Find research datasets worth reusing

Figure 12 from: Hou L, Hernández-Restrepo M, Groenewald JZ, Cai L, Crous PW (2020) Citizen science project reveals high diversity in Didymellaceae (Pleosporales, Dothideomycetes). MycoKeys 65: 49-99. https://doi.org/10.3897/mycokeys.65.47704

Figure 12 Vandijckomycella snoekiae (CBS 144954). A, B Colony on OA (front and reverse) C, D colony on MEA (front and reverse) E, F colony on PDA (front and reverse) G, H pycnidia forming on OAI, J section of pycnidial wall K–N conidiogenous cells O conidia. Scale bars: 100 μm (H); 50 μm (I); 10 μm (J); 5 μm (K–O).

Figure 11 from: Hou L, Hernández-Restrepo M, Groenewald JZ, Cai L, Crous PW (2020) Citizen science project reveals high diversity in Didymellaceae (Pleosporales, Dothideomycetes). MycoKeys 65: 49-99. https://doi.org/10.3897/mycokeys.65.47704

Figure 11 Vandijckomycella joseae (CBS 143011). A, B Colony on OA (front and reverse) C, D colony on MEA (front and reverse) E, F colony on PDA (front and reverse) G, H pycnidia forming on OAI, J section of pycnidial wall K–N conidiogenous cells O conidia. Scale bars: 100 μm (H); 20 μm (I); 10 μm (J); 5 μm (K–O).

Figure 1 from: Hou L, Hernández-Restrepo M, Groenewald JZ, Cai L, Crous PW (2020) Citizen science project reveals high diversity in Didymellaceae (Pleosporales, Dothideomycetes). MycoKeys 65: 49-99. https://doi.org/10.3897/mycokeys.65.47704

Figure 1 Phylogenetic tree generated from the maximum-likelihood analysis based on the combined ITS, LSU, tub2 and rpb2 sequence alignment of Didymellaceae members. The RAxML bootstrap support values (BS), Bayesian posterior probabilities (PP), and parsimony bootstrap support values (PBS) are given at the nodes (BS/PP/PBS). BS and PBS values represent parsimony bootstrap support values >50 %. Full supported branches are indicated in bold. The scale bar represents the expected number of changes per site. Ex-type strains are represented in bold. Strains obtained in the current study are printed in green; among them, whilst strains that represent new taxa are printed in red. Some of the basal branches were shortened to facilitate layout (the fraction in round parentheses refers to the presented length compared to the actual length of the branch). The tree was rooted to Coniothyrium palmarum CBS 400.71 and Leptosphaeria doliolum CBS 505.75.

Figure 2 from: Hou L, Hernández-Restrepo M, Groenewald JZ, Cai L, Crous PW (2020) Citizen science project reveals high diversity in Didymellaceae (Pleosporales, Dothideomycetes). MycoKeys 65: 49-99. https://doi.org/10.3897/mycokeys.65.47704

Figure 2 Ascochyta benningiorum (CBS 144957). A, B Colony on OA (front and reverse) C, D colony on MEA (front and reverse) E, F colony on PDA (front and reverse) G pycnidia forming on OAH pycnidium I section of pycnidium J section of pycnidial wall K–M conidiogenous cells N conidia. Scale bars: 100 μm (H, I); 10 μm (J); 5 μm (K–N).

opennotspecifiedMay 2015View details →

Figure 5 in The five deadly sins of science publishing

Figure 5. Summary of the F1000Research open science publishing process.

Supplementary Table 1. Characteristics of the Group 1 data journals that publish in the fields of biology, environmental science, chemistry, medicine, and health sciences

Supplementary Table 1.  Characteristics of the Group 1 data journals that publish in the fields of biology, environmental science, chemistry, medicine, and health sciences. This file is a digital supplement to William H. Walters, "Data journals: incentivizing data access and documentation within the scholarly communication system," Insights: The UKSG Journal 33, article 18 (June 10, 2020), 1–20, <a href="http://doi.org/10.1629/uksg.510">http://doi.org/10.1629/uksg.510</a>.

opencc-by-4.0Apr 2020View details →

Figure with the slogan "Patience and Science"

In this quarantine period what the world needs "Patience and science".  We have invented this slogan to encourage the scientific community.

opencc-by-4.0Apr 2020View details →

Regionalized Cultural Access and Participation (Books And Libraries) And Science Attitudes Variables (2013)

This dataset was created from the microdata of the Eurobarometer 79.2 survey using the development version of the eurobarometer package. The read a book variable is a weighted sum of the responses that chose from `QB1 How many times in the last twelve months have you read a book?` any answer apart from "not in the last 12 months." The library access variable is a weighted sum of the responses that chose from `QB1 How many times in the last twelve months have you visited a public libarary?` any answer apart from "not in the last 12 months. The limited library access is a weighted sum of the responses that chose from the question block `QB2 And for each of the following activities, please tell me why you haven’t done it or haven’t done it more often in the last 12 months? ... Visited a public library` the answer option `Limited or poor quality of this activity in the place where you live.` In this case, the number of respondents is rather low and this is not a very reliable statistic on regional level. The supports open access variable is a weighted sum of yes answer options to the `QD 17 Do you think that the results of publicly funded research should be made available online free of charge?` question. The internet access question is a weighted sum of responses to the answer option for `D46 Which of the following do you have? - An Internet connection at home`. The internet access question is a weighted sum of responses to the answer option for `D15 What is your current occupation? - student`.

opencc-by-4.0Apr 2020View details →

Supporting Data for "The extent of counterion dissociation at the interface of cationic diblock copolymer nanoparticles in non-polar solvents" (Journal of Colloid and Interface Science, 2020)

NMR measurement files (1D 19F, 1H diffusion, and 19F diffusion) as specified in the filename on dispersions of diblock copolymer nano-particles as specified in the filename.

opencc-by-4.0May 2020View details →

Figure 1 from: Samuel S, Shadaydeh M, Böcker S, Brügmann B, Bucher SF, Deckert V, Denzler J, Dittrich P, von Eggeling F, Güllmar D, Guntinas-Lichius O, König-Ries B, Löffler F, Maicher L, Marz M, Migliavacca M, R. Reichenbach J, Reichstein M, Römermann C, Wittig A (2020) A virtual "Werkstatt" for digitization in the sciences. Research Ideas and Outcomes 6: e54106. https://doi.org/10.3897/rio.6.e54106

Figure 1 Integration of the "Werkstatt" in the Friedrich Schiller University and the research center of Jena.

opencc-by-4.0May 2020View details →

Webinar on the Characteristics of Citizen Science

What is Citizen Science? What is not? Learn more about the characteristics of citizen science and why it is important to define them. On May 27th, 2020, <a href="https://ecsa.citizen-science.net/">ECSA</a> and <a href="https://eu-citizen.science/">EU-Citizen.Science</a> co-hosted a webinar about the recently published ‘<a href="https://ecsa.citizen-science.net/sites/default/files/ecsa_characteristics_of_citizen_science_-_v1_final.pdf">Characteristics of citizen science</a>’. This document, and the accompany notes, is the output of a community-based effort to outline what can be characterised as citizen science. Led by members of the working group that wrote this document, the webinar is an opportunity to learn more about its content, why defining characteristics of citizen science was necessary, and how it will benefit the field.  <a href="https://www.youtube.com/watch?v=Apuw-BbYMTA&list=PLB6IBD9OG9pD_pm7lKnyA_rNewuweFBk7&index=5&t=2s&ab_channel=ECSA-EuropeanCitizenScienceAssociation">WATCH THE WEBINAR RECORDING ONLINE</a> We used the hashtag #CitSciWebinar to tweet about the webinar, and you can continue to use this after the event to share it and ask us questions. <a href="https://eu-citizen.science/blog/2020/06/11/characteristics-QandA/">READ MORE Q & A FROM THE WEBINAR</a>   Speakers <a href="https://www.linkedin.com/authwall?trk=ripf&trkInfo=AQEvx3Y5UusbeQAAAXIxmpXQgqkMD-5awDPe_6gevrhWloVmDB2YrvQwsmPQCqNIgiLm4EHxziI2rtF5g1U34Ge_qmSTgFMI7HHqbQZPT43bGGi4l8SbG4BKgDNMpawIeNmX934=&originalReferer=https://www.google.com/&sessionRedirect=https%3A%2F%2Fbe.linkedin.com%2Fin%2Fcolombe-warin-1913711">Colombe Warin</a>, project advisor at the European Commission <a href="https://www.geog.ucl.ac.uk/people/academic-staff/muki-haklay">Muki Haklay</a>, co-director at ExCiteS at University College London and vice-chair of ECSA <a href="https://www.ufz.de/index.php?en=40346">Susanne Hecker</a>, research associate at the Helmholtz Centre for Environmental Research, and a member of ECSA’s board of directors <a href="https://www.museumfuernaturkunde.berlin/en/taxonomy/term/234/maike.weisspflug">Maike Weißpflug</a>, researcher at the Museum für Naturkunde Berlin   Mediator <a href="https://www.researchgate.net/profile/Margaret_Gold2">Margaret Gold</a>, project officer for ECSA

The rat brain transcripts after cocaine abstinence and relapse: data from the Maj Institute of Pharmacology Polish Academy of Sciences

Substantially altered transcripts in the rat PFC during early (3. day) cocaine abstinence with extinction training identify by microarray analysis – supplementary material – Neurotoxicity research, 2017. Supported by the National Science Centre grant no. UMO-2012/06/A/NZ3/00022

Investing in Open Science infrastructure and repositories, shared repositories and Idea Challenge 2020-2021

Recording of the OR2020 virtual session that includes: <ul> <li>Investing in Open Science infrastructure and repositories: How IOI and SCOSS can help – Vanessa Proudman, SPARC Europe</li> <li>Shared repositories: building a multi-tenancy repository service at the British Library – Sara Gould, the British Library</li> <li>A year long Idea Challenge: the new beginning. Kicking-off Idea Challenge 2020-2021, for more information, please visit the <a href="https://or2020.sun.ac.za/a-year-long-idea-challenge/">Idea Challenge page</a>.</li> </ul>

opencc-by-4.0May 2020View details →

Marine Flatworms (Polycladida) from the Gulf of Mexico and Mexican Caribbean at the "Colección Regional de Policládidos de la Península de Yucatán" at Faculty of Science, UNAM

The dataset contains information about the collection “Policládidos de la Península de Yucatán”  at the Unidad Multidisciplinaria de Docencia e Investigación, Faculty of Science, UNAM. This dataset represents only specimens of Order Polycladida identified to genus and species (122 specimens) from 12 localities of coastal lagoons and coral reefs at the Gulf of Mexico and Mexican Caribbean for a period of time from 2017 to 2019.  The dataset contains information about specimens belonging to 12 families, 17 genera, and 19 species.  The specimens at the collection have been either identified or validated by professional taxonomists, providing a significant level of trust in the data. The taxonomists that participated in this process appear in the list of authors

Materials Data Science for Microstructural Characterization of Archaeological Concrete

<ul> <li>Ancient Roman concrete: volcanic ash, lime, seawater, and volcanic rocks;  </li> <li> Baiae concrete was constructed about 55 BCE to 115 CE, and samples were obtained by the Roman Maritime Concrete Survey (ROMACONS) [12] drilling program from 2002 to 2006, which has been the most comprehensive study of Roman marine concrete. The field program extracted core samples from eleven locations throughout the Mediterranean; </li> <li> Imaging performed at ALS, LBNL. </li> </ul>

Data and code for Chong, Moroni, et al. (2020) Science: "Manipulating synthetic optogenetic odors reveals the coding logic of olfactory perception"

Behavioral response dataset, mitral cell responses, and code to implement the STM model in Chong, Moroni, Wilson, Shoham, Panzeri and Rinberg (2020) Science.  Please contact Edmund Chong (edmund.w.chong@gmail.com) and Monica Moroni (monica.moroni@outlook.com) for questions

OHBM Open Science Special Interest Group Symposium - Towards Global Inclusivity in Open Science

Talk presented as part of the Organisation of Human Brain Mapping (OHBM) Open Science Special Interest Group Symposium on  Collaborative Science. Features interviews with Researchers connected to the Global South, describing some of the challenges they face around interacting with open science coming from the Global North.   Features excerpts from interviews with: Sudarshan GC; Link: https://medialabnepal.com; Twitter: @Sudarsh07847591 Lilian Juma; Link: https://www.kijijieco.org/; Link: https://github.com/Lilian9/Open_Connect_Kenya; Twitter: @TheOnlyJuma Stephan Heunis; Link: https://jsheunis.github.io; Twitter: @fmrwhy Allan Ochola; Twitter: @allanochola Festus Nyasimi; Link: https://bioinformaticshubofkenya.wordpress.com/; Twitter: @Festus_nyasimi Julieta Arancio; Link: https://blog.aranc.io/; Twitter: @Cassandreces Malvika Sharan; Link: https://the-turing-way.netlify.com; Twitter: @malvikasharan

Life Sciences dataset used in INFORE project, part 1

Life Sciences dataset used in INFORE project, part 1 The dataset comprises the output of several simulations of a model of tumor growth with different parameter values. The model is a multi-scale agent-based model of a tumor spheroid that is treated with periodic pulses of the cytokine tumor necrosis factor (TNF). The multi-scale model simulates processes including i) the diffusion, uptake, and secretion of molecular entities such as oxygen, or TNF; ii) the mechanical interaction between cells; and iii) cellular processes including cell life cycle, cell death models, signal transduction. The multi-scale model was implemented and simulated using the PhysiBoSS framework (Letort et al. 2019). The dataset corresponds to different examples of parameters combinations of our use case that correspond to the different panels of Figure 4 in Documentation folder. This figure comes from the paper in the same folder. You can find a broad discussion of our use case in the Biological Use Case Documentation file. Also, The results of the cell simulations can be found in example_XXX/run0/outputs. The results of the microenvironment simulations can be found in example_XXX/run0/microutputs. Details on how these files are built can be found in Biological Use Case output format file (which is a snippet of the broad documentation file that I detached for your convenience). Briefly: each time step defined, the software writes an output and microutput file. For instance, ecm_t00030.txt correspond to time step 30. Each line of these files corresponds to a cell or microenvironment entity (oxygen, TNF, etc). Columns are defined by the first row for output folder. For the microutputs, the first three columns correspond to spatial coordinates and the fourth to the value of the density. The examples are: - example_spheroid_TNF_nopulse: corresponds to Figure 4 A. - example_spheroid_TNF_onepulse: corresponds to Figure 4 C. - example_spheroid_TNF_pulse150: corresponds to Figure 4 D left. This is the simulation outcome desired: proliferative cells die out with increasing number of pulses of TNF. - example_spheroid_TNF_pulse600: corresponds to Figure 4 D right. - example_spheroid_TNF_pulsecont: corresponds to Figure 4 B. - example_cells_with_ECM_mutants: does NOT correspond to Figure 4. This is an example in which microutput folder is full of two entities: oxygen and ECM. Also, in this example you can find a folder (ECM_mut) with the kind of visualisation that we perform to showcase results. - example_spheroid_TNF_pulsecont_oxy: 21 simulations with slightly different oxygen tolerance conditions using as a base the simulation with one continuous pulse (Figure 4 B from the presentation). The only difference among parameters file is the "oxygen_necrotic" value, which controls the threshold above which cells commit to necrosis due to lack of oxygen. In the original simulation this value was zero and the maximum available oxygen is 40 fg/µm^3. Here, we have studied the parameter value from 0 to 40 in steps of 5.

New Data Types in Social Science Research and Data Archives

During the CESSDA event "Strengthening and Widening of the European Infrastructure of Social Science Data Archives" in Skopje, North Macedonia  on 5-6 November 2019, Libby Bishop presented on New Data Types in Social Science Research and Data Archives.  The talk addressed four questions: What are researchers doing with social media and other data? What are repositories currently doing to hold and share new forms data? Are there useful resources for repository staff, and what next steps are planned? What are repositories’ responsibilities in the broader debates?  It is challenging to share data (consistent with Open Science and FAIR principles) while also meeting obligations to obey laws and protect confidentiality. Whereas the ethical procedures for “researcher-generated” data, such as surveys and interviews are well-established, no such consensus exists for social media and other data-in-the-wild.  Even the basic conception of whether virtual spaces are public or private remains contested among researchers and the public.  The talk delineated the key ethical issues of this debate and described practical solutions that some data repositories currently offer. To date, these solutions strictly interpret legal restrictions placed on data sharing.    Alongside the presentation, also a video introducing these issues is available. The video is also available for <a href="https://youtu.be/GvirMb1vmww">viewing on Youtube</a>.

Life Sciences dataset used in INFORE project, part 2

Life Sciences dataset used in INFORE project, part 2 The dataset comprises the output of several simulations of a model of tumor growth with different parameter values. The model is a multi-scale agent-based model of a tumor spheroid that is treated with periodic pulses of the cytokine tumor necrosis factor (TNF). The multi-scale model simulates processes including i) the diffusion, uptake, and secretion of molecular entities such as oxygen, or TNF; ii) the mechanical interaction between cells; and iii) cellular processes including cell life cycle, cell death models, signal transduction. The multi-scale model was implemented and simulated using the PhysiBoSS framework (Letort et al. 2019). The dataset corresponds to different examples of parameters combinations of our use case that correspond to the different panels of Figure 4 in Documentation folder. This figure comes from the paper in the same folder. You can find a broad discussion of our use case in the Biological Use Case Documentation file. Also, The results of the cell simulations can be found in example_XXX/run0/outputs. The results of the microenvironment simulations can be found in example_XXX/run0/microutputs. Details on how these files are built can be found in Biological Use Case output format file (which is a snippet of the broad documentation file that I detached for your convenience). Briefly: each time step defined, the software writes an output and microutput file. For instance, ecm_t00030.txt correspond to time step 30. Each line of these files corresponds to a cell or microenvironment entity (oxygen, TNF, etc). Columns are defined by the first row for output folder. For the microutputs, the first three columns correspond to spatial coordinates and the fourth to the value of the density. The examples are: - example_spheroid_TNF_nopulse: corresponds to Figure 4 A. - example_spheroid_TNF_onepulse: corresponds to Figure 4 C. - example_spheroid_TNF_pulse150: corresponds to Figure 4 D left. This is the simulation outcome desired: proliferative cells die out with increasing number of pulses of TNF. - example_spheroid_TNF_pulse600: corresponds to Figure 4 D right. - example_spheroid_TNF_pulsecont: corresponds to Figure 4 B. - example_cells_with_ECM_mutants: does NOT correspond to Figure 4. This is an example in which microutput folder is full of two entities: oxygen and ECM. Also, in this example you can find a folder (ECM_mut) with the kind of visualisation that we perform to showcase results. - example_spheroid_TNF_pulsecont_oxy: 21 simulations with slightly different oxygen tolerance conditions using as a base the simulation with one continuous pulse (Figure 4 B from the presentation). The only difference among parameters file is the "oxygen_necrotic" value, which controls the threshold above which cells commit to necrosis due to lack of oxygen. In the original simulation this value was zero and the maximum available oxygen is 40 fg/µm^3. Here, we have studied the parameter value from 0 to 40 in steps of 5.