cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13204229
title: Scenario-Based Robust Remanufacturing Scheduling Problem Using Improved Biogeography-Based Optimization Algorithm

description: This is the experimental data of the manuscript entitled “Scenario-Based Robust Remanufacturing Scheduling Problem Using Improved Biogeography-Based Optimization Algorithm ”. All data are generated through computer simulation and saved in the file with csv format. The first folder "1.SRRSP benchmark problem instances" includes 21SRRSP benchmark problem instances, the second folder "2.Additional data used to verify the practicability of considering multiple scenarios" contains the addition data required to demonstrate the practicability of considering multiple scenarios, and the third folder "3.Additional data used to verify the practicability of variable start-up batch size constraint" contains the the addition data required to demonstrate the practicability of variable start-up batch size constraint. These datasets show the number of scenarios, the number of remanufacturing jobs, the number of operations for each job, the number of remanufacturing machines, the maximum batch size of batch processing machines, and the minimum batch size of batch processing machines. The value of variance factor, the probability of the occurrence of each scenario, the index of the batch processing machine, the index of the batch processing operation, the arrival time of each remanufacturing job, the set of machines available for each operation, and the processing time of each operation on the available machines are also shown in the datasets.
DOI: 10.6084/m9.figshare.13204229.v1
title: Scenario-Based Robust Remanufacturing Scheduling Problem Using Improved Biogeography-Based Optimization Algorithm

description: This is the experimental data of the manuscript entitled “Scenario-Based Robust Remanufacturing Scheduling Problem Using Improved Biogeography-Based Optimization Algorithm ”. All data are generated through computer simulation and saved in the file with csv format. The first folder "1.SRRSP benchmark problem instances" includes 21SRRSP benchmark problem instances, the second folder "2.Additional data used to verify the practicability of considering multiple scenarios" contains the addition data required to demonstrate the practicability of considering multiple scenarios, and the third folder "3.Additional data used to verify the practicability of variable start-up batch size constraint" contains the the addition data required to demonstrate the practicability of variable start-up batch size constraint. These datasets show the number of scenarios, the number of remanufacturing jobs, the number of operations for each job, the number of remanufacturing machines, the maximum batch size of batch processing machines, and the minimum batch size of batch processing machines. The value of variance factor, the probability of the occurrence of each scenario, the index of the batch processing machine, the index of the batch processing operation, the arrival time of each remanufacturing job, the set of machines available for each operation, and the processing time of each operation on the available machines are also shown in the datasets.
DOI: 10.21227/gan1-8k64
title: test1

description: test
DOI: 10.21227/mg1p-0009
title: v1

description: This work uses extensive feature engineering to extract useful information about the behaviour of energy consumers. These features are then used to train a random forest model to forecast unknown monthly consumption.
DOI: 10.21227/apa2-qg50
title: The Impact of Height on Indoor Positioning with Magnetic Fields

description: Steel studs, HVAC systems, rebar, and many other building components produce spatially varying magnetic fields. Magnetometers can measure these fields and can be used in combination with inertial sensors for indoor positioning of robots and of handheld devices like smartphones. Current methods of localization and mapping with magnetometers are often based on the simplifying assumption that magnetic fields do not vary with height. In this paper, through analysis of a large dataset collected across three buildings on the University of Illinois campus, we quantify the extent to which this "planar assumption" is likely to be violated and examine the consequences for indoor positioning. First, we show that out-of-plane variations in the magnetic field were significant at over half the locations where magnetometer measurements were taken. Second, we show that absolute trajectory error in positioning was low when both localization and mapping were based on magnetometer measurements taken at the same height, but that error increased significantly with even small differences between these heights. Third, we show that the choice of height at which to take measurements---if this height was kept the same for both localization and mapping---had no significant impact on absolute trajectory error when averaged across a given set of trajectories, although some trajectories existed for which different measurement heights led to significantly different errors. Fourth, we show that absolute trajectory error decreased when magnetometer measurements were aggregated across a small range of heights to produce a single, planar map and when measurements at the median height were used for localization.
DOI: 10.21227/s7jc-5m65
title: new entry

description: test leaderboard
DOI: 10.6084/m9.figshare.13204268
title: AllStages_TrophoblastNuclei_obj.Rdata

description: The R object for the processed single nuclei RNA-seq data from "Single nuclei RNA-seq of the mouse placental labyrinth" containing the merged datasets from only the trophoblast nuclei from E9.5, E10.5, E12.5, E14.5 shown in Figure 2-6.
DOI: 10.6084/m9.figshare.13204265
title: AllStages_AllNuclei_obj.Rdata

description: The R object for the processed single nuclei RNA-seq data from "Single nuclei RNA-seq of the mouse placental labyrinth" containing the merged datasets from E9.5, E10.5, E12.5, E14.5 shown in Figure 1.<br>
DOI: 10.25345/c5rb7f
title: MassIVE MSV000086426 - A Comprehensive Atlas of Protein Turnover Rates in Murine Tissues
title: Data for protein turnover rates in mammalian tissues
DOI: 10.34740/kaggle/dsv/1620633
title: snack breed classification
DOI: 10.6084/m9.figshare.13107155
title: Conversion from road location descriptions to spatial locations through deep learning-based semantic matching and simplified knowledge representation-based qualitative direction reasoning

description: The data and codes that support the findings of the study. Configure of experimental environment and instructions for use are described in readme.docx.
DOI: 10.6084/m9.figshare.13107155.v2
title: Conversion from road location descriptions to spatial locations through deep learning-based semantic matching and simplified knowledge representation-based qualitative direction reasoning

description: The data and codes that support the findings of the study. Configure of experimental environment and instructions for use are described in readme.docx.
DOI: 10.6084/m9.figshare.13107155.v1
title: Conversion from road location descriptions to spatial locations through deep learning-based semantic matching and simplified knowledge representation-based qualitative direction reasoning

description: The data and codes that support the findings of the study.
DOI: 10.21227/prsw-b697
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains IDs and sentiment scores of the geo-tagged tweets related to the COVID-19 pandemic. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location.The paper associated with this dataset is available here: Design and analysis of a large-scale COVID-19 tweets dataset-------------------------------------Related: Coronavirus (COVID-19) Tweets Sentiment Trend (Global), Coronavirus (COVID-19) Tweets Dataset and Tweets Originating from India During COVID-19 Lockdowns 1, 2, 3, 4-------------------------------------Below is the quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 258,049 tweets— Coverage : Global— Language : English (EN)— Dataset usage terms : By using this dataset, you agree to (i) use the content of this dataset and the data generated from the content of this dataset for non-commercial research only, (ii) remain in compliance with Twitter's Developer Policy and (iii) cite the following paper:Lamsal, R. Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence (2020). https://doi.org/10.1007/s10489-020-02029-z— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Active keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for details regarding the collection date and time (and other notes) of each CSV file present in this dataset.
DOI: 10.21227/fcgg-9193
title: Tweets Originating from India During COVID-19 Lockdowns 1, 2, 3, 4

description: This India-specific COVID-19 tweets dataset has been developed using the large-scale Coronavirus (COVID-19) Tweets Dataset, which currently contains more than 600 million COVID-19 specific English language tweets. This dataset contains tweets originating from India during the first week of each four phases of nationwide lockdowns initiated by the Government of India. For more information on filtering keywords, please visit the primary dataset page.— Dataset usage terms : By using this dataset, you agree to (i) use the content of this dataset and the data generated from the content of this dataset for non-commercial research only, (ii) remain in compliance with Twitter's Developer Policy and (iii) cite the following paper:Lamsal, R. Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence (2020). https://doi.org/10.1007/s10489-020-02029-z-------------------------------------Related: Coronavirus (COVID-19) Tweets Dataset, Coronavirus (COVID-19) Geo-tagged Tweets Dataset and Coronavirus (COVID-19) Tweets Sentiment Trend (Global)-------------------------------------What's inside the dataset? The files in the dataset contain IDs of the tweets present in the Coronavirus (COVID-19) Tweets Dataset. Note: Below, (all files) means that all the files mentioned and in-between have been considered to develop the ID file, while (only even-numbered files) suggests that only the even-numbered files have been considered.Lockdown period tweets: (all files)Lockdown1.zip: March 25, 2020 - April 02, 2020; corona_tweets_08.csv to corona_tweets_14.csvLockdown2.zip: April 14, 2020 - April 21, 2020; corona_tweets_27.csv to corona_tweets_33.csvLockdown3.zip: May 01, 2020 - May 07, 2020; corona_tweets_44.csv to corona_tweets_49.csvLockdown4.zip: May 18, 2020 - May 23, 2020; corona_tweets_61.csv to corona_tweets_66.csvExtras: (all files)extras_june1_june7.zip: corona_tweets_75.csv to corona_tweets_80.csvExtras: (only even-numbered files)extras_june24_july1.zip: corona_tweets_96.csv to corona_tweets_104.csvextras_july2_july15.zip: corona_tweets_106.csv to corona_tweets_118.csvextras_july16_august4.zip: corona_tweets_120.csv to corona_tweets_138.csvextras_august5_august18.zip: corona_tweets_140.csv to corona_tweets_152.csvextras_august19_september1.zip: corona_tweets_154.csv to corona_tweets_166.csvextras_september2_september15.zip: corona_tweets_168.csv to corona_tweets_180.csv
DOI: 10.6084/m9.figshare.13204025
title: R0186-DLC_output

description: deeplabcut output and processed 3D reconstructions of data for rat 0186."_direct" zip files contain .csv's from the direct view and .mat files with cropping regions"_right/left" zip files contain .csv's from the mirror view and .mat files with cropping regions"_processed" files contain 3D reconstructions and reach analyses
DOI: 10.6084/m9.figshare.13204034
title: R0190-DLC_output

description: deeplabcut output and processed 3D reconstructions of data for rat 0190."_direct" zip files contain .csv's from the direct view and .mat files with cropping regions"_right/left" zip files contain .csv's from the mirror view and .mat files with cropping regions"_processed" files contain 3D reconstructions and reach analyses
DOI: 10.6084/m9.figshare.13204034.v1
title: R0190-DLC_output

description: deeplabcut output and processed 3D reconstructions of data for rat 0190."_direct" zip files contain .csv's from the direct view and .mat files with cropping regions"_right/left" zip files contain .csv's from the mirror view and .mat files with cropping regions"_processed" files contain 3D reconstructions and reach analyses
DOI: 10.34740/kaggle/dsv/1627762
title: NNK-Dataset
DOI: 10.13140/rg.2.2.29501.28649
title: still life series - door beads and floor boards - 2020.11.07 - No.-2
DOI: 10.13140/rg.2.2.22790.40002
title: still life series - door beads and floor boards - 2020.11.07 - No.-2
DOI: 10.6084/m9.figshare.13199555
title: Bathymetry Data and Tsunami Records of the 2016 Fukushima Earthquake

description: 1) Bathymetry Files:bathy.SD01.grd Bathymetry file of the whole research region (0.3 arc min)bathy.SD0X02.grd 0.1 arc min (6 arc sec)bathy.SD0X03.grd 2 arc secbathy.SD0X04.grd 0.667 arc secX=1: Miyako X=2: Kamaishi X=3: Ofunato X=4: Ayukawa<br>The bathymetry data used in this work come from General Bathymetric Chart of the Ocean released in 2014 (GEBCO_2014) and the M7000 Digital Bathymetric Chart from the Japan Hydrographic Association (JHA; https://www.jha.or.jp/shop/). <br><br><br>2) Tsunami Records at OBPGs of S-net:obs01.dat - obs28.datThe S-net records are obtained from the National Research Institute for Earth Science and Disaster (NIED; https://www.seafloor.bosai.go.jp/S-net/).<br><br><br>3) Tsunami Records at Tide Gauges:Miyako, Kamaishi, Ofunato and AyukawaThe tide gauge records are obtained from the Intergovernmental Oceanographic Commission (IOC; http://www.ioc-sealevelmonitoring.org/), the Japan Oceanographic Data Center (JODC; https://www.jodc.go.jp/jodcweb/aboutData.html) and the Japan Meteorological Agency (JMA; http://www.jma.go.jp/jma/press/1611/22b/kaisetsu201611221100.pdf).<br>
DOI: 10.6084/m9.figshare.13199555.v1
title: Bathymetry Data and Tsunami Records of the 2016 Fukushima Earthquake

description: 1) Bathymetry Files:XXXXX<br>The bathymetry data used in this work come from General Bathymetric Chart of the Ocean released in 2014 (GEBCO_2014) and the M7000 Digital Bathymetric Chart from the Japan Hydrographic Association (JHA; https://www.jha.or.jp/shop/). <br><br><br>2) Tsunami Records at OBPGs of S-net:XXXXXXXXThe S-net records are obtained from the National Research Institute for Earth Science and Disaster (NIED; https://www.seafloor.bosai.go.jp/S-net/).<br><br><br>3) Tsunami Records at Tide Gauges:XXXXXXXXThe tide gauge records are obtained from the Intergovernmental Oceanographic Commission (IOC; http://www.ioc-sealevelmonitoring.org/), the Japan Oceanographic Data Center (JODC; https://www.jodc.go.jp/jodcweb/aboutData.html) and the Japan Meteorological Agency (JMA; http://www.jma.go.jp/jma/press/1611/22b/kaisetsu201611221100.pdf).<br>
DOI: 10.5281/zenodo.4052578
title: Supplementary material for the paper "Paleoenvironment Implications of Layered Ejecta Craters in Chryse Planitia, Mars"

description: The compressed file contains the spatial crater count (scc) files for crater dating, results of randomness analysis tests and derived model ages with different chronology systems. Table S1 lists all the identified layered ejecta craters in the Chryse Planitia, with a 5-m-per-pixel high resolution context camera (CTX) mosaic as a base map. Table S2 lists all the dating results for selected layered ejecta craters in the Chryse Planitia.
DOI: 10.5281/zenodo.4259449
title: Supplementary material for the paper "Paleoenvironment Implications of Layered Ejecta Craters in Chryse Planitia, Mars"

description: The compressed file contains the spatial crater count (scc) files for crater dating, results of randomness analysis tests and derived model ages with different chronology systems. Table S1 lists all the identified layered ejecta craters in the Chryse Planitia, with a 5-m-per-pixel high resolution context camera (CTX) mosaic as a base map. Table S2 lists all the dating results for selected layered ejecta craters in the Chryse Planitia.
DOI: 10.5281/zenodo.4052577
title: Supplementary material for the paper "Paleoenvironment Implications of Layered Ejecta Craters in Chryse Planitia, Mars"

description: The compressed file contains the spatial crater count (scc) files for crater dating, results of randomness analysis tests and derived model ages with different chronology systems. Table S1 lists all the identified layered ejecta craters in the Chryse Planitia, with a 5-m-per-pixel high resolution context camera (CTX) mosaic as a base map. Table S2 lists all the dating results for selected layered ejecta craters in the Chryse Planitia.
DOI: 10.6084/m9.figshare.13204420
title: Additional file 1 of Interprofessional collaboration associated with frequency of life-saving links to HIV continuum of care services in the urban environment of Newark, New Jersey

description: Additional file 1. Study Surveys.
DOI: 10.6084/m9.figshare.13204418
title: Additional file 2 of Comparative analysis of differentially abundant proteins quantified by LC–MS/MS between flash frozen and laser microdissected OCT-embedded breast tumor samples

description: Additional file 2: Table S1. Percentage of tumor and stroma in each OCT/LMD sample before and after laser microdissection. Table S2. Number of proteins quantified in each sample. Table S3. Differentially expressed proteins between FF/HOM and OCT/LMD-T. Table S4. Differentially expressed proteins between FF/HOM and OCT/LMD-TS. Table S5. Differentially expressed proteins common between FF/HOM vs OCT/LMD-T and FF/HOM vs. OCT/LMD-TS. Table S6. Differentially enriched GO Terms between FF/HOM and OCT/LMD-T. Table S7. Differentially enriched GO Terms between FF/HOM and OCT/LMD-TS. Table S8. Differentially enriched GO Terms common between FF/HOM vs OCT/LMD-T and FF/HOM vs. OCT/LMD-TS. Table S9. Non-significant differentially expressed proteins between OCT/LMD-T and OCT/LMD-TS. Table S10. Non-significant differentially enriched GO Terms between OCT/LMD-T and OCT/LMD-TS. Table S11. Trended differentially expressed proteins between OCT/LMD-T and OCT/LMD-TS using paired analysis. Table S12. Differentially enriched GO Terms between OCT/LMD-T and OCT/LMD-TS using paired analysis. Table S13. Select stroma-associated GO terms from Table S12 and the proteins from Table S11 that map to them.
DOI: 10.6084/m9.figshare.13204418.v1
title: Additional file 2 of Comparative analysis of differentially abundant proteins quantified by LC–MS/MS between flash frozen and laser microdissected OCT-embedded breast tumor samples

description: Additional file 2: Table S1. Percentage of tumor and stroma in each OCT/LMD sample before and after laser microdissection. Table S2. Number of proteins quantified in each sample. Table S3. Differentially expressed proteins between FF/HOM and OCT/LMD-T. Table S4. Differentially expressed proteins between FF/HOM and OCT/LMD-TS. Table S5. Differentially expressed proteins common between FF/HOM vs OCT/LMD-T and FF/HOM vs. OCT/LMD-TS. Table S6. Differentially enriched GO Terms between FF/HOM and OCT/LMD-T. Table S7. Differentially enriched GO Terms between FF/HOM and OCT/LMD-TS. Table S8. Differentially enriched GO Terms common between FF/HOM vs OCT/LMD-T and FF/HOM vs. OCT/LMD-TS. Table S9. Non-significant differentially expressed proteins between OCT/LMD-T and OCT/LMD-TS. Table S10. Non-significant differentially enriched GO Terms between OCT/LMD-T and OCT/LMD-TS. Table S11. Trended differentially expressed proteins between OCT/LMD-T and OCT/LMD-TS using paired analysis. Table S12. Differentially enriched GO Terms between OCT/LMD-T and OCT/LMD-TS using paired analysis. Table S13. Select stroma-associated GO terms from Table S12 and the proteins from Table S11 that map to them.
DOI: 10.6084/m9.figshare.13203698
title: 201807-201906-Lower Changjiang River.xlsx

description: The Data is about Discharge, Salinity, Temperature, DOC, <i>a</i><sub>254</sub>, <i>S</i><sub>275-295</sub>, SUVA<sub>254</sub>, SPM, The molecular size distributions among the &lt; 1 kDa, 1-3 kDa, 3-10 kDa, and &gt; 10 kDa size-fractions about the lower Changjiang River from July 2018 to June 2019.
DOI: 10.6084/m9.figshare.13203698.v1
title: 201807-201906-Lower Changjiang River.xlsx

description: The Data is about Discharge, Salinity, Temperature, DOC, <i>a</i><sub>254</sub>, <i>S</i><sub>275-295</sub>, SUVA<sub>254</sub>, SPM, The molecular size distributions among the &lt; 1 kDa, 1-3 kDa, 3-10 kDa, and &gt; 10 kDa size-fractions about the lower Changjiang River from July 2018 to June 2019.
DOI: 10.6084/m9.figshare.13204487
title: Additional file 13 of Genomic analysis of circular RNAs in heart

description: Additional file 13. Table S6. CircRNAs with potential to sponge heart disease-related miRNAs. CircRNAs with at least 5 predicted binding sites for at least one miRNA were listed.
DOI: 10.6084/m9.figshare.13204490.v1
title: Additional file 14 of Genomic analysis of circular RNAs in heart

description: Additional file 14. Table S7. Predicted targets of heart disease-related miRNAs. The targeted genes of each miRNA were obtained from TargetScan ( http://www.targetscan.org/vert_72/ ). The significance of changes of targeted genes in DCM compared to normal samples were determined by DESeq2 with threshold of fold change &gt;2 and FDR &lt;0.05. A total of 937 and 480 genes were identified to be significantly up- and down-regulated in DCM, respectively.
DOI: 10.6084/m9.figshare.13204487.v1
title: Additional file 13 of Genomic analysis of circular RNAs in heart

description: Additional file 13. Table S6. CircRNAs with potential to sponge heart disease-related miRNAs. CircRNAs with at least 5 predicted binding sites for at least one miRNA were listed.
DOI: 10.6084/m9.figshare.13204502
title: Additional file 3 of Genomic analysis of circular RNAs in heart

description: Additional file 3. Table S2. Primers used for validation of the mouse ortholog of selected human circRNAs. Mouse Yap1 primers were used as internal control for PCR reaction using genomic DNA.
DOI: 10.6084/m9.figshare.13204499
title: Additional file 2 of Genomic analysis of circular RNAs in heart

description: Additional file 2. Table S1. Information of DCM-related miRNAs used for analysis of sponge potential. The seed sequence of each miRNA was obtained from TargetScan database. PMID is the PubMed number for studies which reported the miRNAs.
DOI: 10.6084/m9.figshare.13204511.v1
title: Additional file 6 of Genomic analysis of circular RNAs in heart

description: Additional file 6. Table S4. List of circRNAs originated from two different genes in 10 human heart samples. Chromosome coordinates are in hg38 annotation. In the last column, “yes” indicates that the circRNA is from two neighboring genes with highly similar sequence, and “no” indicates no sequence similarity was observed and the circRNA is likely a bona fide rt-circRNA.
DOI: 10.6084/m9.figshare.13204478
title: Additional file 10 of Genomic analysis of circular RNAs in heart

description: Additional file 10. Table S5. List of high-confidence and significantly dysregulated circRNAs in DCM compared to normal heart samples. Chromosome coordinates are in hg38 annotation. The threshold for significance was p &lt;0.05 and fold change &gt; 2.
DOI: 10.6084/m9.figshare.13204499.v1
title: Additional file 2 of Genomic analysis of circular RNAs in heart

description: Additional file 2. Table S1. Information of DCM-related miRNAs used for analysis of sponge potential. The seed sequence of each miRNA was obtained from TargetScan database. PMID is the PubMed number for studies which reported the miRNAs.