An official website of the United States government
Information about individual publications associated with grants funded by NSF to support SES research from 2000-2015 (see "SES grants, 2000-2015"). For grants with ten or fewer publications, we included information about all available publications in this dataset. For grants with more than ten publications, we randomly selected ten to include in this dataset. CSV file with 13 columns and names in header row: "Grant ID" is the ID from the Dimensions platform (string); "Grant Number" is the NSF Award number (integer); "Publication Title" is the title of the paper (text); "Publication Year" is the year in which the paper was published (year); "Authors" is a list or abbreviated list of the authors of the paper (text); "Journal" is the name of the scientific journal or outlet in which the paper is published (text); "Interdis Rubric 1" is a metric representing the dataset authors' assessment for the level of interdisciplinarity represented by the paper (integer: “1” indicated social and natural science interdisciplinarity where both social and environmental conditions are measured or explored and/or author affiliations included departments across these disciplines; “2” indicated general interdisciplinarity between two or more different fields (that may both be within natural or social science); and “3” indicated single-disciplinarity) "Citations" is the count of citations the paper had received as of the date listed in "date for cite count", as reported in Google Scholar (integer); "date for cite count" is the date on which citation count for the paper was obtained (ddBBByy); "Abstract" is the text of the abstract of the paper, where available (text); "Notes" are any notes added by the authors of the dataset (text).
more »
« less
Journal Aims Scope and Further Details
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).
more »
« less
- Award ID(s):
- 1924670
- PAR ID:
- 10482841
- Publisher / Repository:
- Harvard Dataverse
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Predatory journals and publishers are a growing concern in the scholarly publishing arena. As one type of attempt to address this increasingly important issue, numerous individuals, associations, and companies have begun curating journal watchlists or journal safelists. This study uses a qualitative content analysis to explore the inclusion/exclusion criteria stated by scholarly publishing journal watchlists and safelists to better understand the content of these lists, as well as the larger controversies that continue to surround the phenomenon that has come to be known as predatory publishing. Four watchlists and ten safelists were analyzed through an examination of their published mission statements and inclusion/exclusion criteria. Notable differences that emerged include the remaining influence of librarian Jeffrey Beall in the watchlists, and the explicit disavowal of his methods for the safelists, along with a growing recognition that the “list” approach may not fully address systemic aspects of predatory publishing that go beyond the individual author's ethical decision-making agency.more » « less
-
Studies on human mobility have a long history with increasingly strong interdisciplinary connections across social science, environmental science, information and technology, computer science, engineering, and health science. However, what is lacking in the current research is a synthesis of the studies to identify the evolutional pathways and future research directions. To address this gap, we conduct a systematic review of human mobility-related studies published from 1990 to 2020. Drawing on the selected publications retrieved from the Web of Science, we provide a bibliometric analysis and network visualisation using CiteSpace and VOSviewer on the number of publications and year published, authors and their countries and afflictions, citations, topics, abstracts, keywords, and journals. Our findings show that human mobility-related studies have become increasingly interdisciplinary and multi-dimensional, which have been strengthened by the use of the so-called ‘big data’ from multiple sources, the development of computer technologies, the innovation of modelling approaches, and the novel applications in various areas. Based on our synthesis of the work by top cited authors we identify four directions for future research relating to data sources, modelling methods, applications, and technologies. We advocate for more in-depth research on human mobility using multi-source big data, improving modelling methods and integrating advanced technologies including artificial intelligence, and machine and deep learning to address real-world problems and contribute to social good.more » « less
-
Abstract Because research in science, engineering and medical fields advances incrementally, researchers routinely write papers that build directly on their prior work. While each new research article is expected to make a novel contribution, researchers often need to repeat some material—method details, background and so on—from their previous articles, a practice called ‘text recycling’. While increasing awareness of text recycling has led to the proliferation of policies, journal editorials and scholarly articles addressing the practice, these documents tend to employ inconsistent terminology—using different terms to name the same key ideas and, even more problematic, using the same terms with different meanings. These inconsistencies make it difficult for readers to know precisely how the ideas or expectations articulated in one document relate to those of others. This paper first clarifies the problems with current terminology, showing how key terms are used inconsistently across publisher policies for authors, guidelines for editors and textbooks on research ethics. It then offers a new taxonomy of text‐recycling practices with terms designed to align with the acceptability of these practices in common research writing and publishing contexts.more » « less
-
Proc. of 2023 IEEE 39th International Conference on Data Engineering (Ed.)Numerous papers get published all the time. However, some papers are born to be well-cited while others are not. In this work, we revisit the important problem of citation prediction, by focusing on the important yet realistic prediction on the average number of citations a paper will attract per year. The task is nonetheless challenging because many correlated factors underlie the potential impact of a paper, such as the prestige of its authors, the authority of its publishing venue, and the significance of the problems/techniques/applications it studies. To jointly model these factors, we propose to construct a heterogeneous publication network of nodes including papers, authors, venues, and terms. Moreover, we devise a novel heterogeneous graph neural network (HGN) to jointly embed all types of nodes and links, towards the modeling of research impact and its propagation. Beyond graph heterogeneity, we find it also important to consider the latent research domains, because the same nodes can have different impacts within different communities. Therefore, we further devise a novel cluster-aware (CA) module, which models all nodes and their interactions under the proper contexts of research domains. Finally, to exploit the information-rich texts associated with papers, we devise a novel text-enhancing (TE) module for automatic quality term mining. With the real-world publication data of DBLP, we construct three different networks and conduct comprehensive experiments to evaluate our proposed CATE-HGN framework, against various state-of-the-art models. Rich quantitative results and qualitative case studies demonstrate the superiority of CATEHGN in citation prediction on publication networks, and indicate its general advantages in various relevant downstream tasks on text-rich heterogeneous networks.more » « less
