cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4319768
title: MFS-M-00157 Snow precipitation measured manually (Tretyakov rain gauge), raised bog (ridge-hollow complex), 2010-present

description: A raine gauge or Russian hydro-meteorological network standard (Tretyakov' construction) was installed as part of manual meteorological station in Mukhrino research polygon in 2010. The bucket installed at 2m height and protected by wind shield. Since 2014 only winter measurements were collected to supplement rain precipitation measurements measured in summer by automatic rain gauges (see also MFS-M-00154, MFS-M-00307, MFS-M-00308 for summer measurements in the same location).
DOI: 10.5281/zenodo.4319767
title: MFS-M-00157 Snow precipitation measured manually (Tretyakov rain gauge), raised bog (ridge-hollow complex), 2010-present

description: A raine gauge or Russian hydro-meteorological network standard (Tretyakov' construction) was installed as part of manual meteorological station in Mukhrino research polygon in 2010. The bucket installed at 2m height and protected by wind shield. Since 2014 only winter measurements were collected to supplement rain precipitation measurements measured in summer by automatic rain gauges (see also MFS-M-00154, MFS-M-00307, MFS-M-00308 for summer measurements in the same location).
DOI: 10.5281/zenodo.4319766
title: MFS-M-00156 Rain precipitation measured nearby Shapsha field station (in the village), HOBO rain gauge RG3-M, 2016-present

description: A raine gauge HOBO RG3-M was installed in Shapsha village in an open space (Shapsha field station) as part of macrofungi long-term plots monitoring in Shapsha vicinity. Installation on the ground level to prevent wind turbulence.
DOI: 10.5281/zenodo.4319765
title: MFS-M-00156 Rain precipitation measured nearby Shapsha field station (in the village), HOBO rain gauge RG3-M, 2016-present

description: A raine gauge HOBO RG3-M was installed in Shapsha village in an open space (Shapsha field station) as part of macrofungi long-term plots monitoring in Shapsha vicinity. Installation on the ground level to prevent wind turbulence.
DOI: 10.5517/ccdc.csd.cc24qpm6
title: CCDC 1988018: Experimental Crystal Structure Determination

description: Related Article: Ryo Ushiroguchi, Yoshiaki Shuku, Rie Suizu, Kunio Awaga|2020|Cryst.Growth Des.|20|7593|doi:10.1021/acs.cgd.0c01176
DOI: 10.5517/ccdc.csd.cc24qpl5
title: CCDC 1988017: Experimental Crystal Structure Determination

description: Related Article: Ryo Ushiroguchi, Yoshiaki Shuku, Rie Suizu, Kunio Awaga|2020|Cryst.Growth Des.|20|7593|doi:10.1021/acs.cgd.0c01176
DOI: 10.5517/ccdc.csd.cc24vkm6
title: CCDC 1991738: Experimental Crystal Structure Determination

description: Related Article: Eduard Badarau, K. Harsha Vardhan Reddy, Aurore Loudet, Charles Simon, Laurent Trembleau, Stijn Claerhout, Etienne Pair, Stéphane Massip, Philippe Breton, Brigitte Lesur, Solo Goldstein, Jean‐Marie Fourquez, Jean Michel Henlin, Léon Ghosez|2020|Chem.-Eur.J.|26|15477|doi:10.1002/chem.202002372
DOI: 10.5517/ccdc.csd.cc24vkn7
title: CCDC 1991739: Experimental Crystal Structure Determination

description: Related Article: Eduard Badarau, K. Harsha Vardhan Reddy, Aurore Loudet, Charles Simon, Laurent Trembleau, Stijn Claerhout, Etienne Pair, Stéphane Massip, Philippe Breton, Brigitte Lesur, Solo Goldstein, Jean‐Marie Fourquez, Jean Michel Henlin, Léon Ghosez|2020|Chem.-Eur.J.|26|15477|doi:10.1002/chem.202002372
DOI: 10.5517/ccdc.csd.cc24vkp8
title: CCDC 1991740: Experimental Crystal Structure Determination

description: Related Article: Eduard Badarau, K. Harsha Vardhan Reddy, Aurore Loudet, Charles Simon, Laurent Trembleau, Stijn Claerhout, Etienne Pair, Stéphane Massip, Philippe Breton, Brigitte Lesur, Solo Goldstein, Jean‐Marie Fourquez, Jean Michel Henlin, Léon Ghosez|2020|Chem.-Eur.J.|26|15477|doi:10.1002/chem.202002372
DOI: 10.5517/ccdc.csd.cc24vkq9
title: CCDC 1991741: Experimental Crystal Structure Determination

description: Related Article: Eduard Badarau, K. Harsha Vardhan Reddy, Aurore Loudet, Charles Simon, Laurent Trembleau, Stijn Claerhout, Etienne Pair, Stéphane Massip, Philippe Breton, Brigitte Lesur, Solo Goldstein, Jean‐Marie Fourquez, Jean Michel Henlin, Léon Ghosez|2020|Chem.-Eur.J.|26|15477|doi:10.1002/chem.202002372
DOI: 10.5517/ccdc.csd.cc24vkrb
title: CCDC 1991742: Experimental Crystal Structure Determination

description: Related Article: Eduard Badarau, K. Harsha Vardhan Reddy, Aurore Loudet, Charles Simon, Laurent Trembleau, Stijn Claerhout, Etienne Pair, Stéphane Massip, Philippe Breton, Brigitte Lesur, Solo Goldstein, Jean‐Marie Fourquez, Jean Michel Henlin, Léon Ghosez|2020|Chem.-Eur.J.|26|15477|doi:10.1002/chem.202002372
DOI: 10.5517/ccdc.csd.cc24vksc
title: CCDC 1991743: Experimental Crystal Structure Determination

description: Related Article: Eduard Badarau, K. Harsha Vardhan Reddy, Aurore Loudet, Charles Simon, Laurent Trembleau, Stijn Claerhout, Etienne Pair, Stéphane Massip, Philippe Breton, Brigitte Lesur, Solo Goldstein, Jean‐Marie Fourquez, Jean Michel Henlin, Léon Ghosez|2020|Chem.-Eur.J.|26|15477|doi:10.1002/chem.202002372
DOI: 10.5517/ccdc.csd.cc25k0vm
title: CCDC 2012360: Experimental Crystal Structure Determination

description: Related Article: Su San Jang, Young Ho Kim, So Won Youn|2020|Org.Lett.|22|9151|doi:10.1021/acs.orglett.0c02898
DOI: 10.5517/ccdc.csd.cc25k10t
title: CCDC 2012365: Experimental Crystal Structure Determination

description: Related Article: Su San Jang, Young Ho Kim, So Won Youn|2020|Org.Lett.|22|9151|doi:10.1021/acs.orglett.0c02898
DOI: 10.5517/ccdc.csd.cc25k11v
title: CCDC 2012366: Experimental Crystal Structure Determination

description: Related Article: Su San Jang, Young Ho Kim, So Won Youn|2020|Org.Lett.|22|9151|doi:10.1021/acs.orglett.0c02898
DOI: 10.5517/ccdc.csd.cc250h2s
title: CCDC 1996464: Experimental Crystal Structure Determination

description: Related Article: Raja Sekhar Voguri, Santosh Ranga, Archan Dey, Subhas Ghosal|2020|Cryst.Growth Des.|20|7647|doi:10.1021/acs.cgd.0c00752
DOI: 10.5517/ccdc.csd.cc250h3t
title: CCDC 1996465: Experimental Crystal Structure Determination

description: Related Article: Raja Sekhar Voguri, Santosh Ranga, Archan Dey, Subhas Ghosal|2020|Cryst.Growth Des.|20|7647|doi:10.1021/acs.cgd.0c00752
DOI: 10.6084/m9.figshare.13370768.v1
title: SIMPER analysis Want et al 2020

description: SIMPER Analysis for Want et al. 2020
DOI: 10.6084/m9.figshare.13370768
title: SIMPER analysis Want et al 2020

description: SIMPER Analysis for Want et al. 2020
DOI: 10.6084/m9.figshare.13370750
title: ANOSIM Want et al 2020.pdf

description: ANOSIM data set for Want et al 2020
DOI: 10.6084/m9.figshare.13370750.v1
title: ANOSIM Want et al 2020.pdf

description: ANOSIM data set for Want et al 2020
DOI: 10.6084/m9.figshare.13370756.v1
title: CP_data.xlsx

description: the description of this data can be found in the research article "Bootstrap simulations for evaluating the model estimation of the extent of cross-pollination in maize at the field-scale level"
DOI: 10.6084/m9.figshare.13370756
title: CP_data.xlsx

description: the description of this data can be found in the research article "Bootstrap simulations for evaluating the model estimation of the extent of cross-pollination in maize at the field-scale level"
DOI: 10.5281/zenodo.4086154
title: Impact of cropland physiology and phenology on watershed hydrology in a semi-arid watershed in the Pacific Northwest in a changing climate

description: The scripts and figures for the study. The scripts for creating the CLM5 case with transient CO2 concentration over UCPR watershed For the CO2 concentration and N deposition inputs, please find at https://svn-ccsm-inputdata.cgd.ucar.edu/trunk/inputdata/lnd/clm2/ inputdata: https://github.com/bwZh/SFA-CO2-effects-using-CLM5 domain data: domain.lnd.1kmx1km_UCPR_c20190605.nc. parameter data: clm5_params_calibration3_wwrainfed_UCPRvcmax.c171117.nc surface data: Please contact bowen.zhu@pnnl.gov or bwzhu@mail.bnu.edu.cn
DOI: 10.5281/zenodo.4086153
title: Impact of cropland physiology and phenology on watershed hydrology in a semi-arid watershed in the Pacific Northwest in a changing climate

description: The scripts and figures for the study. The scripts for creating the CLM5 case with transient CO2 concentration over UCPR watershed For the CO2 concentration and N deposition inputs, please find at https://svn-ccsm-inputdata.cgd.ucar.edu/trunk/inputdata/lnd/clm2/ inputdata: https://github.com/bwZh/SFA-CO2-effects-using-CLM5 domain data: domain.lnd.1kmx1km_UCPR_c20190605.nc. parameter data: clm5_params_calibration3_wwrainfed_UCPRvcmax.c171117.nc surface data: Please contact bowen.zhu@pnnl.gov or bwzhu@mail.bnu.edu.cn
DOI: 10.21227/s62n-fs84
title: CONFLICT SCENARIOS IN SMART HOMES

description: This is a repository of 102 smart home conflict scenarios, which were designated as conflict by actual human users. In other words, humans consider the scenarios below to be conflicts in a smart home environment. To see how to use this repository, and how the repository was collected, please read the following paper:Fereshteh Jadidi Miandashti, Mohammad Izadi, Ali Asghar Nazari Shirehjini, and Shervin Shirmohammadi, “An Empirical Approach to Modeling User-System Interaction Conflicts in Smart Homes”, IEEE Trans. on Human-Machine Systems, 2020.Please note that the scenarios in our repository help identify what is conflict from the users’ perspective. They do not tell us how to handle those conflicts. In other words, the cases below are about conflict detection, not conflict resolution.This repository can help designers of Smart Homes understand what rules (system behaviors) were perceived by users as conflict, so they can design appropriate conflict detection and conflict resolution modules for their Smart Home system. For example, in one scenario, the user might open the window to let in the fresh air, but the user has forgotten that his pet bird is roaming free in the room and might escape from the window! The system knows the latter fact and closes the window immediately. Our Smart Home users considered this scenario to be a conflict. A designer might come across a scenario similar to this. In this case, the designer can use our repository to (1) understand that his/her scenario, which is very similar to one of the scenarios in our repository, is indeed considered a conflict by smart home users; hence the designer can investigate it subjectively or with some well-known techniques, and (2) design appropriate conflict detection and resolution rules for it in order to increase user satisfaction. Of course, our repository does not contain all possible conflict scenarios in a smart home, but it gives a fairly good starting point. The scenarios were translated to English by the researchers and the table is not listing duplicate entries.
DOI: 10.6084/m9.figshare.13317686
title: Interactive effects of temperature, water, and nitrogen availability on the growth, floral traits, and pollination of white mustard, Sinapis alba

description: Data accompanying the following manuscript:Akter A. & Klečka J. (2020) Interactive effects of temperature, water, and nitrogen availability on the growth, floral traits, and pollination of white mustard, <i>Sinapis alba</i>.<br>
DOI: 10.5519/qd.7j8muq2g
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.21227/npa9-y007
title: CONFLICT SCENARIOS IN SMART HOMES

description: This is a repository of 102 smart home conflict scenarios, which were designated as conflict by actual human users. In other words, humans consider the scenarios below to be conflicts in a smart home environment. To see how to use this repository, and how the repository was collected, please read the following paper:Fereshteh Jadidi Miandashti, Mohammad Izadi, Ali Asghar Nazari Shirehjini, and Shervin Shirmohammadi, “An Empirical Approach to Modeling User-System Interaction Conflicts in Smart Homes”, IEEE Trans. on Human-Machine Systems, 2020.Please note that the scenarios in our repository help identify what is conflict from the users’ perspective. They do not tell us how to handle those conflicts. In other words, the cases below are about conflict detection, not conflict resolution.This repository can help designers of Smart Homes understand what rules (system behaviors) were perceived by users as conflict, so they can design appropriate conflict detection and conflict resolution modules for their Smart Home system. For example, in one scenario, the user might open the window to let in the fresh air, but the user has forgotten that his pet bird is roaming free in the room and might escape from the window! The system knows the latter fact and closes the window immediately. Our Smart Home users considered this scenario to be a conflict. A designer might come across a scenario similar to this. In this case, the designer can use our repository to (1) understand that his/her scenario, which is very similar to one of the scenarios in our repository, is indeed considered a conflict by smart home users; hence the designer can investigate it subjectively or with some well-known techniques, and (2) design appropriate conflict detection and resolution rules for it in order to increase user satisfaction. Of course, our repository does not contain all possible conflict scenarios in a smart home, but it gives a fairly good starting point. The scenarios were translated to English by the researchers and the table is not listing duplicate entries.
DOI: 10.17632/4mpsjpgpc2.1
title: Data for: Quantifying the performance of dual-use rainwater harvesting systems

description: Code for the determination of rainwater harvesting performance metrics.
DOI: 10.17632/nwmyvyyy2v.12
title: Data for: Predicting the Impacts of Mutations on Protein-Ligand Affinity Based on Molecular Dynamics Simulations and Machine Learning Methods

description: Statistics of the original data set, including the mutation type, the ID of the corresponding mutant in the protein data bank (PDB), the PDB ID of the wild type protein, the ID of the ligand, the type of ligand-binding sites (multiple or single), the impact of mutation on the protein-ligand affinity (decreased or increased) and the affinity fold change. For mutation types, XIY means a substitution from residue X to residue Y at position I, and XIY/AIIB indicates a dual mutation at positions I and II.
DOI: 10.5281/zenodo.4319802
title: Dataset-AOB: urban sounds events classification

description: The dataset Dataset-AOB is an audio dataset collected and manually edited for urban sounds events classification using Convolutional Neural Networks for the Master Thesis: Ospina, A. "Audio Event Classification using Deep Learning. Use case: Urban Sounds Events classification with Convolutional Neural Networks," M.Eng. thesis, Beuth University of Applied Sciences, Berlin, 2020. - 10 audio events: alarm-siren, children playing, dog bark, engine, footsteps, glass breaking, gun shot, metro train, rain and screams. - duration: &lt; 4 seconds - format: (.wav) - sampling rate: 22KHz - 44KHz - files: Dataset-AOB: development dataset (4831 samples), DatasetEVAL-AOB: evaluation (218 samples) - metadata: (.csv) - sources per class: (.png) Contact: aospinab@gmail.com
description: {"references": ["T. Spadini. (2019). Sound Events for Surveillance Applications (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3519845", "P. Foggia, N. Petkov, A. Saggese, N. Strisciuglio, M. Vento. \"Reliable Detection of Audio Events in Highly Noisy Environments.\" Pattern Recognition Letters, Available online 9 July 2015, ISSN 0167-8655, http://dx.doi.org/10.1016/j.patrec.2015.06.026.", "J. Salamon, C. Jacoby, J. P. Bello, \"A Dataset and Taxonomy for Urban Sound Research\", 22nd ACM International Conference on Multimedia, Orlando USA, Nov 2014, doi: 10.1145/2647868.2655045", "A. Diment, A. Mesaros,T. Heittola, T. Virtanen. (2017). TUT Rare sound events, Development dataset [Data set]. Zenodo. http://doi.org/10.5281/zenodo.401395", "Still North Media libraries. The firearm sound library. Accessed on: Nov. 2, 2020. [Online]. Available: https://www.stillnorthmedia.com/libraries/", "Listis: Rouen Audio scene dataset. Accessed on: Jul. 2, 2020. [Online]. Available: https://sites.google.com/site/alainrakotomamonjy/home/audio-scene", "A. Mesaros, T. Heittola, T. Virtanen. (2017). TUT Acoustic scenes 2017, Development dataset [Data set]. Zenodo. http://doi.org/10.5281/zenodo.400515", "A. Diment, A. Mesaros,T. Heittola, T. Virtanen. (2017). TUT Rare sound events, Development dataset [Data set]. Zenodo. http://doi.org/10.5281/zenodo.401395", "Trowitzsch, J. Taghia,Y. Kashef, K. Obermayer. (2019). NIGENS general sound events database [Data set]. Zenodo. http://doi.org/10.5281/zenodo.2535878", "E. Fonseca, M. Collado, M. Plakal, D. P. W. Ellis, F. Font, X. Favory, X. Serra. (2019). FSDnoisy18k (Version 1.0) [Data set]. Zenodo. doi: http://doi.org/10.5281/zenodo.2529934"]}
DOI: 10.5281/zenodo.4319801
title: Dataset-AOB: urban sounds events classification

description: The dataset Dataset-AOB is an audio dataset collected and manually edited for urban sounds events classification using Convolutional Neural Networks for the Master Thesis: Ospina, A. "Audio Event Classification using Deep Learning. Use case: Urban Sounds Events classification with Convolutional Neural Networks," M.Eng. thesis, Beuth University of Applied Sciences, Berlin, 2020. - 10 audio events: alarm-siren, children playing, dog bark, engine, footsteps, glass breaking, gun shot, metro train, rain and screams. - duration: &lt; 4 seconds - format: (.wav) - sampling rate: 22KHz - 44KHz - files: Dataset-AOB: development dataset (4831 samples), DatasetEVAL-AOB: evaluation (218 samples) - metadata: (.csv) - sources per class: (.png) Contact: aospinab@gmail.com
description: {"references": ["T. Spadini. (2019). Sound Events for Surveillance Applications (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3519845", "P. Foggia, N. Petkov, A. Saggese, N. Strisciuglio, M. Vento. \"Reliable Detection of Audio Events in Highly Noisy Environments.\" Pattern Recognition Letters, Available online 9 July 2015, ISSN 0167-8655, http://dx.doi.org/10.1016/j.patrec.2015.06.026.", "J. Salamon, C. Jacoby, J. P. Bello, \"A Dataset and Taxonomy for Urban Sound Research\", 22nd ACM International Conference on Multimedia, Orlando USA, Nov 2014, doi: 10.1145/2647868.2655045", "A. Diment, A. Mesaros,T. Heittola, T. Virtanen. (2017). TUT Rare sound events, Development dataset [Data set]. Zenodo. http://doi.org/10.5281/zenodo.401395", "Still North Media libraries. The firearm sound library. Accessed on: Nov. 2, 2020. [Online]. Available: https://www.stillnorthmedia.com/libraries/", "Listis: Rouen Audio scene dataset. Accessed on: Jul. 2, 2020. [Online]. Available: https://sites.google.com/site/alainrakotomamonjy/home/audio-scene", "A. Mesaros, T. Heittola, T. Virtanen. (2017). TUT Acoustic scenes 2017, Development dataset [Data set]. Zenodo. http://doi.org/10.5281/zenodo.400515", "A. Diment, A. Mesaros,T. Heittola, T. Virtanen. (2017). TUT Rare sound events, Development dataset [Data set]. Zenodo. http://doi.org/10.5281/zenodo.401395", "Trowitzsch, J. Taghia,Y. Kashef, K. Obermayer. (2019). NIGENS general sound events database [Data set]. Zenodo. http://doi.org/10.5281/zenodo.2535878", "E. Fonseca, M. Collado, M. Plakal, D. P. W. Ellis, F. Font, X. Favory, X. Serra. (2019). FSDnoisy18k (Version 1.0) [Data set]. Zenodo. doi: http://doi.org/10.5281/zenodo.2529934"]}
DOI: 10.5281/zenodo.3985807
title: A dataset of media releases (Twitter, News and Comments, Youtube, Facebook) from Poland related to COVID-19 for open research

description: Social behavior has a fundamental impact on the dynamics of infectious diseases (such as COVID-19), challenging public health mitigation strategies and possibly the political consensus. The widespread use of the traditional and social media on the Internet provides us with an invaluable source of information on societal dynamics during pandemics. With this dataset, we aim to understand mechanisms of COVID-19 epidemic-related social behavior in Poland deploying methods of computational social science and digital epidemiology. We have collected and analyzed COVID-19 perception on the Polish language Internet during 15.01-31.07(06.08) and labeled data quantitatively (Twitter, Youtube, Articles) and qualitatively (Facebook, Articles and Comments of Article) in the Internet by infomediological approach. - manually labeled1,449 articles / Facebook posts from Lower Silesia (facebook_articles_lower_silesia.zip) and 111 texts from outside this region; -extracted 57,306 representative articles (articles_till_06_08.zip) in Polish using Eventregitry.org tool in language Polish and topic "Coronavirus" in article body; - extracted 1,015,199 (tweets_till_31_07_users.zip and tweets_till_31_07_text.zip) and Tweets from #Koronawirus in language Polish usiing Twitter API. - collected 1,574 videos (youtube_comments_till_31_07.zip and youtube_movie.csv) with keyword: Koronawirus on YouTube and 247,575 comments on them using Google API; - We supplemented the media observations with an analysis of 244 social empirical studies till 25.05 on COVID-19 in Poland (empirical_social_studies.csv). Reports and analyzes and coding books can be found in Polish at: http://www.infodemia-koronawirusa.pl
description: {"references": ["Jarynowski A, W\u00f3jta-Kempa M, Belik V. TRENDS IN PERCEPTION OF COVID-19 IN POLISH INTERNET, Polish Epidemiological Review", "Jarynowski A, W\u00f3jta-Kempa M, P\u0142atek D, Krzowski \u0141, Belik V. Spatial Diversity of COVID-19 Cases in Poland Explained by Mobility Patterns - Preliminary Results 2020; http://dx.doi.org/10.2139/ssrn.3621152"]}
DOI: 10.7910/dvn/iwxkdk
title: Executive Agreements Database, Statement Regarding the Memorandum of Understanding with China for Cooperation in the use of U.S. Land Remote Sensing Satellite Data, with Annexes. Signed At Reston and Beijing October 10 and 20, 2012. Entered in force October 20, 2012.

description: TIAS 12-1020 cover memo
DOI: 10.34740/kaggle/dsv/1740371
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.18112/openneuro.ds003420.v1.0.2
title: Dataset 1
DOI: 10.5281/zenodo.4319710
title: SCOPE VLP abundance

description: Whole sea water collected and filtered with 0.22µm sytinge filter. Samples were then frooze in-80 and kept for analysis.For analysis, samples were filter on 0.02µm anodisc filter, stained with sybr green II and visualized using epiflourcence microscope.
DOI: 10.5281/zenodo.4319709
title: SCOPE VLP abundance

description: Whole sea water collected and filtered with 0.22µm sytinge filter. Samples were then frooze in-80 and kept for analysis.For analysis, samples were filter on 0.02µm anodisc filter, stained with sybr green II and visualized using epiflourcence microscope.
DOI: 10.18112/openneuro.ds003421.v1.0.2
title: Dataset 2
DOI: 10.5281/zenodo.4319552
title: Water stable isotope data for the Ogooué River (Gabon) at Lambaréné, 2012-2014.

description: This dataset contains hydrogen and oxygen stable isotope data on surface water samples collected on the Ogooué River at Lambaréné (Gabon) between April 2012 and April 2014.
DOI: 10.5281/zenodo.4319551
title: Water stable isotope data for the Ogooué River (Gabon) at Lambaréné, 2012-2014.

description: This dataset contains hydrogen and oxygen stable isotope data on surface water samples collected on the Ogooué River at Lambaréné (Gabon) between April 2012 and April 2014.
DOI: 10.5281/zenodo.4319946
title: Reactome

description: The Reactome dataset originates from the website https://reactome.org/ where it can be downloaded as Neo4j graph. This graph has been subsequently imported to Neo4j and in turn exported as RDF dump file in Turtle format which is now hosted on this site. The dataset contains information regarding biological pathways, i.e., signal transduction, genetics and metabolism.
DOI: 10.6084/m9.figshare.13370801
title: Completo Base consolidado.xlsx

description: Dataset including genetics and clinical response to chemotherapy in patients diagnosed with hematologic malignacies.