cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc258cg9
title: CCDC 2004040: Experimental Crystal Structure Determination

description: Related Article: Kohei Iwai, Hiroshi Yamagishi, Colin Herzberger, Yuji Sato, Hayato Tsuji, Ken Albrecht, Kimihisa Yamamoto, Fumio Sasaki, Hiroyasu Sato, Aswin Asaithambi, Axel Lorke, Yohei Yamamoto|2020|Angew.Chem.,Int.Ed.|59|12674|doi:10.1002/anie.202000712
DOI: 10.5517/ccdc.csd.cc258chb
title: CCDC 2004041: Experimental Crystal Structure Determination

description: Related Article: Kohei Iwai, Hiroshi Yamagishi, Colin Herzberger, Yuji Sato, Hayato Tsuji, Ken Albrecht, Kimihisa Yamamoto, Fumio Sasaki, Hiroyasu Sato, Aswin Asaithambi, Axel Lorke, Yohei Yamamoto|2020|Angew.Chem.,Int.Ed.|59|12674|doi:10.1002/anie.202000712
DOI: 10.5517/ccdc.csd.cc258xp2
title: CCDC 2004574: Experimental Crystal Structure Determination

description: Related Article: Bin Jiang, James J.-W. Duan, Sylwia Stachura, Ananta Karmakar, Hemalatha Hemagiri, Dhanya Kumar Raut, Arun Kumar Gupta, Carolyn A. Weigelt, Javed Khan, John S. Sack, Dauh-Rurng Wu, Melissa Yarde, Ding-Ren Shen, Michael A. Galella, Arvind Mathur, Qihong Zhao, Luisa M. Salter-Cid, Percy H. Carter, T.G. Murali Dhar|2020|Bioorg.Med.Chem.Lett.|30|127392|doi:10.1016/j.bmcl.2020.127392
DOI: 10.5517/ccdc.csd.cc25km9p
title: CCDC 2012932: Experimental Crystal Structure Determination

description: Related Article: Bin Jiang, James J.-W. Duan, Sylwia Stachura, Ananta Karmakar, Hemalatha Hemagiri, Dhanya Kumar Raut, Arun Kumar Gupta, Carolyn A. Weigelt, Javed Khan, John S. Sack, Dauh-Rurng Wu, Melissa Yarde, Ding-Ren Shen, Michael A. Galella, Arvind Mathur, Qihong Zhao, Luisa M. Salter-Cid, Percy H. Carter, T.G. Murali Dhar|2020|Bioorg.Med.Chem.Lett.|30|127392|doi:10.1016/j.bmcl.2020.127392
DOI: 10.15161/oar.it/30631
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: 10.5281/zenodo.3899870
title: RIBuild: Energy saving potential, LCA and LCC - case studies

description: Data set from (1) Danish, Latvian and Italian desk-top case studies on energy saving potential, including description and output from building simulation using national simulation tools, referring to RIBuild Task 5.1. (2) LCA and LCC analyses on Italian case study, referring to RIBuild Task 5.2 and 5.3. Overview of data files to be found in ’RIBuild data WP5 Case studies’ as part of this dataset. Further details to be found in RIBuild deliverable D5.1 (Task 5.1 and 5.2) and D5.2 (Task 5.3).
DOI: 10.5281/zenodo.3889876
title: RIBuild: Hygrothermal performance of hydrophobized masonry walls (KUL Vliet test building)

description: Measurement data from a field study on a test building at KU Leuven, studying the hygrothermal performance of hydrophobised walls, provided with vapor tight or capillary active internal insulation. As a reference, also non-hydrophobized and non-insulated walls are analysed. The dataset also includes photo documentation of construction and installation of measurement sensors. Further details to be found in RIBuild deliverable D2.3. Overview of data files to be found in 'RIBuild data_WP2 KUL Vliet' as part of this dataset.
DOI: 10.5281/zenodo.3889875
title: RIBuild: Hygrothermal performance of hydrophobized masonry walls (KUL Vliet test building)

description: Measurement data from a field study on a test building at KU Leuven, studying the hygrothermal performance of hydrophobised walls, provided with vapor tight or capillary active internal insulation. As a reference, also non-hydrophobized and non-insulated walls are analysed. The dataset also includes photo documentation of construction and installation of measurement sensors. Further details to be found in RIBuild deliverable D2.3. Overview of data files to be found in 'RIBuild data_WP2 KUL Vliet' as part of this dataset.
DOI: 10.5281/zenodo.3991597
title: Last interglacial (MIS 5e) sea-level proxies in southeastern South America

description: This dataset is a compilation of published last interglacial sea level indicators for the southeast coast of South America (including Uruguay, Argentina, and one site in southern Chile). We have documented 60 proxies, which includes 48 sea level index points, 11 marine limiting indicators and 1 terrestrial limiting indicator. All of these data have at least some chronological control, with radiocarbon, electron spin resonance, U/Th, amino acid racemization and OSL dating techniques. The vertical uncertainty values were assigned based on information given for measurements, and the type of deposit.
DOI: 10.5285/59d3c54d-8179-4904-8ee7-1a81564ed893
title: Magnetotelluric data from before, during and after the September 2017 magnetic storm at 7 sites in Scotland

description: Magnetotelluric (MT) time series including the September 2017 magnetic storm at 7 sites in the Scottish Highlands collected by Fiona Simpson (University of Southampton) and Karsten Bahr (University of Göttingen) using Göttingen RAP dataloggers, Magson fluxgate magnetometers and Filloux-type electrodes. 

Data acquisition methodology is described in F. Simpson and K. Bahr, 2005. Practical Magnetotellurics, Cambridge University Press, London pp. 254, 2005, ISBN: 9781108462556, DOI: 10.1017/CBO9780511614095

This dataset is described in:

F. Simpson and K. Bahr, 2020a. Nowcasting and validating Earth’s electric field response to extreme space weather events using magnetotelluric data: application to the September 2017 geomagnetic storm and comparison to observed and modelled fields in Scotland, Space Weather, accepted, doi pending. 

F. Simpson and K. Bahr, 2020b. Estimating the electric field response to the Halloween 2003 and September 2017 magnetic storms across Scotland using observed geomagnetic fields, magnetotelluric impedances and perturbation tensors, Journal of Space Weather and Space Climate, accepted, doi pending.
DOI: 10.15129/18916640-5b9d-4bea-afed-db2e1ca0f207
title: Data for: "Improving consistency for a Mefenamic acid immediate release formulation"

description: This data set supports the findings of the study 'Improving consistency for a Mefenamic acid immediate release formulation' published in the Journal of Pharmaceutical Sciences. Data is provided in Microsoft excel format. Original data files are available upon request. Particle size analysis data - Qic/PIC, dynamic imaging system, equipped with the Rhodos attachment from Sympathec Rheology: Temperature sweep and Frequency sweep data - Haake Mars III rotational rheometer Hot-melt-extrusion process data - Process 11, Thermo Fisher Filament diameter data Mechanical testing: Texture Analyser TA-XT (Stable Micro Systems) equipped with a mini 3-point bend rig HPLC data and method validation - Agilent 1100 LC system equipped with G1315A Diode Array Detector Dissolution test data: ADT8i Dissolution bath (USP I, basket) apparatus with a closed loop setting and a T70+ UV/Visible Spectrophotometer (Automated Lab Systems)
DOI: 10.5519/qd.pi72yoep
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5517/ccdc.csd.cc2385wg
title: CCDC 1944285: Experimental Crystal Structure Determination

description: Related Article: Isabelle Karine da Costa Nunes, Everton Tenório de Souza, Italo Rossi Roseno Martins, Gisele Barbosa, Manoel Oliveira de Moraes Junior, Millena de Melo Medeiros, Sheyla Welma Duarte Silva, Tatiane Luciano Balliano, Bagnólia Araújo da Silva, Patrícia Machado Rodrigues Silva, Vinicius de Frias Carvalho, Marco Aurélio Martins, Lidia Moreira Lima|2020|Eur.J.Med.Chem.|204|112492|doi:10.1016/j.ejmech.2020.112492
DOI: 10.5517/ccdc.csd.cc24hpy9
title: CCDC 1981301: Experimental Crystal Structure Determination

description: Related Article: G. Kalaiarasi, S. Dharani, S. Rex Jeya Rajkumar, Vincent M. Lynch, R. Prabhakaran|2020|J.Inorg.Biochem.|211|111176|doi:10.1016/j.jinorgbio.2020.111176
DOI: 10.5517/ccdc.csd.cc24hpzb
title: CCDC 1981302: Experimental Crystal Structure Determination

description: Related Article: G. Kalaiarasi, S. Dharani, S. Rex Jeya Rajkumar, Vincent M. Lynch, R. Prabhakaran|2020|J.Inorg.Biochem.|211|111176|doi:10.1016/j.jinorgbio.2020.111176
DOI: 10.5517/ccdc.csd.cc24nrcy
title: CCDC 1986150: Experimental Crystal Structure Determination

description: Related Article: Galina. B. Guseva, Elena V. Antina, Mikhail B. Berezin, Roman S. Pavelyev, Airat R. Kayumov, Irshad S. Sharafutdinov, Svetlana А. Lisovskaya, Olga A. Lodochnikova, Daut R. Islamov, Konstantin S. Usachev, Sergei V. Boichuk, Liliya E. Nikitina|2020|J.Photochem.Photobiol.A:Chem.|401|112783|doi:10.1016/j.jphotochem.2020.112783
DOI: 10.5517/ccdc.csd.cc24vgl2
title: CCDC 1991644: Experimental Crystal Structure Determination

description: Related Article: Larisa Gurskaya, Tatyana Rybalova, Irina Beregovaya, Elena Zaytseva, Maxim Kazantsev, Evgeny Tretyakov|2020|J.Fluorine Chem.|237|109613|doi:10.1016/j.jfluchem.2020.109613
DOI: 10.5517/ccdc.csd.cc24vgm3
title: CCDC 1991645: Experimental Crystal Structure Determination

description: Related Article: Larisa Gurskaya, Tatyana Rybalova, Irina Beregovaya, Elena Zaytseva, Maxim Kazantsev, Evgeny Tretyakov|2020|J.Fluorine Chem.|237|109613|doi:10.1016/j.jfluchem.2020.109613
DOI: 10.5517/ccdc.csd.cc24vgn4
title: CCDC 1991646: Experimental Crystal Structure Determination

description: Related Article: Larisa Gurskaya, Tatyana Rybalova, Irina Beregovaya, Elena Zaytseva, Maxim Kazantsev, Evgeny Tretyakov|2020|J.Fluorine Chem.|237|109613|doi:10.1016/j.jfluchem.2020.109613
DOI: 10.5517/ccdc.csd.cc24vgp5
title: CCDC 1991647: Experimental Crystal Structure Determination

description: Related Article: Larisa Gurskaya, Tatyana Rybalova, Irina Beregovaya, Elena Zaytseva, Maxim Kazantsev, Evgeny Tretyakov|2020|J.Fluorine Chem.|237|109613|doi:10.1016/j.jfluchem.2020.109613
DOI: 10.5517/ccdc.csd.cc255l87
title: CCDC 2001368: Experimental Crystal Structure Determination

description: Related Article: Ying Zhao, Li-Long Dang, Zhi-Min Zhai, Lu-Fang Ma, Li-Ya Wang|2020|J.Solid State Chem.|290|121573|doi:10.1016/j.jssc.2020.121573
DOI: 10.5517/ccdc.csd.cc255l98
title: CCDC 2001369: Experimental Crystal Structure Determination

description: Related Article: Ying Zhao, Li-Long Dang, Zhi-Min Zhai, Lu-Fang Ma, Li-Ya Wang|2020|J.Solid State Chem.|290|121573|doi:10.1016/j.jssc.2020.121573
DOI: 10.5517/ccdc.csd.cc243zcm
title: CCDC 1970030: Experimental Crystal Structure Determination

description: Related Article: Alexandros A. Kitos, Diogo A. Gálico, Raúl Castañeda, Jeffrey S. Ovens, Muralee Murugesu, Jaclyn L. Brusso|2020|Inorg.Chem.|59|11061|doi:10.1021/acs.inorgchem.0c01534
DOI: 10.5281/zenodo.3991655
title: Twinning in Zr-based Metal-organic Framework Crystals

description: Cif files for three metal-organic framework compounds.
DOI: 10.5281/zenodo.3991654
title: Twinning in Zr-based Metal-organic Framework Crystals

description: Cif files for three metal-organic framework compounds.
DOI: 10.5281/zenodo.3991668
title: KULeuvenDigitalisering/The-Views-of-Leuven-Collection-Dataset

description: First release of the Views of Leuven Collection Dataset. Full dataset of the Views of Leuven Collection which includes images and maps of Leuven from the 16th-20th centuries.
DOI: 10.5281/zenodo.3991667
title: KULeuvenDigitalisering/The-Views-of-Leuven-Collection-Dataset

description: First release of the Views of Leuven Collection Dataset. Full dataset of the Views of Leuven Collection which includes images and maps of Leuven from the 16th-20th centuries.
DOI: 10.5281/zenodo.3991673
title: WALIS database tropical Pacific

description: A standardized database of Marine Isotopic Stage 5e sea-level proxies on tropical Pacific Islands
DOI: 10.5281/zenodo.3991670
title: Greenland Ecosystem Monitoring - Location and Description of Research Stations

description: This shapefile contains locations and descriptions of the research stations that are part of the Greenland Ecosystem Monitoring Program - https://www.g-e-m.dk . Greenland Ecosystem Monitoring (GEM) is an integrated monitoring and long-term research programme on ecosystems and climate change effects and feedbacks in the Arctic. Since 1995 the programme has established a coherent and integrated understanding of the functioning of ecosystems in a highly variable climate, which is based upon a comprehensive, long-term inter-disciplinary data collection carried out by Danish and Greenlandic monitoring and research institutions. The GEM Programme put around 75 scientists in the field annually to collect data on ecosystem and climate change in Greenland. The data base currently covers data from monitoring programmes from Zackenberg (1995-), Kobbefjord at Nuuk (2007-) and Disko (2017-). The well over 1000 parameters are freely available via the GEM Database and used by GEM participants and external scientists to produce scientific papers, scientific assessments, advisory reports, etc. The monitoring data can be downloaded via https://data.g-e-m.dk
DOI: 10.5281/zenodo.3991669
title: Greenland Ecosystem Monitoring - Location and Description of Research Stations

description: This shapefile contains locations and descriptions of the research stations that are part of the Greenland Ecosystem Monitoring Program - https://www.g-e-m.dk . Greenland Ecosystem Monitoring (GEM) is an integrated monitoring and long-term research programme on ecosystems and climate change effects and feedbacks in the Arctic. Since 1995 the programme has established a coherent and integrated understanding of the functioning of ecosystems in a highly variable climate, which is based upon a comprehensive, long-term inter-disciplinary data collection carried out by Danish and Greenlandic monitoring and research institutions. The GEM Programme put around 75 scientists in the field annually to collect data on ecosystem and climate change in Greenland. The data base currently covers data from monitoring programmes from Zackenberg (1995-), Kobbefjord at Nuuk (2007-) and Disko (2017-). The well over 1000 parameters are freely available via the GEM Database and used by GEM participants and external scientists to produce scientific papers, scientific assessments, advisory reports, etc. The monitoring data can be downloaded via https://data.g-e-m.dk
DOI: 10.5281/zenodo.3923862
title: Data Documentation Erdgas-BRidGE – Input data for modeling the power, building, and gas sector

description: This data documentation provides data representing parts of the power, the building and gas sector, which have been compiled within the research project Erdgas-BRidGE (Erdgas - Bedeutung und zukünftige Rolle in der deutschen (German) Energiewende). The aim of this documentation is to increase the transparency of input data for energy modeling in the German context. Therefore, the report Erdgas-BRidGE_Data_Documentation_Report (2020).pdf documents the data collected and processed in the course of the project. Furthermore, the data set Erdgas-BRidGE_Dataset (2020).xlsx provides the compiled or further processed data with separate table sheets. The script available under the file name PythonCodeToProcessCapacityBookings.zip has been used to process historical capacity bookings. Erdgas-BRidGE is a joined effort by the Energiewirtschaftliches Insitut an der Universität zu Köln (ewi) and the Chair of Energy Economics at the Technische Universität Dresden (TUD-EE2). The project was funded by the Federal Ministry for Economic Affairs and Energy through the grant "Erdgas-BRidGE", FKZ: 03ET4055A.
DOI: 10.5281/zenodo.3991702
title: dissertatiom

description: bla bla
DOI: 10.5281/zenodo.3991701
title: dissertatiom

description: bla bla
DOI: 10.5281/zenodo.3991719
title: RCTs and non-RCT studies on preventive measures and treatments for Covid-19

description: In the course of our PubMed searches and prepreints from MedRxiv, we identified a number of RCTs and non-randomized studies on preventive measures and treatments for Covid-19 that we have included in our systematic review. This file is updated regularly.
DOI: 10.5281/zenodo.3835735
title: RCTs and non-RCT studies on preventive measures and treatments for Covid-19

description: In the course of our PubMed searches and prepreints from MedRxiv, we identified a number of RCTs and non-randomized studies on preventive measures and treatments for Covid-19 that we have included in our systematic review. This file is updated regularly.
DOI: 10.5517/ccdc.csd.cc21h5h7
title: CCDC 1891418: Experimental Crystal Structure Determination

description: Related Article: Karlo Wittine, Roberto Antolović, Dubravko Jelić, Sara Bracanović, Mario Cetina, Uroš Andjelkovic, Ozren Wittine, Sandra Kraljević Pavelić, Adrijana Vinter|2020|Bioorg.Med.Chem.Lett.|30|127415|doi:10.1016/j.bmcl.2020.127415
DOI: 10.5517/ccdc.csd.cc2385xh
title: CCDC 1944286: Experimental Crystal Structure Determination

description: Related Article: Isabelle Karine da Costa Nunes, Everton Tenório de Souza, Italo Rossi Roseno Martins, Gisele Barbosa, Manoel Oliveira de Moraes Junior, Millena de Melo Medeiros, Sheyla Welma Duarte Silva, Tatiane Luciano Balliano, Bagnólia Araújo da Silva, Patrícia Machado Rodrigues Silva, Vinicius de Frias Carvalho, Marco Aurélio Martins, Lidia Moreira Lima|2020|Eur.J.Med.Chem.|204|112492|doi:10.1016/j.ejmech.2020.112492
DOI: 10.5517/ccdc.csd.cc2467by
title: CCDC 1972199: Experimental Crystal Structure Determination

description: Related Article: Keiichi Sato, Yasunori Yamada, Takeshi Hanamoto|2020|J.Fluorine Chem.|237|109599|doi:10.1016/j.jfluchem.2020.109599
DOI: 10.5281/zenodo.3696775
title: Problem instances for scheduling jobs with time windows on unrelated parallel machines

description: The following dataset contains problem instances for the unrelated machine scheduling problem with job release dates and deadlines, which are used in the article "Tadumadze, G., Emde, S. & Diefenbach, H. Exact and heuristic algorithms for scheduling jobs with time windows on unrelated parallel machines. <em>OR Spectrum</em> <strong>42, </strong>461–497 (2020). https://doi.org/10.1007/s00291-020-00586-w". The problem instances are stored in table “instances”, where the columns of the table can be interpreted as follows: Problem_ID: &lt;Autonumber&gt; n: &lt;number of jobs&gt;; m: &lt;number of machines&gt;; w: &lt;vector with n elements: the j-th element corresponds to the weight of job j&gt;; r: &lt;vector with n elements: the j-th element corresponds to the release date of job j&gt;; d: &lt;vector with n elements: the j-th element corresponds to the deadline of job j&gt;; p: &lt;n*m matrix: each entry in j-th column and i-th row corresponds to the processing time of job j on machine i&gt;; The first 80 entries (Problem_ID between 1-80), contain discrete Berth-allocation problem instances, provided by “Jean-François Cordeau, Gilbert Laporte, Pasquale Legato, Luigi Moccia, (2005) Models and Tabu Search Heuristics for the Berth-Allocation Problem. Transportation Science 39(4):526-538. https://doi.org/10.1287/trsc.1050.0120” and additionally contain machine availability times, which are stored in the following columns: s: &lt;vector with m elements: the i-th element corresponds to the start availability time of machine i&gt;; e: &lt;vector with m elements: the i-th element corresponds to the end availability time of machine i&gt;; The following 270 entries (Problem_ID between 81-270) contain newly generated random problem instances with the instance generation scheme proposed by “Nicholas G. Hall, Marc E. Posner, (2001) Generating Experimental Data for Computational Testing with Machine Scheduling Applications. Operations Research 49(6):854-865. https://doi.org/10.1287/opre.49.6.854.10014”. The first 10 instances (Problem_ID between 81-90) are used for parameter tuning tests and the next 180 (Problem_ID between 91-270) instances for computational performance comparison. The last 30 entries (Problem_ID between 271-300) contain integrated truck and workforce scheduling problem instances with fixed workforce at each door, provided by “Giorgi Tadumadze, Nils Boysen, Simon Emde, Felix Weidinger (2019) Integrated truck and workforce scheduling to accelerate the unloading of trucks. European Journal of Operational Research 278(1):343-362. https://doi.org/10.1016/j.ejor.2019.04.024”. The detailed computational results for each instance, approach and objective function are reported in tables which are named with the following convention: "results_&lt;approach&gt;_&lt;objective value&gt;”.
DOI: 10.5281/zenodo.3696774
title: Problem instances for scheduling jobs with time windows on unrelated parallel machines

description: The following dataset contains problem instances for the unrelated machine scheduling problem with job release dates and deadlines, which are used in the article "Tadumadze, G., Emde, S. & Diefenbach, H. Exact and heuristic algorithms for scheduling jobs with time windows on unrelated parallel machines. <em>OR Spectrum</em> <strong>42, </strong>461–497 (2020). https://doi.org/10.1007/s00291-020-00586-w". The problem instances are stored in table “instances”, where the columns of the table can be interpreted as follows: Problem_ID: &lt;Autonumber&gt; n: &lt;number of jobs&gt;; m: &lt;number of machines&gt;; w: &lt;vector with n elements: the j-th element corresponds to the weight of job j&gt;; r: &lt;vector with n elements: the j-th element corresponds to the release date of job j&gt;; d: &lt;vector with n elements: the j-th element corresponds to the deadline of job j&gt;; p: &lt;n*m matrix: each entry in j-th column and i-th row corresponds to the processing time of job j on machine i&gt;; The first 80 entries (Problem_ID between 1-80), contain discrete Berth-allocation problem instances, provided by “Jean-François Cordeau, Gilbert Laporte, Pasquale Legato, Luigi Moccia, (2005) Models and Tabu Search Heuristics for the Berth-Allocation Problem. Transportation Science 39(4):526-538. https://doi.org/10.1287/trsc.1050.0120” and additionally contain machine availability times, which are stored in the following columns: s: &lt;vector with m elements: the i-th element corresponds to the start availability time of machine i&gt;; e: &lt;vector with m elements: the i-th element corresponds to the end availability time of machine i&gt;; The following 270 entries (Problem_ID between 81-270) contain newly generated random problem instances with the instance generation scheme proposed by “Nicholas G. Hall, Marc E. Posner, (2001) Generating Experimental Data for Computational Testing with Machine Scheduling Applications. Operations Research 49(6):854-865. https://doi.org/10.1287/opre.49.6.854.10014”. The first 10 instances (Problem_ID between 81-90) are used for parameter tuning tests and the next 180 (Problem_ID between 91-270) instances for computational performance comparison. The last 30 entries (Problem_ID between 271-300) contain integrated truck and workforce scheduling problem instances with fixed workforce at each door, provided by “Giorgi Tadumadze, Nils Boysen, Simon Emde, Felix Weidinger (2019) Integrated truck and workforce scheduling to accelerate the unloading of trucks. European Journal of Operational Research 278(1):343-362. https://doi.org/10.1016/j.ejor.2019.04.024”. The detailed computational results for each instance, approach and objective function are reported in tables which are named with the following convention: "results_&lt;approach&gt;_&lt;objective value&gt;”.
DOI: 10.34740/kaggle/dsv/1430059
title: US counties COVID 19 dataset