cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14737218
title: Additional file 4 of Long non-coding RNA exploration for mesenchymal stem cell characterisation

description: Additional file 4 All information needed about selected Mlincs (differentially expressed and selected by Boruta feature selection).
DOI: 10.6084/m9.figshare.14737217
title: Additional file 22 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional File 22: File S7. Ontologizer results on clusters identified by MCL with &gt;= 3 proteins in the HR+/TN expression-weighted BioGRID network
DOI: 10.6084/m9.figshare.14737214
title: Additional file 21 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional File 21: File S6. Ontologizer results on clusters identified by MCL with &gt;= 3 proteins in the HER2+/TN expression-weighted BioGRID network
DOI: 10.6084/m9.figshare.14737212
title: Additional file 2 of Long non-coding RNA exploration for mesenchymal stem cell characterisation

description: Additional file 2 List of differentially expressed annotated genes between “MSC” and “non MSC” groups.
DOI: 10.6084/m9.figshare.14737211
title: Additional file 20 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional File 20: File S5. NEA results on Tyanova et al.’s dataset using the BioGRID network
DOI: 10.6084/m9.figshare.14737209
title: Additional file 1 of Long non-coding RNA exploration for mesenchymal stem cell characterisation

description: Additional file 1 Details and metadata of RNAseq dataset(s) downloaded for unannotated lncRNA prediction and differential expression between “MSC” and “non-MSC” groups.
DOI: 10.6084/m9.figshare.14737206
title: Additional file 18 of Long non-coding RNA exploration for mesenchymal stem cell characterisation

description: Additional file 18 Details and metadata of RNAseq dataset downloaded for k-mer based quantification of candidates in MSCs in different biological situations.
DOI: 10.6084/m9.figshare.14737205
title: Additional file 19 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional File 19: File S4. GSEA results on Tyanova et al.’s dataset
DOI: 10.6084/m9.figshare.14737202
title: Additional file 18 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional File 18: File S3. Ontologizer results of significantly dysregulated proteins with loosened-up cut-offs from t-test results in Tyanova et al.’s dataset
DOI: 10.6084/m9.figshare.14737200
title: Additional file 16 of Long non-coding RNA exploration for mesenchymal stem cell characterisation

description: Additional file 16 Single-cell RNAseq data of Adipose MSCs by X. Liu et al, used for co-expression research at the cell level.
DOI: 10.6084/m9.figshare.14737199
title: Additional file 17 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional File 17: File S2. Ontologizer results of significantly dysregulated proteins from t-test results in Tyanova et al.’s dataset
DOI: 10.6084/m9.figshare.14737197
title: Additional file 15 of Long non-coding RNA exploration for mesenchymal stem cell characterisation

description: Additional file 15 Details and metadata of FANTOM6 dataset used for functional prediction.
DOI: 10.6084/m9.figshare.14737196
title: Additional file 16 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional File 16: File S1. t-test results on protein expression values from Tyanova et al.’s dataset
DOI: 10.6084/m9.figshare.14737191
title: Additional file 14 of Long non-coding RNA exploration for mesenchymal stem cell characterisation

description: Additional file 14 Potential interactions between selected Mlincs and annotated proteins, found by LncADeep.
DOI: 10.6084/m9.figshare.14737184
title: Additional file 12 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional File 12: Table S10. PIGNON results on unweighted STRING network with weighted sampling
DOI: 10.6084/m9.figshare.14737181
title: Additional file 11 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional file 11: Table S9. PIGNON results on HR+/TN expression-weighted STRING network with weighted sampling
DOI: 10.6084/m9.figshare.14737178
title: Additional file 10 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional file 10: Table S8. PIGNON results on HER2+/HR+ expression-weighted STRING network with weighted sampling
DOI: 10.6084/m9.figshare.14737110
title: Additional file 4 of Low awareness and common misconceptions about schistosomiasis in endemic lowland areas in Western Ethiopia: a mixed-methods study

description: Additional file 4: Supplementary file 4 Data.xlsx
DOI: 10.13139/olcf/1783186
title: SARS-CoV2 Docking Dataset

description: Description: Small-molecule conformations and docking scores for 1.4 billion molecules docked against 6 protein targets from SARS-CoV2: MPro 5R84, MPro 6WQF, NSP15 6WLC, PLPro 7JIR, Spike 6M0J, and a hand-optimized model of the RNA-dependent RNA polymerase. Docking was carried out using the Autodock-GPU program performing 20 independent structure minimizations per dock - saving 3 results per molecule. Scores reported include the Autodock free energy estimate as well as RF3 and VS-DUD-E v2 machine-learned rescoring models. Protein structure files and maps in the format input to Autodock-GPU are included. Literature Ref: Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19, J. Chem. Inf. Model. 2020, 60(12): 5832â5852.
DOI: 10.6084/m9.figshare.14737091
title: Additional file 1 of Long-term results of topical 0.02% tacrolimus ointment for refractory ocular surface inflammation in pediatric patients

description: Additional file 1. Raw data from pediatric patients using topical 0.02% tacrolimus ointment for the treatment of refractory ocular surface inflammation.
DOI: 10.6084/m9.figshare.14737086
title: Additional file 1 of A copper alloy light cannon from Grodno: an example of early firearms from Eastern Europe

description: Additional file 1. Chemistry of late medieval and modern period copper alloy European firearms.
DOI: 10.6084/m9.figshare.14737062
title: Additional file 4 of Effect of herbal extracts on peripheral nerve regeneration after microsurgery of the sciatic nerve in rats

description: Additional file 4: Figure S3. The liquid chromatography-mass spectrometry (LCMS) chromatograph of Aconitum carmichaelii Debeaux extracts.
DOI: 10.6084/m9.figshare.14737059
title: Additional file 3 of Effect of herbal extracts on peripheral nerve regeneration after microsurgery of the sciatic nerve in rats

description: Additional file 3: Figure S2. The liquid chromatography-mass spectrometry (LCMS) chromatograph of Coptis japonica (Thunb.) Makino extracts.
DOI: 10.5439/1785488
title: Ozone and aerosol size distribution measurements at the Oliktok Point, AK AMF3

description: Aerosol size distribution measurements at the Oliktok Point, AK AMF3 as measured by a TSI model 3082 scanning mobility particle sizer (SMPS), and ozone measurements at the Oliktok Point, AK AMF3 as measured by a 2B model 205 ozone monitor. 2B specifies a limit of detection (2σ) of 2 ppb and these data were calibrated using a 2B model 306 ozone calibration source. Users of the data are asked to contact PI Pratt prior to use for consultation, discussion of co-authorship, and to ensure that the most up-to-date information is provided.
DOI: 10.6084/m9.figshare.14736996
title: Additional file 7 of Tsunami evacuation times and routes to safe zones: a GIS-based approach to tsunami evacuation planning on the island of Stromboli, Italy

description: Additional file 7: Appendix G. Evaluation of the possibility of evacuating in less than 4 min in Piscità (a) Evacuation of all the centroids in the Piscità zone. (b) Specific evacuation of the centroids to the RAEP1. (c) Individual evacuation of 101 centroid to the RAEP1.
DOI: 10.6084/m9.figshare.14736954
title: Additional file 2 of Germline nuclear-predominant Pten murine model exhibits impaired social and perseverative behavior, microglial activation, and increased oxytocinergic activity

description: Additional file 2. contains the raw differential expression data from transcriptomic surgery of P40 Y68H cortex.
DOI: 10.6084/m9.figshare.14736924
title: Additional file 2 of Annotation of snoRNA abundance across human tissues reveals complex snoRNA-host gene relationships

description: Additional file 2. Supplementary table S5 describes the abundance across tissues and characteristics of all expressed snoRNAs and their host gene.
DOI: 10.34740/kaggle/dsv/2304311
title: Reviews of Universal Studios
DOI: 10.5281/zenodo.4902289
title: NaturalProofs Dataset

description: The NaturalProofs Dataset is a multi-domain corpus for studying mathematical reasoning in natural language. NaturalProofs consists of roughly 30,000 theorem statements and proofs, 15,000 definitions, and 2,000 additional pages (e.g. axioms, corollaries), written in natural mathematical language. NaturalProofs contains broad-coverage data from ProofWiki, deep-coverage data from the Stacks project, and low-resource data from mathematics textbooks. NaturalProofs unifies these sources in a common schema and is made publicly available as a resource to drive progress on tasks involving informal mathematics. Please see the description in [Welleck et al 2021], and its corresponding GitHub repository for more details.
DOI: 10.5281/zenodo.4632538
title: NaturalProofs Dataset

description: The NaturalProofs Dataset is a multi-domain corpus for studying mathematical reasoning in natural language. NaturalProofs consists of roughly 30,000 theorem statements and proofs, 15,000 definitions, and 2,000 additional pages (e.g. axioms, corollaries), written in natural mathematical language. NaturalProofs contains broad-coverage data from ProofWiki, deep-coverage data from the Stacks project, and low-resource data from mathematics textbooks. NaturalProofs unifies these sources in a common schema and is made publicly available as a resource to drive progress on tasks involving informal mathematics. Please see the description in [Welleck et al 2021], and its corresponding GitHub repository for more details.
DOI: 10.5281/zenodo.4737033
title: Confocal and micro-CT data of Paraodontomma leptocristatum, holotype, NIGP174676

description: This dataset contains the raw confocal laser scanning microscopy (CLSM) slices and X-ray microtomography (micro-CT) slices for the holotype of <em>Paraodontomma leptocristatum</em> (NIGP174676) from mid-Cretaceous Burmese amber. Additional confocal data for <em>Paraodontomma szwedoi</em> (NIGP169926) are also provided. This dataset is associated with the publication "New species of <em>Paraodontomma </em>from mid-Cretaceous Burmese amber with muscle tissue preservation (Coleoptera: Archostemata: Ommatidae)" (DOI:10.11606/1807-0205/2021.61.53). Confocal images were obtained with a Zeiss LSM710 confocal laser scanning microscope, using 488 nm Argon laser excitation line. Both original CZI files and exported TIFF stacks are provided. Micro-CT data were obtained with a Zeiss Xradia 520 Versa 3D X-ray microscope. Scanning parameters were as follows: isotropic voxel size, 9.4355 μm; power, 4 W; acceleration voltage, 50 kV; exposure time, 1.5 s; projections, 2201. The TIFF stack is provided.
DOI: 10.5281/zenodo.4737032
title: Confocal and micro-CT data of Paraodontomma leptocristatum, holotype, NIGP174676

description: This dataset contains the raw confocal laser scanning microscopy (CLSM) slices and X-ray microtomography (micro-CT) slices for the holotype of <em>Paraodontomma leptocristatum</em> (NIGP174676) from mid-Cretaceous Burmese amber. Additional confocal data for <em>Paraodontomma szwedoi</em> (NIGP169926) are also provided. This dataset is associated with the publication "New species of <em>Paraodontomma </em>from mid-Cretaceous Burmese amber with muscle tissue preservation (Coleoptera: Archostemata: Ommatidae)" (DOI:10.11606/1807-0205/2021.61.53). Confocal images were obtained with a Zeiss LSM710 confocal laser scanning microscope, using 488 nm Argon laser excitation line. Both original CZI files and exported TIFF stacks are provided. Micro-CT data were obtained with a Zeiss Xradia 520 Versa 3D X-ray microscope. Scanning parameters were as follows: isotropic voxel size, 9.4355 μm; power, 4 W; acceleration voltage, 50 kV; exposure time, 1.5 s; projections, 2201. The TIFF stack is provided.
DOI: 10.17632/gz4wd6gdr5.1
title: Data for: Nonlinear based Chaotic Harris Hawks Optimizer: Algorithm and Internet of Vehicles Application

description: To alleviate the drawbacks of this algorithm, a modified version called Nonlinear based Chaotic Harris Hawks Optimization (NCHHO) is proposed in this paper. NCHHO uses chaotic and nonlinear control parameters to improve HHO’s optimization performance. The main goal of using the chaotic maps in the proposed method is to improve the exploratory behavior of HHO. In addition, this paper introduces a nonlinear control parameter to adjust HHO’s exploratory and exploitative behaviours. The performance of NCHHO’s is developed using a variety of chaotic maps to identify the most effective one and tested on several well-known benchmark functions. The results demonstrate the NCHHO algorithm provides very competitive, often superior, results compared to the other algorithms. The paper also considers solving an Internet of Vehicles optimization problem that showcases the applicability of NCHHO.
DOI: 10.6084/m9.figshare.14737347.v1
title: FRET_measurement

description: Measurement of FRET ratio from MDA-MB-231 cells stably transfected with either glucose (FLII12Pglu-700uDelta6) or lactate (Laconic) reporter.<br>fretx_0.csv: confocal microscope image with no glucose; before addition of glucose to x mM (x = 0.5, 1, 2, 5, 8, 10, 15)fretx_x.csv: confocal microscope image after addition of glucose to x mM (x = 0.5, 1, 2, 5, 8, 10, 15)<br><br>calibration_xxx.csv: normalized FERT ratio of glucose/lactate in protein extraction(vitro)/intact cell(vivo) in response to glucose/lactate concentration; first column: concentration<br>longitudinal_xxx.csv: longitudinal measurement of normalized FRET ratio with different initial glucose/lactate concentrations;first column: concentration; first row: time<br>lac_colc2.csv: FRET response of lactate reporter to therapy with CoCl2xxx_CytochalasinB: FRET response of glucose/lactate reporter to therapy with Cytochalasin Bxxx_Phloretin: FRET response of glucose/lactate reporter to therapy with Phloretin<br>
DOI: 10.6084/m9.figshare.14737347
title: FRET_measurement

description: Measurement of FRET ratio from MDA-MB-231 cells stably transfected with either glucose (FLII12Pglu-700uDelta6) or lactate (Laconic) reporter.<br>fretx_0.csv: confocal microscope image with no glucose; before addition of glucose to x mM (x = 0.5, 1, 2, 5, 8, 10, 15)fretx_x.csv: confocal microscope image after addition of glucose to x mM (x = 0.5, 1, 2, 5, 8, 10, 15)<br><br>calibration_xxx.csv: normalized FERT ratio of glucose/lactate in protein extraction(vitro)/intact cell(vivo) in response to glucose/lactate concentration; first column: concentration<br>longitudinal_xxx.csv: longitudinal measurement of normalized FRET ratio with different initial glucose/lactate concentrations;first column: concentration; first row: time<br>lac_colc2.csv: FRET response of lactate reporter to therapy with CoCl2xxx_CytochalasinB: FRET response of glucose/lactate reporter to therapy with Cytochalasin Bxxx_Phloretin: FRET response of glucose/lactate reporter to therapy with Phloretin<br>
DOI: 10.6084/m9.figshare.14731323
title: SeasonDepth: Cross-Season Monocular Depth Prediction Dataset

description: <pre>Changing environments poses great challenge on the outdoor visual perception and scene understanding for robust long-term autonomous driving and mobile robots, where depth-auxiliary geometric information plays an essential role to the robustness under challenging scenes. Although monocular depth prediction has been well studied recently, there are few work focused on the depth prediction across multiple environmental conditions, e.g. changing illumination and seasons, owing to the lack of such real-world dataset and benchmark. In this work, a new cross-season scaleless monocular depth prediction dataset SeasonDepth (Available on https://seasondepth.github.io) is derived from CMU Visual Localization dataset through structure from motion. To benchmark the depth estimation performance under different environments, we investigate representative and recent state-of-the-art open-source supervised, self-supervised and domain adaptation depth prediction methods from KITTI benchmark using several newly-formulated metrics. Through extensive experimental evaluation on the proposed dataset without fine-tuning, the influence of multiple environments on mean and variance of performance is analyzed, showing that the long-term monocular depth prediction is far from solved. We further give promising solutions especially with self-supervised stereo geometry and multi-task training to enhance the robustness to changing environments. </pre>
DOI: 10.6084/m9.figshare.14731323.v2
title: SeasonDepth: Cross-Season Monocular Depth Prediction Dataset

description: <pre>Changing environments poses great challenge on the outdoor visual perception and scene understanding for robust long-term autonomous driving and mobile robots, where depth-auxiliary geometric information plays an essential role to the robustness under challenging scenes. Although monocular depth prediction has been well studied recently, there are few work focused on the depth prediction across multiple environmental conditions, e.g. changing illumination and seasons, owing to the lack of such real-world dataset and benchmark. In this work, a new cross-season scaleless monocular depth prediction dataset SeasonDepth (Available on https://seasondepth.github.io) is derived from CMU Visual Localization dataset through structure from motion. To benchmark the depth estimation performance under different environments, we investigate representative and recent state-of-the-art open-source supervised, self-supervised and domain adaptation depth prediction methods from KITTI benchmark using several newly-formulated metrics. Through extensive experimental evaluation on the proposed dataset without fine-tuning, the influence of multiple environments on mean and variance of performance is analyzed, showing that the long-term monocular depth prediction is far from solved. We further give promising solutions especially with self-supervised stereo geometry and multi-task training to enhance the robustness to changing environments. </pre>
DOI: 10.6084/m9.figshare.14731323.v1
title: SeasonDepth: Cross-Season Monocular Depth Prediction Dataset

description: <pre>Changing environments poses great challenge on the outdoor visual perception and scene understanding for robust long-term autonomous driving and mobile robots, where depth-auxiliary geometric information plays an essential role to the robustness under challenging scenes. Although monocular depth prediction has been well studied recently, there are few work focused on the depth prediction across multiple environmental conditions, e.g. changing illumination and seasons, owing to the lack of such real-world dataset and benchmark. In this work, a new cross-season scaleless monocular depth prediction dataset SeasonDepth (Available on https://seasondepth.github.io) is derived from CMU Visual Localization dataset through structure from motion. To benchmark the depth estimation performance under different environments, we investigate representative and recent state-of-the-art open-source supervised, self-supervised and domain adaptation depth prediction methods from KITTI benchmark using several newly-formulated metrics. Through extensive experimental evaluation on the proposed dataset without fine-tuning, the influence of multiple environments on mean and variance of performance is analyzed, showing that the long-term monocular depth prediction is far from solved. We further give promising solutions especially with self-supervised stereo geometry and multi-task training to enhance the robustness to changing environments. </pre>
DOI: 10.5281/zenodo.4902820
title: Proceedings of The International Research Education & Training Center

description: JOURNAL OF SOCIAL RESEARCH & BEHAVIORAL SCIENCES
DOI: 10.25919/54g1-0j25
title: Parkes observations for project PX500 semester 2021APRS_05

description: This proposal relates to time purchased by the Chinese Academy of Sciences for Parkes follow-up confirmation and timing of FAST discoveries. As the observing time has been purchased this proposal (and the related PX501) does not require TAC grading. The primary reason for submitting a proposal is to ensure an outreach statement and that the proposal is entered into OPAL for the data archive to access. The Chinese team have two time requests. The time scheduled for the first, relating to this proposal, will be allocated in blocks of 24 hours and the data will become available in the data archive with an 18 month embargo period. The second type of time will be scheduled in shorter blocks and the data archived with a 10 year embargo.
DOI: 10.5281/zenodo.4902831
title: Proceedings of The International Research Education & Training Center

description: JOURNAL OF SOCIAL RESEARCH & BEHAVIORAL SCIENCES
DOI: 10.5281/zenodo.4405134
title: Proceedings of The International Research Education & Training Center

description: JOURNAL OF SOCIAL RESEARCH & BEHAVIORAL SCIENCES
DOI: 10.15151/esrf-es-429809945
title: A comparison study between cement materials and their natural analogous by ex-situ and in-situ high-temperature Synchrotron X-ray Powder Dif

description: The experiment aims want to compare the combustion pyrometamorphism of limestone, which represents the natural analogous of cement production, and cement materials, by means of ex-situ and in-situ high-temperature Synchrotron X-Ray Powder Diffraction. Combustion pyrometamorphism is a poorly understood geological process involving sedimentary rocks in a first stage of high-temperature combustion , followed by a late hydration stage, leading the formation of natural analogous of anhydrous and hydrated cement, respectively. The main focus of the experiment is to get accurate data on (i) mineral assemblages, (ii) crystallographic parameters (cell parameters, coherent domain size and microstrain), (iii) chemical reactions and (iv) phase transitions during heating of limestone. The results will be useful for geology, leading a deeper understanding on combustion pyrometamorphism, and cement manufacture, trying to discover new cement type for reducing the environmental impact.