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1. Pain and Laboratory Animals: Publication Practices for Better Data Reproducibility and Better Animal Welfare

   https://doi.org/10.1371/journal. pone.0155001  

   Larry Carbone, Jamie Austin (May 2016, PloS one)

   Scientists who perform major survival surgery on laboratory animals face a dual welfare and methodological challenge: how to choose surgical anesthetics and post-operative analgesics that will best control animal suffering, knowing that both pain and the drugs that manage pain can all affect research outcomes. Scientists who publish full descriptions of animal procedures allow critical and systematic reviews of data, demonstrate their adherence to animal welfare norms, and guide other scientists on how to conduct their own studies in the field. We investigated what information on animal pain management a reasonably diligent scientist might find in planning for a successful experiment. To explore how scientists in a range of fields describe their management of this ethical and methodological concern, we scored 400 scientific articles that included major animal survival surgeries as part of their experimental methods, for the completeness of information on anesthesia and analgesia. The 400 articles (250 accepted for publication pre-2011, and 150 in 2014–15, along with 174 articles they reference) included thoracotomies, craniotomies, gonadectomies, organ transplants, peripheral nerve injuries, spinal laminectomies and orthopedic procedures in dogs, primates, swine, mice, rats and other rodents. We scored articles for Publication Completeness (PC), which was any mention of use of anesthetics or analgesics; Analgesia Use (AU) which was any use of post-surgical analgesics, and Analgesia Completeness (a composite score comprising intra-operative analgesia, extended post-surgical analgesia, and use of multimodal analgesia). 338 of 400 articles were PC. 98 of these 338 were AU, with some mention of analgesia, while 240 of 338 mentioned anesthesia only but not postsurgical analgesia. Journals’ caliber, as measured by their 2013 Impact Factor, had no effect on PC or AU. We found no effect of whether a journal instructs authors to consult the ARRIVE publishing guidelines published in 2010 on PC or AC for the 150 mouse and rat articles in our 2014–15 dataset. None of the 302 articles that were silent about analgesic use included an explicit statement that analgesics were withheld, or a discussion of how pain management or untreated pain might affect results. We conclude that current scientific literature cannot be trusted to present full detail on use of animal anesthetics and analgesics. We report that publication guidelines focus more on other potential sources of bias in experimental results, under-appreciate the potential for pain and pain drugs to skew data, PLOS ONE | DOI:10.1371/journal.pone.0155001 May 12, 2016 1 / 24 a11111 OPEN ACCESS Citation: Carbone L, Austin J (2016) Pain and Laboratory Animals: Publication Practices for Better Data Reproducibility and Better Animal Welfare. PLoS ONE 11(5): e0155001. doi:10.1371/journal. pone.0155001 Editor: Chang-Qing Gao, Central South University, CHINA Received: December 29, 2015 Accepted: April 22, 2016 Published: May 12, 2016 Copyright: © 2016 Carbone, Austin. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Authors may be contacted for further information. Funding: This study was funded by the United States National Science Foundation Division of Social and Economic Sciences. Award #1455838. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. and thus mostly treat pain management as solely an animal welfare concern, in the jurisdiction of animal care and use committees. At the same time, animal welfare regulations do not include guidance on publishing animal data, even though publication is an integral part of the cycle of research and can affect the welfare of animals in studies building on published work, leaving it to journals and authors to voluntarily decide what details of animal use to publish. We suggest that journals, scientists and animal welfare regulators should revise current guidelines and regulations, on treatment of pain and on transparent reporting of treatment of pain, to improve this dual welfare and data-quality deficiency. 

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2. Engaging young scholars in science through publication: A survey analysis of published middle and high school authors

   https://doi.org/10.1002/leap.1480  

   Mattison, Kari A.; Merchak, Andrea R.; Wieman, Scott T.; Zimmer, Stephanie; Fankhauser, Sarah C. (July 2022, Learned Publishing)

   Abstract Young researchers are often excluded from the scholarly processes of peer‐review and publication, which are cornerstones of scholarly work. TheJournal of Emerging Investigatorsis an open access journal dedicated to publishing the research of middle and high school students. We surveyed student authors before and after they participated in the peer‐review and publication process of their scientific articles. Following peer‐review and publication, students report gains in their confidence and self‐efficacy in science, and increased feelings of identity and belonging in science. Our findings demonstrate that even the youngest scholars are capable of participating in the publication process, and our data suggest that participation in the process has positive outcomes. 

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3. Successes and Struggles with Computational Reproducibility: Lessons from the Fragile Families Challenge

   https://doi.org/10.1177/2378023119849803  

   Liu, David M.; Salganik, Matthew J. (January 2019, Socius: Sociological Research for a Dynamic World)

   Reproducibility is fundamental to science, and an important component of reproducibility is computational reproducibility: the ability of a researcher to recreate the results of a published study using the original author’s raw data and code. Although most people agree that computational reproducibility is important, it is still difficult to achieve in practice. In this article, the authors describe their approach to enabling computational reproducibility for the 12 articles in this special issue of Socius about the Fragile Families Challenge. The approach draws on two tools commonly used by professional software engineers but not widely used by academic researchers: software containers (e.g., Docker) and cloud computing (e.g., Amazon Web Services). These tools made it possible to standardize the computing environment around each submission, which will ease computational reproducibility both today and in the future. Drawing on their successes and struggles, the authors conclude with recommendations to researchers and journals. 

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4. Journal Aims Scope and Further Details

   https://doi.org/10.7910/DVN/ASMDSA  

   Kaiser, Kendra; Braswell, Anna; Fork, Megan; Fork, Megan (January 2022, Harvard Dataverse)

   Detailed information and published mission or aims scope for journals in which 3 or more publications from the dataset Publications associated with SES grants, 2000-2015 appeared. CSV file with 10 columns and names in header row: journal is the name of the scientific journal or outlet in which at least 3 papers were published (text); number of papers is the number of papers from the dataset Publications associated with SES grants, 2000-2015 published in the journal (integer); Impact factor is the most recent available Impact Factor for the journal as of March 2020 (float); Discipline is the broad disciplinary category to which the journal belongs, as identified by the authors of this dataset (text); Stated aimsscope is the text of the journal aimsscope as provided on the journal website (text); Mission includes interdisciplinary? categorizes whether the stated aimsscope of the journal includes dissemination of interdisciplinary research (Y indicates the stated aimsscope explicitly include interdisciplinary research, I indicates that publication of interdisciplinary research is implicit but not directly stated in the aimsscope, N indicates there is no evidence that interdisciplinary research are part of the aimsscope of the journal); Mission includes humans/social? categorizes whether the stated aimsscope of the journal includes dissemination of research about human or social systems (Y indicates the stated aimsscope include some mention of human impacts, social systems, etc., N indicates there is no evidence that research on human or social systems are part of the aimsscope of the journal) Gutcheck Interdisciplinary? is an evaluation of whether the journal publishes interdisciplinary research as a matter of course, as judged by the authors of the dataset (Y indicates the journal publishes interdisciplinary research s a matter of course, N indicates journal does not tend to publish interdisciplinary research, kinda to indicate some history of publishing interdisciplinary research that may not be a major focus of published content. Forward slashes between values show where the dataset authors differed in their assessments.); Gutcheck CNHS? is an evaluation of whether the journal publishes research on socio-environmental systems (social-ecological systems, coupled natural and human systems) as a matter of course, as judged by the authors of the dataset (Y indicates the journal publishes research on socio-environmental systems as a matter of course, N indicates journal does not tend to publish research on socio-environmental systems , kinda to indicate some history of publishing research on socio-environmental systems that may not be a major focus of published content. Forward slashes between values show where the dataset authors differed in their assessments.); Notes provide any other notes added by the authors of this dataset during our processing of these data (text). 

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5. Predicting Licenses for Changed Source Code

   https://doi.org/10.1109/ASE.2019.00070  

   Liu, Xiaoyu; Huang, LiGuo; Ge, Jidong; Ng, Vincent (November 2019, Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering)

   null (Ed.)

   Open source software licenses regulate the circumstances under which software can be redistributed, reused and modified. Ensuring license compatibility and preventing license restriction conflicts among source code during software changes are the key to protect their commercial use. However, selecting the appropriate licenses for software changes requires lots of experience and manual effort that involve examining, assimilating and comparing various licenses as well as understanding their relationships with software changes. Worse still, there is no state-of-the-art methodology to provide this capability. Motivated by this observation, we propose in this paper Automatic License Prediction (ALP), a novel learning-based method and tool for predicting licenses as software changes. An extensive evaluation of ALP on predicting licenses in 700 open source projects demonstrate its effectiveness: ALP can achieve not only a high overall prediction accuracy (92.5% in micro F1 score) but also high accuracies across all license types. 

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