cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4397078
title: Wikidata Dump All Humans with Gender Info

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.5281/zenodo.4397077
title: Wikidata Dump All Humans with Gender Info

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.5281/zenodo.4397080
title: Wikidata Dump All Humans with Gender Info

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.5281/zenodo.4397079
title: Wikidata Dump All Humans with Gender Info

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.6084/m9.figshare.13514755
title: RAW DATA for Divergent effects of drought and nitrogen deposition on microbial and arthropod soil communities in a Mediterranean forest

description: Raw data supporting the results of the research article entitled "Divergent effects of drought and nitrogen deposition on microbial and arthropod soil communities in a Mediterranean forest", to be published at the European Journal of Soil Biology (Peguero et al. 2021 IN PRESS). Please contact the corresponding author for any clarification or for the code used for the data analyses. <br>
DOI: 10.6084/m9.figshare.13514755.v1
title: RAW DATA for Divergent effects of drought and nitrogen deposition on microbial and arthropod soil communities in a Mediterranean forest

description: Raw data supporting the results of the research article entitled "Divergent effects of drought and nitrogen deposition on microbial and arthropod soil communities in a Mediterranean forest", to be published at the European Journal of Soil Biology (Peguero et al. 2021 IN PRESS). Please contact the corresponding author for any clarification or for the code used for the data analyses. <br>
DOI: 10.25919/mz1r-8975
title: Parkes observations for project P1088 semester 2020OCTS_03

description: We propose a targeted pulsar survey of twelve candidate sources that display interplanetary scintillation or linear polarisation in lower frequency observations using the Murchison Widefield Array (MWA). Known pulsars have been detected using these characteristics. We request 12 hours of Parkes observations using the UWL receiver and back-end to conduct a sensitive and thorough pulsar search using novel techniques. Targeted searches for pulsars using imaging surveys have been revived in recent years due to the availability of new sensitive, broadband, widefield surveys using SKA precursors/pathfinders, and have already resulted in pulsar discoveries. Discovering new pulsars provides prospects for many science cases because pulsars provide powerful probes of the ISM and fundamental physics either individually or as a population.
DOI: 10.5281/zenodo.4412083
title: Replication Package for the Paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions"

description: This is the replication package for the paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions". It contains the source code and dataset of our experiment for the replication by other researchers. In the meanwhile, we provide brief description of the files in the replication package below. <strong>1. main_code folder</strong> <em>automatic_approach.py </em>contains the main source code of the automatic approach for identifying decisions in our experiment, which is conducted on MacOs and Python 3.7.9. <strong>Note that you may get slightly</strong> <strong>different experiment results when conducting the experiments on different environment configurations.</strong> <em>requirement.txt</em> records all the installation packages and their version numbers needed for the current program to run. You can use "<em>pip install -r requirement.txt</em>" to rebuild the project and install all dependencies. <strong>Note that you may get slightly different experiment results when using different packages or versions. </strong> <strong>2. dataset folder</strong> <em>EASE2020 - 650 decisions.xlsx </em>contains 650 decision sentences from our previous work (EASE2020) <em>EASE2020 - 650 non decisions.xlsx </em>contains 650 non-decision sentences from our previous work (EASE2020) <em>Our 844 relabeled decisions.xlsx</em> contains 844 relabeled decisions in this work. <em>Our 750 assumptions.xlsx</em> contains 750 assumptions from our previous work (APSEC2019) <strong>3. RQ1 folder</strong> <em>experiment_RQ1.py</em> contains the main source code of the experiment for answering RQ1, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em> <strong>4. RQ2 folder</strong> <em>experiment_RQ2.py</em> contains the main source code of the experiment for answering RQ2, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em> <strong>5. RQ3 folder</strong> <em>experiment_RQ3.py</em> contains the main source code of the experiment for answering RQ3, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em>
DOI: 10.5281/zenodo.4412082
title: Replication Package for the Paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions"

description: This is the replication package for the paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions". It contains the source code and dataset of our experiment for the replication by other researchers. In the meanwhile, we provide brief description of the files in the replication package below. <strong>1. main_code folder</strong> <em>automatic_approach.py </em>contains the main source code of the automatic approach for identifying decisions in our experiment, which is conducted on MacOs and Python 3.7.9. <strong>Note that you may get slightly</strong> <strong>different experiment results when conducting the experiments on different environment configurations.</strong> <em>requirement.txt</em> records all the installation packages and their version numbers needed for the current program to run. You can use "<em>pip install -r requirement.txt</em>" to rebuild the project and install all dependencies. <strong>Note that you may get slightly different experiment results when using different packages or versions. </strong> <strong>2. dataset folder</strong> <em>EASE2020 - 650 decisions.xlsx </em>contains 650 decision sentences from our previous work (EASE2020) <em>EASE2020 - 650 non decisions.xlsx </em>contains 650 non-decision sentences from our previous work (EASE2020) <em>Our 844 relabeled decisions.xlsx</em> contains 844 relabeled decisions in this work. <em>Our 750 assumptions.xlsx</em> contains 750 assumptions from our previous work (APSEC2019) <strong>3. RQ1 folder</strong> <em>experiment_RQ1.py</em> contains the main source code of the experiment for answering RQ1, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em> <strong>4. RQ2 folder</strong> <em>experiment_RQ2.py</em> contains the main source code of the experiment for answering RQ2, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em> <strong>5. RQ3 folder</strong> <em>experiment_RQ3.py</em> contains the main source code of the experiment for answering RQ3, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em>
DOI: 10.5519/qd.1k22jmdr
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5517/cc1j7zmc
title: CCDC 1407853: Experimental Crystal Structure Determination

description: Related Article: Xiao lufei|2015|CSD Communication|||
description: Related Article: Lufei Xiao, Fei Meng, Rui Xiao, Hui Wang, Xiaohe Tian|2021|J.Organomet.Chem.|933|121655|doi:10.1016/j.jorganchem.2020.121655
DOI: 10.5517/cc1j9959
title: CCDC 1409110: Experimental Crystal Structure Determination

description: Related Article: Xiao Lufei, Tian Yupeng|2015|CSD Communication|||
description: Related Article: Lufei Xiao, Fei Meng, Rui Xiao, Hui Wang, Xiaohe Tian|2021|J.Organomet.Chem.|933|121655|doi:10.1016/j.jorganchem.2020.121655
DOI: 10.5517/cc1kbsvj
title: CCDC 1440348: Experimental Crystal Structure Determination

description: Related Article: Tian Yupeng, Xiaolufei|2015|CSD Communication|||
description: Related Article: Lufei Xiao, Fei Meng, Rui Xiao, Hui Wang, Xiaohe Tian|2021|J.Organomet.Chem.|933|121655|doi:10.1016/j.jorganchem.2020.121655
DOI: 10.5517/ccdc.csd.cc23vzzy
title: CCDC 1962361: Experimental Crystal Structure Determination

description: Related Article: Xue Zhang, Xiaoyang Wang, Bin Wang, Zhi-Jun Ding, Chunju Li|2020|Chin.Chem.Lett.|31|3230|doi:10.1016/j.cclet.2020.02.037
DOI: 10.5281/zenodo.4282295
title: Replication Package for the Paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions"

description: This is the replication package for the paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions". It contains the source code and dataset of our experiment for the replication by other researchers. In the meanwhile, we provide brief description of the files in the replication package below. <strong>1. main_code folder</strong> <em>automatic_approach.py </em>contains the main source code of the automatic approach for identifying decisions in our experiment, which is conducted on MacOs and Python 3.7.9. <strong>Note that you may get slightly</strong> <strong>different experiment results when conducting the experiments on different environment configurations.</strong> <em>requirement.txt</em> records all the installation packages and their version numbers needed for the current program to run. You can use "<em>pip install -r requirement.txt</em>" to rebuild the project and install all dependencies. <strong>Note that you may get slightly different experiment results when using different packages or versions. </strong> <strong>2. dataset folder</strong> <em>EASE2020 - 650 decisions.xlsx </em>contains 650 decision sentences from our previous work (EASE2020) <em>EASE2020 - 650 non decisions.xlsx </em>contains 650 non-decision sentences from our previous work (EASE2020) <em>Our 844 relabeled decisions.xlsx</em> contains 844 relabeled decisions in this work. <em>Our 750 assumptions.xlsx</em> contains 750 assumptions from our previous work (APSEC2019) <strong>3. RQ1 folder</strong> <em>experiment_RQ1.py</em> contains the main source code of the experiment for answering RQ1, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em> <strong>4. RQ2 folder</strong> <em>experiment_RQ2.py</em> contains the main source code of the experiment for answering RQ2, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em> <strong>5. RQ3 folder</strong> <em>experiment_RQ3.py</em> contains the main source code of the experiment for answering RQ3, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em>
DOI: 10.5281/zenodo.4282294
title: Replication Package for the Paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions"

description: This is the replication package for the paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions". It contains the source code and dataset of our experiment for the replication by other researchers. In the meanwhile, we provide brief description of the files in the replication package below. <strong>1. main_code folder</strong> <em>automatic_approach.py </em>contains the main source code of the automatic approach for identifying decisions in our experiment, which is conducted on MacOs and Python 3.7.9. <strong>Note that you may get slightly</strong> <strong>different experiment results when conducting the experiments on different environment configurations.</strong> <em>requirement.txt</em> records all the installation packages and their version numbers needed for the current program to run. You can use "<em>pip install -r requirement.txt</em>" to rebuild the project and install all dependencies. <strong>Note that you may get slightly different experiment results when using different packages or versions. </strong> <strong>2. dataset folder</strong> <em>EASE2020 - 650 decisions.xlsx </em>contains 650 decision sentences from our previous work (EASE2020) <em>EASE2020 - 650 non decisions.xlsx </em>contains 650 non-decision sentences from our previous work (EASE2020) <em>Our 844 relabeled decisions.xlsx</em> contains 844 relabeled decisions in this work. <em>Our 750 assumptions.xlsx</em> contains 750 assumptions from our previous work (APSEC2019) <strong>3. RQ1 folder</strong> <em>experiment_RQ1.py</em> contains the main source code of the experiment for answering RQ1, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em> <strong>4. RQ2 folder</strong> <em>experiment_RQ2.py</em> contains the main source code of the experiment for answering RQ2, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em> <strong>5. RQ3 folder</strong> <em>experiment_RQ3.py</em> contains the main source code of the experiment for answering RQ3, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em>
DOI: 10.5281/zenodo.4321270
title: Grassland African Road Images (GARI): A Driving Dataset from Kenyan Highways and National Parks

description: A driving dataset collected on grassland roads in Kenyan national parks and highways in western Kenya. The dataset contains images from 20 hours of driving as well the driving signals from the vehicle such as speed, steering, acceleration etc. This is the first driving dataset from Africa to the best of our knowledge. See <em>GARI_dataset_description.pdf</em> for more information.
DOI: 10.5281/zenodo.4321269
title: Grassland African Road Images (GARI): A Driving Dataset from Kenyan Highways and National Parks

description: A driving dataset collected on grassland roads in Kenyan national parks and highways in western Kenya. The dataset contains images from 20 hours of driving as well the driving signals from the vehicle such as speed, steering, acceleration etc. This is the first driving dataset from Africa to the best of our knowledge. See <em>GARI_dataset_description.pdf</em> for more information.
DOI: 10.6084/m9.figshare.13513632.v1
title: Data_Rui_PhD_Thesis_NTHU_Discussion

description: This is used for Rui Han's PhD thesis in NTHU, 2020.
DOI: 10.5281/zenodo.4399659
title: Wikidata Dump Films

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.5281/zenodo.4399658
title: Wikidata Dump Films

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.5281/zenodo.4402295
title: Wikidata Dump SnomedCT_metadata

description: RDF dump of wikidata produced with wdumps. Pulling wiki SNOMED CT metadata<br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.5281/zenodo.4402294
title: Wikidata Dump SnomedCT_metadata

description: RDF dump of wikidata produced with wdumps. Pulling wiki SNOMED CT metadata<br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.5281/zenodo.4402297
title: Wikidata Dump SnomedCT_metadata

description: RDF dump of wikidata produced with wdumps. Pulling wiki SNOMED CT metadata<br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.5281/zenodo.4402296
title: Wikidata Dump SnomedCT_metadata

description: RDF dump of wikidata produced with wdumps. Pulling wiki SNOMED CT metadata<br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.15161/oar.it/73381
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.6084/m9.figshare.13514855
title: Monte-Carlo Results Thermal Conductivity

description: This archive contains the results of the Monte-Carlo Simulations performed using COMSOL Multiphysics.<br>
DOI: 10.6084/m9.figshare.13514855.v1
title: Monte-Carlo Results Thermal Conductivity

description: This archive contains the results of the Monte-Carlo Simulations performed using COMSOL Multiphysics.<br>
DOI: 10.6084/m9.figshare.13514858
title: Temperature Data Thermal Conductivity

description: This archive contains the calibrated temperature data of the active heating experiment performed on Sol 680 of the InSight Mars Mission.<br>
DOI: 10.5281/zenodo.4412199
title: MicN model calibration and calibration data

description: MicN model calibration and validation data, including concentrations of ammonium and nitrate and N2O emission.
DOI: 10.5281/zenodo.4412198
title: MicN model calibration and calibration data

description: MicN model calibration and validation data, including concentrations of ammonium and nitrate and N2O emission.
DOI: 10.5281/zenodo.4412126
title: Relative humidity measurements of the vault of the apse of the Cathedral of Valencia

description: This data set contains relative humidity measurements obtained with sensors installed in the apse vault of the Cathedral of Valencia in Spain. The interest of these sensors is to monitor the conservation conditions of Renaissance frescoes. Included files are: Cathedral_of_Valencia_RH_2008.csv : Relative humidity measurements for the year 2008 Cathedral_of_Valencia_RH_2010.csv : Relative humidity measurements for the year 2010
DOI: 10.5281/zenodo.4412125
title: Relative humidity measurements of the vault of the apse of the Cathedral of Valencia

description: This data set contains relative humidity measurements obtained with sensors installed in the apse vault of the Cathedral of Valencia in Spain. The interest of these sensors is to monitor the conservation conditions of Renaissance frescoes. Included files are: Cathedral_of_Valencia_RH_2008.csv : Relative humidity measurements for the year 2008 Cathedral_of_Valencia_RH_2010.csv : Relative humidity measurements for the year 2010
DOI: 10.5519/qd.7ewwvoc9
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.p3rv7pz8
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.25646/7805
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: 10.5519/qd.j5lwv5qz
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.6084/m9.figshare.13058945
title: The role of marginal sea isolations of the Northwestern Pacific during the Pleistocene on the genetic structure of Black sea bream Acanthopagrus schlegelii (Bleeker, 1854) in the coastal waters of Japan

description: Sequences, haplotypes and frequencies of the paper
DOI: 10.6084/m9.figshare.13058945.v1
title: The role of marginal sea isolations of the Northwestern Pacific during the Pleistocene on the genetic structure of Black sea bream Acanthopagrus schlegelii (Bleeker, 1854) in the coastal waters of Japan

description: Sequences, haplotypes and frequencies of the paper
DOI: 10.5281/zenodo.4412229
title: Automatic Method of Flow Direction Reasoning for Bridging Tributaries

description: Please see the detailed description of the data in the Read me.txt file.
DOI: 10.5281/zenodo.4412228
title: Automatic Method of Flow Direction Reasoning for Bridging Tributaries

description: Please see the detailed description of the data in the Read me.txt file.
DOI: 10.5281/zenodo.4412086
title: Word Sense Disambiguation based on Context Selection using Knowledge-based Word Similarity

description: This is graph-based word vector representations data for our publication "Word Sense Disambiguation based on Context Selection using Knowledge-based Word Similarity". The data include three files: "Experimental Results", "vector_result_bfs_set_all.zip", and "vector_result_dfs_set_all.zip". The "Experimental Results" contains the experimental results of our model ITSR+SRP_BFS and the golden set on WSD benchmarks: SensEeval-02, SensEeval-03, SemEval-7, SemEval-13, and SemEval-15. The "vector_result_bfs_set_all.zip" (SRP_BFS), and "vector_result_dfs_set_all.zip" (SRP_DFS) are word vector representations extracted from the BabelNet knowledge graph. These vectors were used for the Word Sense Disambiguation (WSD) task in the experiments.
DOI: 10.6084/m9.figshare.12272360.v4
title: 2bRAD-M simulated and sequenced datasets

description: 2bRAD-M paper related datasets
DOI: 10.12760/01-2017-1-03
title: Atmospheric correction of imaging spectroscopy data using shadow-based quantification of aerosol scattering effects.

description: The atmospheric correction accuracy strongly depends on the correct estimate of the aerosol scattering effects. This paper shows first results of a new aerosol optical thickness inversion method and its use for improved atmospheric correction of high spatial resolution imaging spectroscopy data. The approach uses small scale shadow pixels for the determination of the atmospheric scattering by inverting the shadow correction within the ATCOR atmospheric compensation method. The detection of shadow pixels is done by a blue to red ratio which is further adjusted by the near infrared band in order to take the vegetation bias into account. On high resolution instruments with resolutions below 5 m, a decent quantity of shaded pixels can be found by this method in a reliable way. Using this shadow mask, the aerosol inversion is done. The aerosol contents are varied in a way that retrieves a correction of shaded areas to a brightness comparable to non-shaded areas, leading to the best fitting aerosol amount. The shadow based aerosol optical thickness (SHAOT) method is tested on APEX and HYSPEX airborne imaging spectroscopy data. It can be shown that the atmospheric compensation using the such derived aerosol contents are significantly improved in comparison to standard correction techniques.
DOI: 10.34740/kaggle/dsv/1809461
title: US counties COVID 19 dataset