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6,025 results for “Science of science”
Science Communication Webinar Series: Talking to the Media
<p>Blue-Action coordinated a series of webinars on climate science communication in 2020. These were aimed at early-career researchers, but anyone who wishes to engage people with their research at any stage will find them valuable.</p> <p>This webinar was delivered on 19th June 2020 by <a href="https://www.sams.ac.uk/people/professional-services/paterson-euan/">Euan Paterson</a>, who is the Communications and Media Officer at the <a href="https://www.sams.ac.uk/">Scottish Association for Marine Science.</a> Euan is trained in journalism, and spent over a decade working in print media, before moving to work at the Institute. He brought his years of experience on both sides of the relationship to the webinar, sharing some insights and ideas on how to engage with the media.</p>
Science Communication Webinar Series: Designing Infographics Presentation
<p>Blue-Action coordinated a series of webinars on climate science communication in 2020. These were aimed at early-career researchers, but anyone who wishes to engage people with their research at any stage will find them valuable.</p> <p>This webinar was delivered on 17th July 2020 by <a href="https://www.sams.ac.uk/people/professional-services/harvey-will/#:~:text=Will%20Harvey&text=As%20a%20member%20of%20SAMS,intranet%20and%20asset%20management%20tools.&text=will.harvey%40sams.ac,%2B44%20(0)1631%20559327">Will Harvey</a>, who is a web and graphic designer at the <a href="https://www.sams.ac.uk/">Scottish Association for Marine Science.</a> Will spent over a decade as a graphic designer in industry, before moving to academia. Here he shares insights into the process of designing infographics and key principles to create a design with impact. </p>
The impact of learning modality on team-based learning (TBL) outcomes in anatomical sciences education
<p>Team-based learning (TBL) is an instructional methodology that has been increasingly used in anatomy and physiology education in recent years. The appropriateness of TBL methods for students with diverse preferred sensory learning modalities has not been adequately examined. This study aimed to show the influence of students preferred sensory modality for learning on TBL and traditional academic outcome measures (tests, assignments, etc.). 157 American undergraduate Communication Sciences and Disorders students taking anatomy and physiology courses took the VARK, a questionnaire quantifying their preferred learning modality. These students traditional and TBL measures of academic success were compared by preferred learning modality. A one-way MANOVA failed to find any difference in TBL or other academic outcomes by preferred learning modality (<em>p</em> = .252). The results of this study support the effectiveness of TBL methods for undergraduate students regardless of what sensory mode they prefer to receive information</p>
Dataset for "Understanding Performance Concerns in the API Documentation of Data Science Libraries"
<p>Dataset for the manuscript "Understanding Performance Concerns in the API Documentation of Data Science Libraries", including the results of knowledge classification, consistency analysis, and evolution analysis on the API documentation data.</p>
Machine intelligence in mechanical and aerospace sciences: Today & beyond
<p>In this lecture, the application of AI / ML and the upcoming trends are discussed, particularly in reference to mechanical and aerospace industries. AI and ML have been topics of huge interest in recent times. Machine learning techniques are being applied vastly to understand the uncertainties in the models - both solid and fluid mechanics. During the lecture, we will discuss on how artificial intelligence is being used in the aerospace industry.</p>
Science Communication Webinar Series: The Art of Pitching Presentation
<p>Blue-Action are coordinating a series of webinars on climate science communication in 2020. These are aimed at early-career researchers, but anyone who wishes to engage people with their research at any stage will find them valuable.</p> <p>This webinar was delivered on 21st August 2020 by <a href="https://nordic.climate-kic.org/what-we-offer/entrepreneurship/our-offering-for-start-ups/">Thomas Simon Oleson</a>, who is an Entrepreneurship Manager at <a href="https://nordic.climate-kic.org/">EIT Climate-KIC</a>. Thomas has worked with businesses and start-ups for over 8 years. Here he describes how to pitch your work to stakeholders with visual and verbal style.</p> <p> </p>
Metadata standards and tools practice at EPFL School of Life Sciences 2020 Survey
<p>In 2020, EPFL Library conducted a study about Tools and Metadata Standards practice in EPFL School of Life Sciences.</p> <p>By standard, we mean:<br> - terminological resources (vocabularies, terminologies, classifications, thesauri),<br> - formats and data models / schemas,<br> - structured knowledge bases (databases, reference databases, ontologies).<br> And by tools, we mean:<br> - bioinformatics software (i.e. for sequence or molecular structure analysis of proteins and genes)<br> - databases from the Life Sciences field (i.e. genome databases).</p> <p>Our goal was twofold: on the one hand, to gain new knowledge and insights, and on the other hand, to develop a reproducible survey methodology resolutely based on liaison librarian-data librarian collaboration.</p> <p>This dataset reflects the results collected during the second phase of the study: "Survey in EPFL Life Sciences Community".</p> <p> </p>
Reconciling seascape genetics and fisheries science in three co-distributed flatfishes
<p>Uncertainty hampers innovative mixed-fisheries management by the scales at which connectivity dynamics are relevant to management objectives. The spatial scale of sustainable stock management is species-specific and depends on ecology, life history and population connectivity. One valuable approach to understand these spatial scales is to determine to what extent population genetic structure correlates with the oceanographic environment. Here we compare the level of genetic connectivity in three co-distributed and commercially exploited demersal flatfish species living in the North East Atlantic Ocean. Population genetic structure was analysed based on 14, 14 and 10 neutral DNA microsatellite markers for turbot, brill and sole respectively. We then used redundancy analysis (RDA) to attribute the genetic variation to spatial (geographic location), temporal (sampling year) and oceanographic (water column characteristics) components.</p>
Features of research data policies of Earth science and biodiversity academic society journals
<p>Dataset related to Hübner (2020) Earth science and biodiversity journals can improve support for data publication. Preprint: https://doi.org/10.23689/fidgeo-3818</p> <p>This study reviews research data policies and author instructions of 31 journals from the Earth sciences and from biodiversity that are published by German learned societies or research institutions. The statements on data publishing of the journal´s data policies / author guidelines were matched to 14 pre-defined features of journal research data policies from Hrynaszkiewicz, I. et al. (2020) Developing a Research Data Policy Framework for All Journals and Publishers. Data Science Journal, 19: 5, pp. 1–15. <a href="https://doi.org/10.5334/dsj-2020-005">https://doi.org/10.5334/dsj-2020-005</a>.</p>
Open database of small-scale solar PV installations: a Citizen Science initiative
<p>Presentation given at the European PV Solar Energy Conference 2020 about the development of a Citizen Science initiative related to PV installations, Generation Solar.</p>
MSL Curiosity Rover Images with Science and Engineering Classes
<p> </p> <p><strong>Please note that the file msl-labeled-data-set-v2.1.zip</strong><strong> below contains the latest images and labels associated with this data set. </strong></p> <p> </p> <p><strong>Data Set Description</strong></p> <p>The data set consists of 6,820 images that were collected by the Mars Science Laboratory (MSL) Curiosity Rover by three instruments: (1) the Mast Camera (Mastcam) Left Eye; (2) the Mast Camera Right Eye; (3) the Mars Hand Lens Imager (MAHLI). With the help from Dr. Raymond Francis, a member of the MSL operations team, we identified 19 classes with science and engineering interests (see the "Classes" section for more information), and each image is assigned with 1 class label. We split the data set into training, validation, and test sets in order to train and evaluate machine learning algorithms. The training set contains 5,920 images (including augmented images; see the "Image Augmentation" section for more information); the validation set contains 300 images; the test set contains 600 images. The training set images were randomly sampled from sol (Martian day) range 1 - 948; validation set images were randomly sampled from sol range 949 - 1920; test set images were randomly sampled from sol range 1921 - 2224. All images are resized to 227 x 227 pixels without preserving the original height/width aspect ratio.</p> <p><strong>Directory Contents</strong></p> <ul> <li>images - contains all 6,820 images</li> <li>class_map.csv - string-integer class mappings</li> <li>train-set-v2.1.txt - label file for the training set</li> <li>val-set-v2.1.txt - label file for the validation set</li> <li>test-set-v2.1.txt - label file for the test set</li> </ul> <p>The label files are formatted as below:</p> <p>"Image-file-name class_in_integer_representation"</p> <p><strong>Labeling Process</strong></p> <p>Each image was labeled with help from three different volunteers (see Contributor list). The final labels are determined using the following processes:</p> <ul> <li>If all three labels agree with each other, then use the label as the final label.</li> <li>If the three labels do not agree with each other, then we manually review the labels and decide the final label.</li> <li>We also performed error analysis to correct labels as a post-processing step in order to remove noisy/incorrect labels in the data set. </li> </ul> <p><strong>Classes</strong></p> <p>There are 19 classes identified in this data set. In order to simplify our training and evaluation algorithms, we mapped the class names from string to integer representations. The names of classes, string-integer mappings, distributions are shown below:</p> <p>Class name, counts (training set), counts (validation set), counts (test set), integer representation</p> <p>Arm cover, 10, 1, 4, 0</p> <p>Other rover part, 190, 11, 10, 1</p> <p>Artifact, 680, 62, 132, 2</p> <p>Nearby surface, 1554, 74, 187, 3</p> <p>Close-up rock, 1422, 50, 84, 4</p> <p>DRT, 8, 4, 6, 5</p> <p>DRT spot, 214, 1, 7, 6</p> <p>Distant landscape, 342, 14, 34, 7</p> <p>Drill hole, 252, 5, 12, 8</p> <p>Night sky, 40, 3, 4, 9</p> <p>Float, 190, 5, 1, 10</p> <p>Layers, 182, 21, 17, 11</p> <p>Light-toned veins, 42, 4, 27, 12</p> <p>Mastcam cal target, 122, 12, 29, 13</p> <p>Sand, 228, 19, 16, 14</p> <p>Sun, 182, 5, 19, 15</p> <p>Wheel, 212, 5, 5, 16</p> <p>Wheel joint, 62, 1, 5, 17</p> <p>Wheel tracks, 26, 3, 1, 18</p> <p> </p> <p><strong>Image Augmentation</strong></p> <p>Only the training set contains augmented images. 3,920 of the 5,920 images in the training set are augmented versions of the remaining 2000 original training images. Images taken by different instruments were augmented differently. As shown below, we employed 5 different methods to augment images. Images taken by the Mastcam left and right eye cameras were augmented using a horizontal flipping method, and images taken by the MAHLI camera were augmented using all 5 methods. Note that one can filter based on the file names listed in the train-set.txt file to obtain a set of non-augmented images.</p> <ul> <li>90 degrees clockwise rotation (file name ends with -r90.jpg)</li> <li>180 degrees clockwise rotation (file name ends with -r180.jpg)</li> <li>270 degrees clockwise rotation (file name ends with -r270.jpg)</li> <li>Horizontal flip (file name ends with -fh.jpg)</li> <li>Vertical flip (file name ends with -fv.jpg)</li> </ul> <p><strong>Acknowledgment</strong></p> <p>The authors would like to thank the volunteers (as in the Contributor list) who provided annotations for this data set. We would also like to thank the PDS Imaging Note for the continuous support of this work.</p>
Figure 4 in Optimizing biodiversity informatics to improve information flow, data quality, and utility for science and society
Figure 4. Graphical representation of original and recently modified pathways for feedback regarding Primary Biodiversity Data, showing information transfer among users, providers, and aggregators. Such feedback consists of suggested improvements or additions to data fields, for example a change in species identification or a newly determined georeference. The diagrams contrast two complementary mechanisms: (a) the original feedback loop (currently dominant); and (b) the emerging feedback pendulum (proposed for expansion). In a: (1) the user sends feedback to the provider (e.g., a given natural history museum); (2) if the provider makes a corresponding change to its database, the updated information is sent to the aggregator; and (3) that information becomes available for query by all users. In practice, because many providers do not consistently make such changes (denoted by an X), users do have access to updated information (dashed line). In b: (1) the user sends feedback to the aggregator; (2) the aggregator simultaneously both annotates the record (visible to all users) and sends the suggested information to the provider; (3) if the provider makes a corresponding change to its database, the updated information is sent to the aggregator; and (4) the aggregator makes the updated information available for query by all users. Note that even if a provider takes no action regarding the suggested information, the annotations placed by the aggregator are nevertheless available to users. Additionally, because the quantifications of data quality and use described in the text allow for benchmarks that can be tracked over time, we anticipate that the feedback pendulum will help providers become more successful in justifying and securing funding to make data improvements based on feedback from users.
Supplementary material 1 from: Zermoglio PF, Plos A, Acosta N, Amaya L, Escobar DA, Grattarola F, Mancina CA, Nuñez F, Plata CA, Quintero E, Vargas M (2020) Latin American Plea for Incorporation of Other, Non-English Languages in TDWG Standards Documentation. Biodiversity Information Science and Standards 4: e58973. https://doi.org/10.3897/biss.4.58973
Signatories to the petition for incorporation of other languages to the Biodiversity Information Standards (TDWG) standards and documentation
Supplementary material 1 from: Wilson JRU, Bacher S, Daehler CC, Groom QJ, Kumschick S, Lockwood JL, Robinson TB, Zengeya TA, Richardson DM (2020) Frameworks used in invasion science: progress and prospects. In: Wilson JR, Bacher S, Daehler CC, Groom QJ, Kumschick S, Lockwood JL, Robinson TB, Zengeya TA, Richardson DM. NeoBiota 62: 1-30. https://doi.org/10.3897/neobiota.62.58738
Details of the workshop, the invitation, list of attendees, the programme, ground rules, form for highlighting case-studies, and the process for compiling the special issue
Supporting Data for Tanioka, Fichot, and Matsumoto (2020), Frontiers in Marine Science
<p>This dataset contains files from [Tanioka, T., Fichot, C. G., & Matsumoto, K. (2020). Toward Determining the Spatio-Temporal Variability of Upper-Oean Ecosystem Stoichiometry From Satellite Remote Sensing. <em>Frontiers in Marine Science, </em> 7:604893. doi: 10.3389/fmars.2020.604893]</p> <p>1. TFM20_Monthly.nc = Monthly climatology of satellite-derived variables.</p> <p>2. TFM20_Timeseries.nc = Timeseries of satellite-derived phytoplankton C:P, POC:POP, and Cphyto:POC at BATS and HOT.</p>
Stemming the Fake Flow: How Unistem Day Reveals the Importance of Science Communication to Students
<p><strong>Episode Summary</strong></p> <p>In this episode we cover UniStem Day, a pan European outreach event that bring stem cell research and high school students together. We talk to the organisers of UniStem Day at the Max-Delbrück-Center for Molecular Medicine, Stefanie Mahler and Dr Daniel Besser, as well as teachers and pupils who attended. </p> <p><strong>Episode Links</strong></p> <p><a href="https://gscn.org/en/HOME.aspx">German Stem Cell Network</a></p>
An Inventive Step: Shobita Parthasarathy on the Role of Patents and Innovation in Science
<p><strong>Episode Summary:</strong></p> <p>One of the issues that gets raised by the Open Science movement is that it has a potential conflict with innovation and commercialisation of research. To explore this topic we talked to Professor Shobita Parthasarathy about how patents actually work, how we understand the idea of science as a public good, and the challenges of inclusive innovative. </p> <p><strong>Episode Links: </strong></p> <p><a href="http://stpp.fordschool.umich.edu/">Science, Technology, and Public Policy Program</a></p> <p><a href="http://shobitap.org/">http://shobitap.org/</a></p> <p>Twitter: <a href="https://twitter.com/ShobitaP">@ShobitaP</a></p> <p><em>Further reading: </em></p> <p>Shobita Parthasarathy. <a href="https://www.amazon.com/Building-Genetic-Medicine-Technology-Comparative/dp/0262162423/ref=sr_1_2?keywords=building+genetic+medicine&qid=1583277962&s=books&sr=1-2"><em>Building Genetic Medicine: Breast Cancer, Technology, and the Comparative Politics of Health Care</em></a>. Cambridge, MA: MIT Press, 2007. (available at Amazon, MIT Press, and wherever books are sold) </p> <p>Shobita Parthasarathy. <em><a href="https://www.amazon.com/Patent-Politics-Markets-Public-Interest/dp/022643785X/ref=sr_1_1?s=books&ie=UTF8&qid=1489155980&sr=1-1&keywords=patent+politics">Patent Politics: Life Forms, Markets, and the Public Interest in the United States and Europe</a>. </em>Chicago: University of Chicago Press, 2017 (available at Amazon, University of Chicago Press, and wherever books are sold)</p> <p> Shobita Parthasarathy. “<a href="https://www.nature.com/articles/d41586-018-07108-3">Use Patents to Regulate Gene Editing</a>.” <em>Nature</em>. October 25, 2018.</p> <p> Shobita Parthasarathy. “<a href="https://theconversation.com/an-early-expression-of-democracy-the-us-patent-system-is-out-of-step-with-todays-citizens-43812">An early expression of democracy, the US patent system is out of step with today’s citizens</a>.” [Updated]. <em>The Conversation</em>. July 4 2018.<strong> </strong>(reprinted in <em>Business Insider, Associated Press, </em>among other outlets)</p> <p> Shobita Parthasarathy. “<a href="https://theconversation.com/how-to-make-sure-we-all-benefit-when-nonprofits-patent-technologies-like-crispr-76829">How to make sure we all benefit when nonprofits patent technologies like CRISPR</a>.” <em>The Conversation</em>. July 19, 2017. </p>
Data on universities offering undergraduate degrees that train students for soil science careers at universities in the USA and its territories
<p>Several soil science education studies over the last 15 years have focused on the number of students enrolled in soil science programs. However, no studies have quantitatively addressed the number of undergraduate soil science preparatory programs that exist in the United States, which means we do not have solid data concerning whether overall program numbers are declining, rising, or holding steady. This also means we do not have complete data on the same trends for total undergraduate soil science students in the United States. This study used the US Office of Personnel Management (OPM) Soil Science Series 0470 standards to determine if a bachelor's degree met soil science preparatory criteria. Lists of the approximately 3,500 regionally accredited colleges and universities were obtained from the regional accrediting agencies and the website of each of the colleges and universities was visited to determine if they had a degree program that met the OPM 0470 criteria. A total of 92 soil science preparatory degree programs were identified at 86 colleges and universities. These programs were primarily linked to 1) agriculture, 2) environmental science, and 3) soil and water science based on number of degree occurrences. This study creates a baseline for future studies that can investigate trends in soil science programs. It also provides insight into the institutions and degree programs that should be included in soil science education studies.</p>
Making plasma science more open, collaborative, and reproducible
<p>The reproducibility crisis of modern science is the inability of scientists to reproduce roughly half of the results published in scientific journals. This crisis has affected a broad range of fields, such as psychology, chemistry, and oncology. While physicists tend to have high confidence that physics research is reproducible, no comprehensive studies have been performed to support or refute this claim. Nevertheless, the scientific, cultural, and institutional practices that contribute to the reproducibility crisis in other fields are also present in plasma science. This tutorial will describe how to implement best practices for scientific reproducibility into plasma research. The talk will begin by outlining sources of irreproducibility, such as cognitive biases, improper use of statistics, publication bias, closed access policies for data and software, and the reward system for modern academia. The talk will then describe remedies for these problems such as open access data policies; open metadata standards; open source software; training on proper use of statistics; pre-registration of research methodologies; independent methodological and statistical support; and valuing the reproducibility of research in tenure, hiring, and funding decisions.</p>
Process Not Product: How the Open Life Science Mentoring Program Creates Open Science Ambassadors
<p><strong>Episode Summary</strong></p> <p>In this episode we talk to Yo Yehudi and Malvika Sharan, two of the co-founders and organisers of the Open Life Science training and mentoring program. We discuss why mentorship and community are so important in encouraging open science, what makes the program unique, and what the future for Open Life Science. </p> <p><strong>Episode Links</strong></p> <ul> <li><a href="https://openlifesci.org/">Open Life Science</a></li> <li><a href="https://about.me/malvikasharan">Malvika Sharan</a> <ul> <li><a href="https://twitter.com/MalvikaSharan">Twitter</a></li> </ul> </li> <li><a href="https://yo-yehudi.com/">Yo Yehudi</a> <ul> <li><a href="https://twitter.com/yoyehudi">Twitter</a></li> </ul> </li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.