, processed messages, maps of vessel positions), showing vessel traffic in the Gulf of La Spezia during the experiments This dataset, is complemented by a Raw acoustic data from SliTa array towed on AUVs (and Matlab reader) published by the authors in Zenodo https://zenodo.org/record/6375048. For a full description of the dataset, see [**] <strong>Conditions for use and distributions</strong> This dataset is provided by NATO STO CMRE within the condition stated in the H2020 INFORE Grant and Consortium Agreement (GA. no. 825070) . The creation of derived products, as well the use in scientific publications must be pre-approved by CMRE and acknowledged. <strong>Non liability clause</strong> These data and software are provided in the scope of INFORE by NATO STO CMRE, in compliance with the INFORE open data strategy. Data and software are provided as they are. NATO and NATO STO CMRE decline any responsibility for bugs and any damage or accidental issue that the use of those data and software could cause. <strong>References</strong> [*] Gabriele Ferri, Raffaele Grasso, Elena Camossi, Francesca de Rosa, Alessandro Faggiani, Kevin LePage, Konstantina Bereta, Marios Vodas, Dimitris Kladis, Antonis Kontaxakis, Nikos Giatrakos, Antonios Deligiannakis, Maritime Use Case: Final Evaluation Report Work Package 3 Tasks 3.3 INFORE Deliverable D3.3 [**] Nikos Giatrakos, Antonios Deligiannakis, Arnau Montagud, Miguel Ponce de León, Thaleia Ntiniakou, Holger Arndt, Stefan Burkard, Konstantina Bereta, Marios Vodas, Dimitris Kladis, Raffaele Grasso, Gabriele Ferri, Arjan Vermeij, Alessandro Faggiani, Elena Camossi, Kevin Le Page: Data Management Plan V3 Work Package 8 Task 8.3 INFORE Deliverable 8.6
DOI: 10.5281/zenodo.6372727
title: INFORE22 sea trial open dataset

description: Data have been generated during the INFORE22 sea trial, run by CMRE from 21st February to 1st March 2022 in the Gulf of La Spezia to experiment and validate the CMRE hybrid robotic network in support of the INFORE maritime use case [*]. The dataset, which covers two days of experiments (28 February and 1 March 2022), includes: - GPS NMEA positions of targets - Target contact messages from marine autonomous robots - Thermal camera video streams with metadata on detected targets - AIS data (log of NMEA messages, processed messages, maps of vessel positions), showing vessel traffic in the Gulf of La Spezia during the experiments This dataset, is complemented by a Raw acoustic data from SliTa array towed on AUVs (and Matlab reader) published by the authors in Zenodo https://zenodo.org/record/6375048. For a full description of the dataset, see [**] <strong>Conditions for use and distributions</strong> This dataset is provided by NATO STO CMRE within the condition stated in the H2020 INFORE Grant and Consortium Agreement (GA. no. 825070) . The creation of derived products, as well the use in scientific publications must be pre-approved by CMRE and acknowledged. <strong>Non liability clause</strong> These data and software are provided in the scope of INFORE by NATO STO CMRE, in compliance with the INFORE open data strategy. Data and software are provided as they are. NATO and NATO STO CMRE decline any responsibility for bugs and any damage or accidental issue that the use of those data and software could cause. <strong>References</strong> [*] Gabriele Ferri, Raffaele Grasso, Elena Camossi, Francesca de Rosa, Alessandro Faggiani, Kevin LePage, Konstantina Bereta, Marios Vodas, Dimitris Kladis, Antonis Kontaxakis, Nikos Giatrakos, Antonios Deligiannakis, Maritime Use Case: Final Evaluation Report Work Package 3 Tasks 3.3 INFORE Deliverable D3.3 [**] Nikos Giatrakos, Antonios Deligiannakis, Arnau Montagud, Miguel Ponce de León, Thaleia Ntiniakou, Holger Arndt, Stefan Burkard, Konstantina Bereta, Marios Vodas, Dimitris Kladis, Raffaele Grasso, Gabriele Ferri, Arjan Vermeij, Alessandro Faggiani, Elena Camossi, Kevin Le Page: Data Management Plan V3 Work Package 8 Task 8.3 INFORE Deliverable 8.6
DOI: 10.5519/qd.umk0xk7g
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (32028 records)
DOI: 10.6084/m9.figshare.19403513
title: Flexcom extensible tether parametric analysis data

description: Parametric study of extensible tethers on DeepCWind OC4 FOWT model using commercial FEA software Flexcom. <br>Data contains:- 9 above-rated/extreme load cases with various combinations of wind speed and wave height, for both linear and non-linear stiffness extensible tethers- For each load case, different values of tether length and stiffness<br>
DOI: 10.6084/m9.figshare.19403513.v1
title: Flexcom extensible tether parametric analysis data

description: Parametric study of extensible tethers on DeepCWind OC4 FOWT model using commercial FEA software Flexcom. <br>Data contains:- 9 above-rated/extreme load cases with various combinations of wind speed and wave height, for both linear and non-linear stiffness extensible tethers- For each load case, different values of tether length and stiffness<br>
DOI: 10.6084/m9.figshare.19328894.v2
title: Encounter with a selfish virus sabotages its vector to orient towards requisite host plant: A case study with chilli leaf curl virus-whitefly

description: GC -MS data (heat map & table). Differential expressed genes list and 3-Way ANOVA table.
DOI: 10.6084/m9.figshare.19328894
title: Encounter with a selfish virus sabotages its vector to orient towards requisite host plant: A case study with chilli leaf curl virus-whitefly

description: GC -MS data (heat map & table). Differential expressed genes list and 3-Way ANOVA table.
DOI: 10.6084/m9.figshare.19328894.v1
title: Encounter with a selfish virus sabotages its vector to orient towards requisite host plant: A case study with chilli leaf curl virus-whitefly

description: GC -MS data (heat map & table). Differential expressed genes list and 3-Way ANOVA table.
DOI: 10.34820/fk2/mp70pl/uxnzvv
title: exclude

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DOI: 10.34820/fk2/mp70pl/36cp9n
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DOI: 10.34820/fk2/mp70pl/gx8yxy
title: items_kompas_11.tab

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DOI: 10.34820/fk2/mp70pl/libkee
title: items_detik_27.tab

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DOI: 10.34820/fk2/mp70pl/dsfx8o
title: items_detik_9.tab

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DOI: 10.34820/fk2/mp70pl/9uy11r
title: items_kompas_16.tab

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DOI: 10.34820/fk2/mp70pl/6ctw9z
title: fsmonitor-watchman.sample

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DOI: 10.34820/fk2/mp70pl/ioo37y
title: 5b75beb854f2e48298192b20d87a84055991e7

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DOI: 10.34820/fk2/mp70pl/oh51zd
title: items_detik_8.tab

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DOI: 10.34820/fk2/mp70pl/aupr2y
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title: items_kompas_15.tab

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title: items_detik_7.tab

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DOI: 10.34820/fk2/mp70pl/yjejae
title: items_detik_16.tab

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DOI: 10.34820/fk2/mp70pl/kmppob
title: post-update.sample

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DOI: 10.34820/fk2/mp70pl/gzodci
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DOI: 10.34820/fk2/mp70pl/jpzan2
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DOI: 10.34820/fk2/mp70pl/elhmuz
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DOI: 10.34820/fk2/mp70pl/2oieav
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DOI: 10.34820/fk2/mp70pl/3khfx5
title: items_kompas_10.tab

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DOI: 10.34820/fk2/mp70pl/fr44cq
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DOI: 10.34820/fk2/mp70pl/vgcfcs
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DOI: 10.34820/fk2/mp70pl/oquvda
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DOI: 10.34820/fk2/mp70pl/1q2nfz
title: index

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DOI: 10.34820/fk2/mp70pl/byiyj0
title: pre-push.sample

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DOI: 10.34820/fk2/mp70pl/pne1lf
title: items_detik_17.tab

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DOI: 10.34820/fk2/mp70pl/zdr3nd
title: items_detik_23.tab

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DOI: 10.34820/fk2/mp70pl/9cd9qy
title: config

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DOI: 10.34820/fk2/mp70pl/i7gv0g
title: items_kompas_3.tab

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DOI: 10.34820/fk2/mp70pl/zhtopt
title: items_kompas_6.tab

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DOI: 10.5281/zenodo.6378934
title: SciExpeM - 10.5281/zenodo.6378934

description: Togbé, C.; Halter, F.; Foucher, F.; Mounaim-Rousselle, C.; Dagaut, P. - Elsevier Bv, 2011
DOI: 10.34820/fk2/mp70pl/ok9s8r
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DOI: 10.5281/zenodo.6378933
title: SciExpeM - 10.5281/zenodo.6378934

description: Togbé, C.; Halter, F.; Foucher, F.; Mounaim-Rousselle, C.; Dagaut, P. - Elsevier Bv, 2011
DOI: 10.34820/fk2/mp70pl/diwxqa
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title: items_detik_10.tab

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DOI: 10.34820/fk2/mp70pl/svsjnp
title: pre-merge-commit.sample

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DOI: 10.34820/fk2/mp70pl/tde0cx
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DOI: 10.34820/fk2/mp70pl/9cghw0
title: pre-rebase.sample

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DOI: 10.34820/fk2/mp70pl/e43mqk
title: items_detik_6.tab

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DOI: 10.34820/fk2/mp70pl/evkhdk
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DOI: 10.34820/fk2/mp70pl/qf2br6
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DOI: 10.34820/fk2/mp70pl/rdzmde
title: HEAD

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DOI: 10.34820/fk2/mp70pl/u6qqs1
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DOI: 10.34820/fk2/mp70pl/d6awdr
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DOI: 10.34820/fk2/mp70pl/jaf7u6
title: 44d07bde4474b3d6faf8f51951fb2fcfefab0d

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DOI: 10.34820/fk2/mp70pl/t4abkr
title: items_kompas_18.tab

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