cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13988100.v1
title: Supplementary file 3: Estimated variance and the genomic heritability of miRNA from Genetic regulation and heritability of miRNA and mRNA expression link to phosphorus utilization and gut microbiome

description: Improved utilization of phytates and mineral phosphorus (P) in monogastric animals contributes significantly to preserving the finite resource of mineral P and mitigating environmental pollution. In order to identify pathways and to prioritize candidate genes related to P-utilization (PU), the genomic heritability of 77 and 80 trait-dependent expressed miRNAs and mRNAs in 482 Japanese quail were estimated and eQTL (expression quantitative trait loci) were detected. In total, 104 miR-eQTL (microRNA expression quantitative traits loci) were associated with SNPs markers (false discovery rate (FDR) less than 10%) including 41 eQTL of eight miRNAs. Similarly, 944 mRNA-eQTL were identified at the 5% FDR threshold with 573 being cis-eQTL of 36 mRNAs. High heritabilities of miRNA and mRNA expression coincide with highly significant eQTL. Integration of phenotypic data with transcriptome and microbiome data of the same animals revealed genetic regulated mRNA and miRNA transcripts (SMAD3, CAV1, ENNPP6, ATP2B4, miR-148a-3p, miR-146b-5p, miR-16-5p, miR-194, miR-215-5p, miR-199-3p, miR-1388a-3p) and microbes (Candidatus Arthromitus, Enterococcus) that are associated with PU. The results reveal novel insights into the role of mRNAs and miRNAs in host gut tissue functions, which are involved in PU and other related traits, in terms of the genetic regulation and inheritance of their expression and in association with microbiota components.
DOI: 10.6084/m9.figshare.12894296
title: Bootstrap Indexes

description: These are indexes for the 2000 bootstrap resamples for dimensions {2,5,10.20}, using Dx{100,178,316,562,1000} points, produced using a uniformly distributed integer number random generator, through the MATLAB function randi(). Unfortunately, seed value was not recorded.<br>If you use this dataset please use the citation for the complete collection, as follows:<br>Mario A. Muñoz "LEOPARD: LEarning and OPtimization Archive of Research Data, Version 1.0" Aug. 2020 DOI:10.6084/m9.figshare.c.5106758.
DOI: 10.6084/m9.figshare.12894299
title: ELA Feature Values & Statistics

description: Values and statistics for 33 ELA features:- FDC- Dispersion [7 levels log10 spaced]- Information significance [First, Second and N orders]- Information Content [Using both NN and Random sorting]- Linear and Quadratic meta-models- Skewness, Kurtosis and Entropy of cost distribution<br>Statistics were calculated through bootstrapping the function data (composed of Xinput and Yinput) using bootstrap indexes. These are:- Bootstrap variance- Mean Squared Error [defined as the squared difference between the value and the bootstrap mean]- Bias- Confidence Interval at 95% [Divided into High and Low value]<br>Raw bootstrap values are provided in a different dataset. Code to be uploaded.<br>If you use this dataset please use the citation for the complete collection, as follows:<br>Mario A. Muñoz "LEOPARD: LEarning and OPtimization Archive of Research Data, Version 1.0" Aug. 2020 DOI:10.6084/m9.figshare.c.5106758.
DOI: 10.6084/m9.figshare.12894302.v1
title: ELA Bootstrapped Feature Values

description: Bootstrapped values for 33 ELA features:- FDC- Dispersion [7 Values]- Information significance [First, Second and N orders]- Information Content [Using both NN and Random sorting]- Linear and Quadratic meta-models- Skewness, Kurtosis and Entropy of cost distribution<br>Values were calculated through bootstrapping the function data (composed of Xinput and Yinput) using bootstrap indexes. These are used to calculate statistics such as:- Bootstrap variance- Mean Squared Error [defined as the squared difference between the value and the bootstrap mean]- Bias- Confidence Interval at 95% [Divided into High and Low value]<br>Statistic values are provided in a different dataset. Code to be uploaded.<br>If you use this dataset please use the citation for the complete collection, as follows:<br>Mario A. Muñoz "LEOPARD: LEarning and OPtimization Archive of Research Data, Version 1.0" Aug. 2020 DOI:10.6084/m9.figshare.c.5106758.
DOI: 10.6084/m9.figshare.12894290
title: Input Samples BBOB

description: These are function inputs for dimensions {2,5,10.20}, using Dx{100,178,316,562,1000} points, generated using Latin hypercube sampling as produced by the MATLAB function lhsdesign(). The data has been scaled from [0,1] to [-5,5] to fit the range of the BBOB benchmark set. The sample size has been generated using an equally spaced log10 scale. Unfortunately, seed values were not recorded.<br>If you use this dataset please use the citation for the complete collection, as follows:<br>Mario A. Muñoz "LEOPARD: LEarning and OPtimization Archive of Research Data, Version 1.0" Aug. 2020 DOI:10.6084/m9.figshare.c.5106758.
DOI: 10.6084/m9.figshare.12894302
title: ELA Bootstrapped Feature Values

description: Bootstrapped values for 33 ELA features:- FDC- Dispersion [7 Values]- Information significance [First, Second and N orders]- Information Content [Using both NN and Random sorting]- Linear and Quadratic meta-models- Skewness, Kurtosis and Entropy of cost distribution<br>Values were calculated through bootstrapping the function data (composed of Xinput and Yinput) using bootstrap indexes. These are used to calculate statistics such as:- Bootstrap variance- Mean Squared Error [defined as the squared difference between the value and the bootstrap mean]- Bias- Confidence Interval at 95% [Divided into High and Low value]<br>Statistic values are provided in a different dataset. Code to be uploaded.<br>If you use this dataset please use the citation for the complete collection, as follows:<br>Mario A. Muñoz "LEOPARD: LEarning and OPtimization Archive of Research Data, Version 1.0" Aug. 2020 DOI:10.6084/m9.figshare.c.5106758.
DOI: 10.6084/m9.figshare.12894434
title: Output Samples BBOB

description: These are function outputs for the first 15 instances from the 24 noiseless BBOB benchmark set v13.09 at dimensions {2,5,10.20}, using Dx{100,178,316,562,1000} points, generated using the respective Xinputs. By sharing the function inputs, it is guaranteed that the differences between outputs only depend on the function instance. The data has not been scaled.<br>If you use this dataset please use the citation for the complete collection, as follows:<br>Mario A. Muñoz "LEOPARD: LEarning and OPtimization Archive of Research Data, Version 1.0" Aug. 2020 DOI:10.6084/m9.figshare.c.5106758.
DOI: 10.6084/m9.figshare.12894434.v1
title: Output Samples BBOB

description: These are function outputs for the first 15 instances from the 24 noiseless BBOB benchmark set v13.09 at dimensions {2,5,10.20}, using Dx{100,178,316,562,1000} points, generated using the respective Xinputs. By sharing the function inputs, it is guaranteed that the differences between outputs only depend on the function instance. The data has not been scaled.<br>If you use this dataset please use the citation for the complete collection, as follows:<br>Mario A. Muñoz "LEOPARD: LEarning and OPtimization Archive of Research Data, Version 1.0" Aug. 2020 DOI:10.6084/m9.figshare.c.5106758.
DOI: 10.6084/m9.figshare.12894299.v1
title: ELA Feature Values & Statistics

description: Values and statistics for 33 ELA features:- FDC- Dispersion [7 levels log10 spaced]- Information significance [First, Second and N orders]- Information Content [Using both NN and Random sorting]- Linear and Quadratic meta-models- Skewness, Kurtosis and Entropy of cost distribution<br>Statistics were calculated through bootstrapping the function data (composed of Xinput and Yinput) using bootstrap indexes. These are:- Bootstrap variance- Mean Squared Error [defined as the squared difference between the value and the bootstrap mean]- Bias- Confidence Interval at 95% [Divided into High and Low value]<br>Raw bootstrap values are provided in a different dataset. Code to be uploaded.<br>If you use this dataset please use the citation for the complete collection, as follows:<br>Mario A. Muñoz "LEOPARD: LEarning and OPtimization Archive of Research Data, Version 1.0" Aug. 2020 DOI:10.6084/m9.figshare.c.5106758.
DOI: 10.6084/m9.figshare.12894290.v1
title: Input Samples BBOB

description: These are function inputs for dimensions {2,5,10.20}, using Dx{100,178,316,562,1000} points, generated using Latin hypercube sampling as produced by the MATLAB function lhsdesign(). The data has been scaled from [0,1] to [-5,5] to fit the range of the BBOB benchmark set. The sample size has been generated using an equally spaced log10 scale. Unfortunately, seed values were not recorded.<br>If you use this dataset please use the citation for the complete collection, as follows:<br>Mario A. Muñoz "LEOPARD: LEarning and OPtimization Archive of Research Data, Version 1.0" Aug. 2020 DOI:10.6084/m9.figshare.c.5106758.
DOI: 10.21227/c9yx-x936
title: Unpaired MR-CT Brain Dataset for Unsupervised Image Translation

description: The Magnetic Resonance – Computed Tomography (MR-CT) Jordan University Hospital (JUH) dataset has been collected after receiving Institutional Review Board (IRB) approval of the hospital and consent forms have been obtained from all patients. All procedures followed are consistent with the ethics of handling patients’ data.The dataset consists of unpaired brain CT and MR images of 20 patients scanned for radiotherapy treatment planning for brain tumors. The dataset contains T2-MR and CT images for patients aged between 26-71 years with mean-std equal to 47-14.07. The MR images were acquired with a 5.00mm T Siemens Verio 3T using a T2-weighted without contrast agent, 3 Fat sat pulses (FS), 2500-4000 TR, 20-30 TE, and 90/180 flip angle. The CT images were acquired with Siemens Somatom scanner with 2.46mGY.cm dose length, 130KV voltage, 113-327 mAs tube current, topogram acquisition protocol, 64 dual source, one projection, and slice thickness of 7.0mm. Smooth and sharp filters have been applied to CT images. The MR scans have a resolution of 0.7×0.6×5 mm^3, while the CT scans have a resolution of 0.6×0.6×7 mm^3. There are a total of 420 2D axial image slices in the compressed folder (420 MR and 420 CT 2D axial image slices).(Please contact Omar Al-Kadi at o.alkadi[at]ju.edu.jo for compressed file access code)
DOI: 10.21227/fe9x-qg64
title: Unpaired MR-CT Brain Dataset for Unsupervised Image Translation

description: The Magnetic Resonance – Computed Tomography (MR-CT) Jordan University Hospital (JUH) dataset has been collected after receiving Institutional Review Board (IRB) approval of the hospital and consent forms have been obtained from all patients. All procedures followed are consistent with the ethics of handling patients’ data.The dataset consists of unpaired brain CT and MR images of 20 patients scanned for radiotherapy treatment planning for brain tumors. The dataset contains T2-MR and CT images for patients aged between 26-71 years with mean-std equal to 47-14.07. The MR images were acquired with a 5.00mm T Siemens Verio 3T using a T2-weighted without contrast agent, 3 Fat sat pulses (FS), 2500-4000 TR, 20-30 TE, and 90/180 flip angle. The CT images were acquired with Siemens Somatom scanner with 2.46mGY.cm dose length, 130KV voltage, 113-327 mAs tube current, topogram acquisition protocol, 64 dual source, one projection, and slice thickness of 7.0mm. Smooth and sharp filters have been applied to CT images. The MR scans have a resolution of 0.7×0.6×5 mm^3, while the CT scans have a resolution of 0.6×0.6×7 mm^3. There are a total of 840 2D axial image slices in the compressed folder (420 MR and 420 CT 2D axial image slices).(Please contact Omar Al-Kadi at o.alkadi[at]ju.edu.jo for compressed file access code)
DOI: 10.6084/m9.figshare.13989542
title: Odiong CEJ Data.xlsx

description: There are two sets of files in the MS Excel document.First is Rainfall data for Calabar, Nigeria.Second is CORDEX raw data for Calabar grid.
DOI: 10.6084/m9.figshare.13989542.v1
title: Odiong CEJ Data.xlsx

description: There are two sets of files in the MS Excel document.First is Rainfall data for Calabar, Nigeria.Second is CORDEX raw data for Calabar grid.
DOI: 10.5519/qd.pwz13il7
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1747 records)
DOI: 10.6084/m9.figshare.13989839
title: Calabar Measured and CORDEX Data.xlsx

description: There are two sets of files in the MS Excel document.First is Rainfall data for Calabar, Nigeria.Second is CORDEX raw data for Calabar grid.
DOI: 10.5281/zenodo.4539990
title: Законы о бюджете города Москвы на 2014-2018 года и плановый период. Ведомственная структура расходов

description: Ведомственная структура расходов бюджета города Москвы утвержденная Законом о бюджете города Москвы с 2014 по 2018 года и плановый период.
DOI: 10.5281/zenodo.4539989
title: Законы о бюджете города Москвы на 2014-2018 года и плановый период. Ведомственная структура расходов

description: Ведомственная структура расходов бюджета города Москвы утвержденная Законом о бюджете города Москвы с 2014 по 2018 года и плановый период.
DOI: 10.25646/8015
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.17031/omgq4g
title: Marine Recorder JNCC Public Offshore Snapshot 2020_1 – habitat data trial

description: Extract of the publicly available offshore UK marine benthic data holdings of JNCC (Joint Nature Conservation Committee), held within the “Marine Recorder” data management system. Data comprises multi-thematic benthic surveys in the offshore, including both taxa occurrence and abundance alongside physical and biotope recordings. This dataset is being used as a pilot study into submitting fully classified biotope data into OBIS alongside associated taxonomic occurrences under the EU EMODnet initiative.
DOI: 10.34740/kaggle/dsv/1937585
title: License Plate Dataset
DOI: 10.6084/m9.figshare.13991150
title: ADHD_diet_feasibility

description: Data from a feasibility study exploring a microbiome-targeted dietary intervention in children with ADHD.
DOI: 10.17909/t9-81yt-ch19
title: HST/STIS obervations of IXVel

description: Part of HST Program ID 14637, the goal of which was to obtain very wide band (1150-10000 A) spectra of 5 novalike variables including IX Vel by observing them with multiple gratings in a single orbit.
DOI: 10.5281/zenodo.4534892
title: Data to "Friction is preferred over grasp configuration in precision grip grasping"

description: This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in: Klein, L. K., Maiello G., Fleming, R. W., & Voudouris, D. (2021) Friction is preferred over grasp configuration in precision grip grasping. Journal of Neurophysiology. A preprint version of the manuscript is available at: https://doi.org/10.1101/2021.01.13.426550
description: This research was supported by the DFG (IRTG-1901: "The Brain in Action"; SFB-TRR-135: "Cardinal Mechanisms of Perception", project number: 222641018).
DOI: 10.5281/zenodo.4534891
title: Data to "Friction is preferred over grasp configuration in precision grip grasping"

description: This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in: Klein, L. K., Maiello G., Fleming, R. W., & Voudouris, D. (2021) Friction is preferred over grasp configuration in precision grip grasping. Journal of Neurophysiology. A preprint version of the manuscript is available at: https://doi.org/10.1101/2021.01.13.426550
description: This research was supported by the DFG (IRTG-1901: "The Brain in Action"; SFB-TRR-135: "Cardinal Mechanisms of Perception", project number: 222641018).
DOI: 10.34740/kaggle/dsv/1938098
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5281/zenodo.4540173
title: Conjunto de datos de fuentes digitales para el estudio del exilio republicano español

description: Conjunto de datos elaborado para la realización de un análisis acerca del uso de las fuentes digitales para el estudio del exilio republicano español, publicado en la revista Hispania Nova
DOI: 10.5281/zenodo.4229793
title: Conjunto de datos de fuentes digitales para el estudio del exilio republicano español

description: Conjunto de datos elaborado para la realización de un análisis acerca del uso de las fuentes digitales para el estudio del exilio republicano español, publicado en la revista Hispania Nova
DOI: 10.5281/zenodo.4540166
title: A Multi-Band Forced-Photometry Catalog in the ELAIS-S1 Field

description: These are the data products presented in the Zou et al. (2021) RNAAS paper, “A Multi-Band Forced Photometry Catalog in the ELAIS-S1 Field”.
DOI: 10.6084/m9.figshare.13992317
title: HOL Light Code

description: This file contains the HOL Light code for the formal verification of MATLAB functions. <br>
DOI: 10.6084/m9.figshare.13992317.v1
title: HOL Light Code

description: This file contains the HOL Light code for the formal verification of MATLAB functions. <br>
DOI: 10.5281/zenodo.4540178
title: A Multi-Band Forced-Photometry Catalog in the ELAIS-S1 Field

description: These are the data products presented in the Zou et al. (2021) RNAAS paper, “A Multi-Band Forced Photometry Catalog in the ELAIS-S1 Field”.
DOI: 10.6084/m9.figshare.13992779
title: Estrategia de busqueda

description: Search strategy
DOI: 10.21227/cbmz-zz42
title: TL multiplier - supplemental materials

description: This directory contains the Verilog sources of our paper,” A two-stage operand trimming approximate logarithmic multiplier”, which is currently under review.
DOI: 10.6084/m9.figshare.13992839.v1
title: Insects-1099257_Data.xlsx

description: Data corresponding to the following accepted ms: Evaluating the Impact of Post-Emergence Weed Control in Honeybee Colonies Located in Different Agricultural Surroundings
DOI: 10.6084/m9.figshare.13992779.v1
title: Untitled Item

description: Search strategy
DOI: 10.6084/m9.figshare.13992839
title: Insects-1099257_Data.xlsx

description: Data corresponding to the following accepted ms: Evaluating the Impact of Post-Emergence Weed Control in Honeybee Colonies Located in Different Agricultural Surroundings
DOI: 10.6084/m9.figshare.12887249
title: Ignorance and Prejudice in Software Fairness

description: Machine learning software can be unfair when making human-related decisions, having prejudices over certain groups of people. Existing work primarily focuses on proposing fairness metrics and presenting fairness improvement approaches. It remains unclear how key aspect of any machine learning system, such as feature set and training data, affect fairness. This paper presents results from a comprehensive study that addresses this problem. We find that extending the feature set plays a significant role in improving fairness (with an average increase rate of 38%). Importantly, and contrary to widely-held beliefs that greater fairness often corresponds to lower accuracy, our findings reveal that extending the feature set improves both accuracy and fairness. Perhaps also surprisingly, we find that extending training data does not improve fairness. Our results suggest such data enhancement can further reduce fairness when feature sets are insufficient; an important cautionary finding for practising software engineers.
DOI: 10.6084/m9.figshare.10062068
title: Mapping Shape to Visuomotor Mapping: Learning and Generalisation of Sensorimotor Behaviour Based on Contextual Information.

description: This contains the Matlab files for the model explained in <br>van Dam LCJ & Ernst MO (2015) Mapping Shape to Visuomotor Mapping: Learning and Generalisation of Sensorimotor Behaviour Based on Contextual Information. PLOS Computational Biology 11(3): e1004172. https://doi.org/10.1371/journal.pcbi.1004172<br>
DOI: 10.6084/m9.figshare.10062068.v2
title: Mapping Shape to Visuomotor Mapping: Learning and Generalisation of Sensorimotor Behaviour Based on Contextual Information.

description: This contains the Matlab files for the model explained in <br>van Dam LCJ & Ernst MO (2015) Mapping Shape to Visuomotor Mapping: Learning and Generalisation of Sensorimotor Behaviour Based on Contextual Information. PLOS Computational Biology 11(3): e1004172. https://doi.org/10.1371/journal.pcbi.1004172<br>
DOI: 10.6084/m9.figshare.10062068.v1
title: Mapping Shape to Visuomotor Mapping: Learning and Generalisation of Sensorimotor Behaviour Based on Contextual Information.

description: This data is the raw data to the following paper:van Dam LCJ & Ernst MO (2015) Mapping Shape to Visuomotor Mapping: Learning and Generalisation of Sensorimotor Behaviour Based on Contextual Information. PLOS Computational Biology 11(3): e1004172. https://doi.org/10.1371/journal.pcbi.1004172<br>Matlab files for the Mathematical model are included as well.<br>
DOI: 10.6084/m9.figshare.13789354.v1
title: Dataset of geochemistry, P-T data and Re-Os isotopic compositions of peridotites from Arctic Canada

description: The dataset provides the whole-rock and mineral major and trace contents for peridotites from Arctic Canada as well as their whole-rock Re-Os isotopic compositions and P-T data.
DOI: 10.6084/m9.figshare.13994858
title: Interdisciplinary research integration survey 2021

description: Dataset from the evaluation survey on pilot convergence framework in Biofilm and computational engineering. The evaluation consisted of an 8 question survey (Q1:Who are you? - Q2:What is your primary discipline/background? - Q3:Select all disciplines involved in your current research project. - Q4:How long have you being involved with different labs (disciplines) to solve your research problem? - Q5:Rate how working with different labs (disciplines) help you in your research project and/or your career development so far ("0=not at all" AND "5=Very helpful"). - Q6:How confident do you feel recommending this experience to another student(1=not confident at all, 5=Highly confident)? - Q7: - Q8:How do you think your experience can be improved?). The survey was completed by all 19 participants. Out of 18 participants, 17 find the framework helpful (94.4% rate at 4 or 5 on a scale of 5) toward their research and career development. The same number feel confident recommending the framework to other students. (Google Form: https://docs.google.com/forms/d/e/1FAIpQLScVeYbRkV6csgM8hLmerYfiW8OxEhY-TYU_innUewi7WCfDEQ/viewform?usp=sf_link)