cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6522810
title: European Aerosol Phenomenology - 8: Harmonised Source Apportionment of Organic Aerosol using 22 Year-long ACSM/AMS Datasets

description: Organic aerosol (OA) is a key component of total submicron particulate matter (PM<sub>1</sub>), and comprehensive knowledge of OA sources across Europe is crucial to mitigate PM<sub>1</sub> levels. Europe has a well-established air quality research infrastructure from which yearlong datasets using 21 aerosol chemical speciation monitors (ACSMs) and 1 aerosol mass spectrometer (AMS) were gathered during 2013–2019. It includes 9 non-urban and 13 urban sites. This study developed a state-of-the-art source apportionment protocol to analyse long-term OA mass spectrum data by applying the most advanced source apportionment strategies (i.e., rolling PMF, ME-2, and bootstrap). This harmonised protocol was followed strictly for all 22 datasets, making the source apportionment results more comparable. In addition, it enables quantification of the most common OA components such as hydrocarbon-like OA (HOA), biomass burning OA (BBOA), cooking-like OA (COA), more oxidised-oxygenated OA (MO-OOA), and less oxidised-oxygenated OA (LO-OOA). Other components such as coal combustion OA (CCOA), solid fuel OA (SFOA: mainly mixture of coal and peat combustion), cigarette smoke OA (CSOA), sea salt (mostly inorganic but part of the OA mass spectrum), coffee OA, and ship industry OA could also be separated at a few specific sites. Oxygenated OA (OOA) components make up most of the submicron OA mass (average = 71.1%, range from 43.7 to 100%). Solid fuel combustion-related OA components (i.e., BBOA, CCOA, and SFOA) are still considerable with in total 16.0% yearly contribution to the OA, yet mainly during winter months (21.4%). Overall, this comprehensive protocol works effectively across all sites governed by different sources and generates robust and consistent source apportionment results. Our work presents a comprehensive overview of OA sources in Europe with a unique combination of high time resolution (30–240 min) and long-term data coverage (9–36 months), providing essential information to improve/validate air quality, health impact, and climate models.
DOI: 10.22008/fk2/jkgzpw/z6ckw8
title: Basisdatakort_40-1_Rudkoebing_Bagenkop_Spodsbjerg.pdf

description: Kortblad - Map sheet
DOI: 10.22008/fk2/abxko4/zwvfjq
title: Basisdatakort_39-1_Tommerup_Haderslev.pdf

description: Kortblad - Map sheet
DOI: 10.5281/zenodo.6382482
title: DEBS 2022 Grand Challenge Data Set: Trading Data

description: The data provided here as part of the DEBS 2022 Grand Challenge is based on real tick data captured by Infront Financial Technology GmbH for the complete week of November 8th to 14th, 2021 (i.e., five trading days Monday to Friday + Saturday and Sunday). The data set contains 289 million tick data events covering 5504 equities and indices that are traded on three European exchanges: Paris (FR), Amsterdam (NL), and Frankfurt (ETR). Some event notifications appear to come with no payload. This is due to the fact that the 2022 GC requires only a small subset of attributes to be evaluated; other attributes have been eliminated from the data set to minimize its overall size while keeping the amount of events to process unchanged. Further details on the data set, its syntax and its semantics can be found in the official DEBS 2022 Grand Challenge paper as part of the DEBS 2022 conference proceedings (please use this for citation): <br> <br> <em>Sebastian Frischbier, Jawad Tahir, Christoph Doblander, Arne Hormann, Ruben Mayer, and Hans-Arno Jacobsen. 2022. The DEBS 2022 Grand Challenge: Detecting Trading Trends in Financial Tick Data. In The 16th ACM International Conference on Distributed and Event-based Systems (DEBS ’22), </em>June 27-June 30<em>, 2022, Copenhagen. ACM, New York, NY, USA.</em> <strong>All files of the DEBS 2022 Grand Challenge Data Set “Trading Data” are provided as-is. By downloading and using this data you agree to the terms and conditions of the licensing agreement (CC by-nc-sa).</strong>
description: {"references": ["Sebastian Frischbier, Jawad Tahir, Christoph Doblander, Arne Hormann, Ruben Mayer, and Hans-Arno Jacobsen. 2022. The DEBS 2022 Grand Challenge: Detecting Trading Trends in Financial Tick Data. In The 16th ACM International Conference on Distributed and Event-based Systems (DEBS'22), June 27-June 30, 2022, Copenhagen. ACM, New York, NY, USA, 7 pages."]}
DOI: 10.5281/zenodo.6382481
title: DEBS 2022 Grand Challenge Data Set: Trading Data

description: The data provided here as part of the DEBS 2022 Grand Challenge is based on real tick data captured by Infront Financial Technology GmbH for the complete week of November 8th to 14th, 2021 (i.e., five trading days Monday to Friday + Saturday and Sunday). The data set contains 289 million tick data events covering 5504 equities and indices that are traded on three European exchanges: Paris (FR), Amsterdam (NL), and Frankfurt (ETR). Some event notifications appear to come with no payload. This is due to the fact that the 2022 GC requires only a small subset of attributes to be evaluated; other attributes have been eliminated from the data set to minimize its overall size while keeping the amount of events to process unchanged. Further details on the data set, its syntax and its semantics can be found in the official DEBS 2022 Grand Challenge paper as part of the DEBS 2022 conference proceedings (please use this for citation): <br> <br> <em>Sebastian Frischbier, Jawad Tahir, Christoph Doblander, Arne Hormann, Ruben Mayer, and Hans-Arno Jacobsen. 2022. The DEBS 2022 Grand Challenge: Detecting Trading Trends in Financial Tick Data. In The 16th ACM International Conference on Distributed and Event-based Systems (DEBS ’22), </em>June 27-June 30<em>, 2022, Copenhagen. ACM, New York, NY, USA.</em> <strong>All files of the DEBS 2022 Grand Challenge Data Set “Trading Data” are provided as-is. By downloading and using this data you agree to the terms and conditions of the licensing agreement (CC by-nc-sa).</strong>
description: {"references": ["Sebastian Frischbier, Jawad Tahir, Christoph Doblander, Arne Hormann, Ruben Mayer, and Hans-Arno Jacobsen. 2022. The DEBS 2022 Grand Challenge: Detecting Trading Trends in Financial Tick Data. In The 16th ACM International Conference on Distributed and Event-based Systems (DEBS'22), June 27-June 30, 2022, Copenhagen. ACM, New York, NY, USA, 7 pages."]}
DOI: 10.5281/zenodo.6671039
title: Bio-oil production from biogenic wastes, the hydrothermal conversion step - Data

description: Food wastes are an abundant resource that can be effectively valorised by hydrothermal liquefaction to produce bio-fuels. The objective of the European project Waste2Road is to demonstrate the complete value chain from waste collection to engine tests. The principle of hydrothermal liquefaction is well known but there are still many factors that make the science very empirical. Most experiments in the literature are performed on batch reactors. Comparison of results from batch reactors with experiments with continuous reactors are rare in the literature. This dataset presents fully documented experiments, performed in this project, on food wastes, with different compositions, conditions and solvents. The data set is extended with data from the literature. This data set also includes bio-oil and aqueous phase analysis by gas chromatography coupled with mass spectrometry.
DOI: 10.22008/fk2/mkxzfg/xe0qun
title: Basisdatakort_37-1_Bogense.pdf

description: :unav
DOI: 10.22008/fk2/vgo1sp/nimzbs
title: Basisdatakort_36-1_Odense_Korsoer.pdf

description: Kortblad - Map sheet
DOI: 10.22008/fk2/nsvbra/im3g9q
title: Basisdatakort_35-1_Faaborg.pdf

description: Kortblad - Map sheet
DOI: 10.22008/fk2/46wb8c/cwz2vj
title: Basisdatakort_34-1_Hammershoej.pdf

description: Kortblad - Map sheet
DOI: 10.22008/fk2/ub1ltg/bqaoed
title: Basisdatakort_33-1_Karup.pdf

description: Kortblad - Map sheet
DOI: 10.22008/fk2/yup3zd/oosi9x
title: Basisdatakort_32-1_Bjerringbro.pdf

description: Kortblad - Map sheet
DOI: 10.48579/pro/sngycn/ohrxzt
title: ERDA_dataStaurolite.tab

description: :unav
DOI: 10.48579/pro/sngycn/leydeb
title: Data_description.pdf

description: :unav
DOI: 10.48579/pro/sngycn/gx9ljv
title: NanoSIMS_data_st.tab

description: :unav
DOI: 10.48579/pro/sngycn
title: NanoSIMS determination of the water content of staurolite

description: These data (NanoSIMS and ERDA) are affiliated to the article "NanoSIMS determination of the water content of staurolite" ( doi: 10.1002/rcm.9331).
DOI: 10.18746/bmth.data.00000232
title: Data journalism practises in Gulf Cooperation Council (GCC) states - content analysis
DOI: 10.5517/ccdc.csd.cc26p5rv
title: CCDC 2046147: Experimental Crystal Structure Determination

description: Related Article: Zhaolong Wang, Xinyu Gou, Gang Wang, Xingmao Chang, Ke Liu, Taihong Liu, Gang He, Yu Fang|2022|Chem.Commun.|58|7082|doi:10.1039/D2CC02042D
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc27g1sk
title: CCDC 2069088: Experimental Crystal Structure Determination

description: Related Article: Manmath Mishra, Prabhat Kumar Maharana, Pallab Karjee, Tharmalingam Punniyamurthy|2022|Chem.Commun.|58|7090|doi:10.1039/D2CC01926D
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc291b8z
title: CCDC 2116440: Experimental Crystal Structure Determination

description: Related Article: Giammarco Maria Romano, Liviana Mummolo, Matteo Savastano, Paola Paoli, Patrizia Rossi, Luca Prodi, Andrea Bencini|2022|Chem.Commun.|58|7022|doi:10.1039/D2CC01107G
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc29yhzr
title: CCDC 2143525: Experimental Crystal Structure Determination

description: Related Article: Na Yeon Kwon, Neeraj Kumar Mishra, Jung Su Park, Sang Kook Woo, Prithwish Ghosh, In Su Kim|2022|Chem.Commun.|58|7078|doi:10.1039/D2CC02636H
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2b5qnx
title: CCDC 2150459: Experimental Crystal Structure Determination

description: Related Article: Lalita Devi, Prashant Kumar, Ruchir Kant, Namrata Rastogi|2022|Chem.Commun.|58|7062|doi:10.1039/D2CC01897G
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2b8931
title: CCDC 2152922: Experimental Crystal Structure Determination

description: Related Article: Zheng Liu, Qian Li, Chengyong Yang, Xuesong Zheng, Di Wu, Ge Gao, Jingbo Lan|2022|Chem.Commun.|58|7042|doi:10.1039/D2CC01646J
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bdqj0
title: CCDC 2157182: Experimental Crystal Structure Determination

description: Related Article: Monojit Batabyal, Saket Jain, Aditya Upadhyay, Saravanan Raju, Sangit Kumar|2022|Chem.Commun.|58|7050|doi:10.1039/D2CC02181A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bdqk1
title: CCDC 2157183: Experimental Crystal Structure Determination

description: Related Article: Monojit Batabyal, Saket Jain, Aditya Upadhyay, Saravanan Raju, Sangit Kumar|2022|Chem.Commun.|58|7050|doi:10.1039/D2CC02181A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bdql2
title: CCDC 2157184: Experimental Crystal Structure Determination

description: Related Article: Monojit Batabyal, Saket Jain, Aditya Upadhyay, Saravanan Raju, Sangit Kumar|2022|Chem.Commun.|58|7050|doi:10.1039/D2CC02181A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bdqm3
title: CCDC 2157185: Experimental Crystal Structure Determination

description: Related Article: Monojit Batabyal, Saket Jain, Aditya Upadhyay, Saravanan Raju, Sangit Kumar|2022|Chem.Commun.|58|7050|doi:10.1039/D2CC02181A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bdqp5
title: CCDC 2157187: Experimental Crystal Structure Determination

description: Related Article: Monojit Batabyal, Saket Jain, Aditya Upadhyay, Saravanan Raju, Sangit Kumar|2022|Chem.Commun.|58|7050|doi:10.1039/D2CC02181A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bdqq6
title: CCDC 2157188: Experimental Crystal Structure Determination

description: Related Article: Monojit Batabyal, Saket Jain, Aditya Upadhyay, Saravanan Raju, Sangit Kumar|2022|Chem.Commun.|58|7050|doi:10.1039/D2CC02181A
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.6571001
title: files associated with HR_1

description: Files associated with tagged-profilin manuscript.
description: added files related to experiments requested by reviewers
DOI: 10.5281/zenodo.5329584
title: files associated with HR_1

description: Files associated with tagged-profilin manuscript.
description: added files related to experiments requested by reviewers
DOI: 10.22008/fk2/1ibva9/enhlrc
title: DGU_KORT_SERIE_NR_31-2_TopZechstein_Depth.pdf

description: Kortblad - Map sheet
DOI: 10.22008/fk2/1ibva9/yclbrc
title: DGU_KORT_SERIE_NR_31-3_Triassic_IntervalVel.pdf

description: Kortblad - Map sheet
DOI: 10.22008/fk2/1ibva9/xgx1sz
title: DGU_KORT_SERIE_NR_31-4_TopZechstein_StructuralTime.pdf

description: Kortblad - Map sheet
DOI: 10.22008/fk2/1ibva9/ejgnhx
title: DGU_KORT_SERIE_NR_31-3_Triassic_Isochore.pdf

description: Kortblad - Map sheet
DOI: 10.5523/bris.3h319uol4k54e29s4bexynfxj9
title: Peradeniya self-poisoning data Jan 2019-Aug 2020

description: Data pertains to the data used for the following publication: Knipe et al 2021 Hospital presenting self-poisoning during COVID-19 in Sri Lanka: an interrupted time series analysis https://research-information.bris.ac.uk/en/publications/hospital-presenting-self-poisoning-during-covid-19-in-sri-lanka-a
DOI: 10.5281/zenodo.6675628
title: Nasal Naloxone for Opioid Overdoses

description: Fentanyl and Morphine doses with nasal naloxone intervention after 6 mins. Repeat nasal dose if O2 saturation falls below 0.9
DOI: 10.5281/zenodo.6675664
title: Butastur indicus SPAdes preassembly

description: .
DOI: 10.5281/zenodo.6675663
title: Butastur indicus SPAdes preassembly

description: .
DOI: 10.3886/e162281
title: Code for: Heterogeneous Global Booms and Busts

description: <pre>We investigate the heterogeneous boom and bust patterns across countries that emerge as a result of global shocks. Our analysis sheds light on the emergence of core and periphery countries, and the joint determination of the depth of recessions and tightness of credit across countries. The model implies that interest rates are similar across core and periphery countries in booms, with larger credit and output growth in periphery countries. However, a common global shock that leads to a credit crunch across the globe gives rise to a sharper spike in interest rates and a deeper recession in periphery countries, while a credit flight to the core alleviates the adverse consequences in these countries. We explore the implication of the model about credit spreads, portfolio rebalancing, investment, non-performing debt and concentration of debt ownership during booms and busts, both in the time series and in the cross-section, and connect them to existing stylized facts. We further demonstrate how the anatomy of the global economy evolves as a result of aggregate demand and supply shocks to financing, such as a global saving glut. </pre>
DOI: 10.3886/e162281v1
title: Code for: Heterogeneous Global Booms and Busts

description: <pre>We investigate the heterogeneous boom and bust patterns across countries that emerge as a result of global shocks. Our analysis sheds light on the emergence of core and periphery countries, and the joint determination of the depth of recessions and tightness of credit across countries. The model implies that interest rates are similar across core and periphery countries in booms, with larger credit and output growth in periphery countries. However, a common global shock that leads to a credit crunch across the globe gives rise to a sharper spike in interest rates and a deeper recession in periphery countries, while a credit flight to the core alleviates the adverse consequences in these countries. We explore the implication of the model about credit spreads, portfolio rebalancing, investment, non-performing debt and concentration of debt ownership during booms and busts, both in the time series and in the cross-section, and connect them to existing stylized facts. We further demonstrate how the anatomy of the global economy evolves as a result of aggregate demand and supply shocks to financing, such as a global saving glut. </pre>
DOI: 10.34740/kaggle/dsv/3841372
title: COVID Vaccination in World (updated daily)
DOI: 10.3886/e164661
title: Data and Code for: When Choices Are Mistakes

description: This contains the data and code to produce the results in When Choices Are Mistakes.
description: Response Rates: We collected 13 sessions in-person at Ohio State University's experimental economics lab for a total of 224 subjects. All subjects who were registered finished the experiment. We separately recruited 500 subjects on Prolific.
description: There are approximately 36,000 participants registered for experiments at OSU. We randomly invited individuals from this sample.<br><br>Subjects on Prolific were recruited through their random sampling.
description: on-site questionnaire;
DOI: 10.3886/e159061
title: Data and Code for: Factor Market Failures and the Adoption of Irrigation in Rwanda

description: Factor market failures can limit adoption of profitable technologies. We leverage a plot-level spatial regression discontinuity design in the context of irrigation use by farmers provided free access to water. Using irrigation boosts profits by 43-62%. Yet, farmers only irrigate 30% of plots because of labor costs. We demonstrate inefficient irrigation use, by showing farmers irrigating one plot reduce their irrigation use on other plots. This inefficiency is largest for smaller households and wealthier households, suggesting labor market frictions constrain use of irrigation.
DOI: 10.3886/e159061v1
title: Data and Code for: Factor Market Failures and the Adoption of Irrigation in Rwanda

description: Factor market failures can limit adoption of profitable technologies. We leverage a plot-level spatial regression discontinuity design in the context of irrigation use by farmers provided free access to water. Using irrigation boosts profits by 43-62%. Yet, farmers only irrigate 30% of plots because of labor costs. We demonstrate inefficient irrigation use, by showing farmers irrigating one plot reduce their irrigation use on other plots. This inefficiency is largest for smaller households and wealthier households, suggesting labor market frictions constrain use of irrigation.
DOI: 10.22008/fk2/i76ct6/mexoss
title: DGU_KORT_SERIE_NR_30-2_IntervalVel_LCret-Jur.pdf

description: Kortblad - Map sheet
DOI: 10.22008/fk2/i76ct6/xtue2m
title: DGU_KORT_SERIE_NR_30-3_Thickness_LCret-Jur.pdf

description: Kortblad - Map sheet
DOI: 10.22008/fk2/i76ct6/khcjmi
title: DGU_KORT_SERIE_NR_30-1_TopTrias_Detph.pdf

description: Kortblad - Map sheet
DOI: 10.22008/fk2/i76ct6/j0v7ig
title: DGU_KORT_SERIE_NR_30-2_TopTrias_Detph.pdf

description: Kortblad - Map sheet
DOI: 10.22008/fk2/i76ct6/kg74yv
title: DGU_KORT_SERIE_NR_30-1_TopTrias_TimeStructure.pdf

description: Kortblad - Map sheet
DOI: 10.22008/fk2/i76ct6/fsfmli
title: DGU_KORT_SERIE_NR_30-4_IntervalVel_LCret-Jur.pdf

description: Kortblad - Map sheet
DOI: 10.22008/fk2/i76ct6/abbiza
title: DGU_KORT_SERIE_NR_30-3_Thickness_LCret-Jur.pdf

description: :unav
DOI: 10.22008/fk2/i76ct6/z41ha6
title: DGU_KORT_SERIE_NR_30-3_Thickness_LCret-Jur-2.pdf

description: Kortblad - Map sheet