R. Iping, Ilse Nederveen, B. Ranjbar-Sahraei, Hosein Azarbonyad, Max Dumoulin, Georgios Tsatsaronis, I. M. Mathijssen
This article describes the process of the development of a research intelligence tool to analyse rare disease research in the Netherlands. To the best of our knowledge, this is the first tool that can surface and organise scientific output on rare diseases using established annotation and natural language processing mechanisms. We focus on the track leading up to the development, including strategic motivation and user needs, the development of a proof-of-concept tool, upscaling the idea to a national collaboration project, the development of the final tool and a usability evaluation and subsequent fine-tuning. The tool is a unique visualisation that allows users to benefit with a few clicks from getting the information they require for their needs, and offers novel scientific indicators to characterize (relative) rare disease research activity. We discuss the applications of insights derived from this tool for science policy and to support decision making, and to identify opportunities and potential collaborations and make recommendations for future developments, including a broadening of the scope and discuss potential novel applications.
{"title":"The development of a research intelligence tool for rare disease research in the Netherlands","authors":"R. Iping, Ilse Nederveen, B. Ranjbar-Sahraei, Hosein Azarbonyad, Max Dumoulin, Georgios Tsatsaronis, I. M. Mathijssen","doi":"10.1162/qss_a_00320","DOIUrl":"https://doi.org/10.1162/qss_a_00320","url":null,"abstract":"\u0000 This article describes the process of the development of a research intelligence tool to analyse rare disease research in the Netherlands. To the best of our knowledge, this is the first tool that can surface and organise scientific output on rare diseases using established annotation and natural language processing mechanisms. We focus on the track leading up to the development, including strategic motivation and user needs, the development of a proof-of-concept tool, upscaling the idea to a national collaboration project, the development of the final tool and a usability evaluation and subsequent fine-tuning. The tool is a unique visualisation that allows users to benefit with a few clicks from getting the information they require for their needs, and offers novel scientific indicators to characterize (relative) rare disease research activity. We discuss the applications of insights derived from this tool for science policy and to support decision making, and to identify opportunities and potential collaborations and make recommendations for future developments, including a broadening of the scope and discuss potential novel applications.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141812265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Antonio Perianes-Rodríguez, Bianca S. Mira, Daniel Martínez-Ávila, M. C. Grácio
For the last fifty years, the journal impact factor (IF) has been the most prominent of all bibliometric indicators. Since the first Journal Citation Report was launched, the IF has been used, often improperly, to evaluate institutions, publications, and individuals. Its well-known significant technical limitations have not detracted from its popularity, and they contrast with the lack of consensus over the numerous alternatives suggested as complements or replacements. This paper presents a percentile distribution-based proposal for assessing the influence of scientific journals and publications that corrects several of the IF’s main technical limitations using the same set of documents as is used to calculate the IF. Nearly 400 journals of Library Science and Information Science and Biochemistry and Molecular Biology categories were analyzed for this purpose. The results show that the new indicator retains many of its predecessor’s advantages and adds benefits of its own: It is more accurate, more gaming-resistant, more complete, and less influenced by the citation window or extreme observations.
过去五十年来，期刊影响因子（IF）一直是所有文献计量指标中最重要的指标。自第一份《期刊引文报告》发布以来，IF 一直被用来评价机构、出版物和个人，但往往使用不当。众所周知，IF 在技术上有很大的局限性，但这并没有影响它的受欢迎程度，与此形成鲜明对比的是，人们对作为补充或替代的众多替代指标缺乏共识。本文提出了一种基于百分位数分布的科学期刊和出版物影响力评估建议，利用计算 IF 所用的同一组文献，修正了 IF 的几个主要技术局限。为此分析了近 400 种图书馆科学与信息科学类期刊和生物化学与分子生物学类期刊。结果表明，新指标保留了其前身的许多优点，并增加了自身的优势：它更准确、更耐博弈、更完整，受引用窗口或极端观察的影响也更小。
{"title":"Real influence: A novel approach to characterize the visibility of journals and publications","authors":"Antonio Perianes-Rodríguez, Bianca S. Mira, Daniel Martínez-Ávila, M. C. Grácio","doi":"10.1162/qss_a_00316","DOIUrl":"https://doi.org/10.1162/qss_a_00316","url":null,"abstract":"\u0000 For the last fifty years, the journal impact factor (IF) has been the most prominent of all bibliometric indicators. Since the first Journal Citation Report was launched, the IF has been used, often improperly, to evaluate institutions, publications, and individuals. Its well-known significant technical limitations have not detracted from its popularity, and they contrast with the lack of consensus over the numerous alternatives suggested as complements or replacements. This paper presents a percentile distribution-based proposal for assessing the influence of scientific journals and publications that corrects several of the IF’s main technical limitations using the same set of documents as is used to calculate the IF. Nearly 400 journals of Library Science and Information Science and Biochemistry and Molecular Biology categories were analyzed for this purpose. The results show that the new indicator retains many of its predecessor’s advantages and adds benefits of its own: It is more accurate, more gaming-resistant, more complete, and less influenced by the citation window or extreme observations.","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141664733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Global algorithms have taken precedence in bibliometrics as approaches to the reconstruction of topics from networks of publications. They partition a large set of publications and the resulting disjoint clusters are then interpreted as individual topics. This is at odds with a sociological understanding of topics as formed by the participants working on and being influenced by them, an understanding that is best operationalized by algorithms prioritizing cohesion rather than separation, by using local information and by allowing topics to overlap. Thus, a different kind of algorithm is needed for topic reconstruction to be successful. Local algorithms represent a promising solution. In this paper, we present for consideration a new Multilayered, Adjustable, Local Bibliometric Algorithm (MALBA), which is in line with sociological definitions of topics and reconstructs dense regions in bibliometric networks locally. MALBA grows a subgraph from a publications seed either by interacting with a fixed network dataset, or by querying an online database to obtain up-to-date linkage information. New candidates for addition are evaluated by assessing the links in two data models. Experiments with publications on the h-index and with ground truth data positioned in a dataset of AMO physics illustrate the properties of MALBA and its potential. https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00314
作为从出版物网络中重建主题的方法，全局算法在文献计量学中占据了主导地位。它们对大量出版物进行分区，然后将由此产生的不相连的聚类解释为单个主题。这与社会学对主题的理解相悖，社会学认为主题是由研究主题并受其影响的参与者形成的，而这种理解的最佳操作方式是算法优先考虑内聚而非分离，使用本地信息并允许主题重叠。因此，要想成功进行话题重构，需要一种不同的算法。本地算法是一种很有前途的解决方案。在本文中，我们提出了一种新的多层、可调整、本地文献计量算法（MALBA）供大家参考，该算法符合社会学对主题的定义，可在本地重建文献计量网络中的密集区域。MALBA 通过与固定的网络数据集交互，或通过查询在线数据库以获取最新的链接信息，从出版物种子中生成子图。通过评估两个数据模型中的链接来评估新的候选添加子图。利用 h 指数上的出版物和 AMO 物理数据集中的地面实况数据进行的实验说明了 MALBA 的特性及其潜力。https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00314。
{"title":"Exploring publication networks with a local cohesion-maximizing algorithm","authors":"Matthias Held, Jochen Gläser","doi":"10.1162/qss_a_00314","DOIUrl":"https://doi.org/10.1162/qss_a_00314","url":null,"abstract":"\u0000 Global algorithms have taken precedence in bibliometrics as approaches to the reconstruction of topics from networks of publications. They partition a large set of publications and the resulting disjoint clusters are then interpreted as individual topics. This is at odds with a sociological understanding of topics as formed by the participants working on and being influenced by them, an understanding that is best operationalized by algorithms prioritizing cohesion rather than separation, by using local information and by allowing topics to overlap. Thus, a different kind of algorithm is needed for topic reconstruction to be successful. Local algorithms represent a promising solution. In this paper, we present for consideration a new Multilayered, Adjustable, Local Bibliometric Algorithm (MALBA), which is in line with sociological definitions of topics and reconstructs dense regions in bibliometric networks locally. MALBA grows a subgraph from a publications seed either by interacting with a fixed network dataset, or by querying an online database to obtain up-to-date linkage information. New candidates for addition are evaluated by assessing the links in two data models. Experiments with publications on the h-index and with ground truth data positioned in a dataset of AMO physics illustrate the properties of MALBA and its potential.\u0000 \u0000 \u0000 https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00314\u0000","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141362519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To provide valuable insights for shaping future funding policies, in this study, we offer a comprehensive panorama of the research funding across 171 SCI disciplines in the past decade (2011–2020), based on more than 13 million scientific literature records from the Web of Science. The relationship between funding and research impact was also explored. To this end, we employ two indicators, i.e., the universality and multiplicity of funding, to indicate the funding level and six indicators to gauge the impact advantages of funding. Our findings reveal an upward trend in both the universality (increasing from 66.30% to 74.26%) and multiplicity (increasing from 2.82 to 3.26) of funding over the past decade. The allocation of funding varies across disciplines, with life sciences and earth sciences receiving the highest percentage of funding (78.31%) and medicine having the highest multiplicity of funding (3.07). Engineering and computer science have seen relatively rapid growth in terms of universality and multiplicity of funding. Funded articles have a greater impact than unfunded ones. And this impact strengthens as the number of funding grants increases. Through regression analysis, the citation advantage of funding was also proven at the article level, although the usage advantage is not significant. https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00315
为了给未来资助政策的制定提供有价值的见解，在本研究中，我们基于 Web of Science 中超过 1300 万条科学文献记录，对过去十年（2011-2020 年）中 171 个 SCI 学科的研究资助情况进行了全面概述。我们还探讨了经费与研究影响力之间的关系。为此，我们采用了两个指标（即资助的普遍性和多重性）来表示资助水平，并采用六个指标来衡量资助的影响优势。我们的研究结果表明，在过去十年中，资助的普遍性（从 66.30% 增加到 74.26%）和多重性（从 2.82 增加到 3.26）都呈上升趋势。各学科的资金分配情况各不相同，生命科学和地球科学获得的资金比例最高（78.31%），医学获得的资金倍数最高（3.07）。工程学和计算机科学在资助的普遍性和多重性方面增长相对较快。获得资助的文章比未获得资助的文章具有更大的影响力。而且这种影响力随着资助数量的增加而增强。https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00315。
{"title":"Research funding in different SCI disciplines: A comparison analysis based on Web of Science","authors":"Wencan Tian, Ruonan Cai, Zhichao Fang, Qianqian Xie, Zhigang Hu, Xianwen Wang","doi":"10.1162/qss_a_00315","DOIUrl":"https://doi.org/10.1162/qss_a_00315","url":null,"abstract":"\u0000 To provide valuable insights for shaping future funding policies, in this study, we offer a comprehensive panorama of the research funding across 171 SCI disciplines in the past decade (2011–2020), based on more than 13 million scientific literature records from the Web of Science. The relationship between funding and research impact was also explored. To this end, we employ two indicators, i.e., the universality and multiplicity of funding, to indicate the funding level and six indicators to gauge the impact advantages of funding. Our findings reveal an upward trend in both the universality (increasing from 66.30% to 74.26%) and multiplicity (increasing from 2.82 to 3.26) of funding over the past decade. The allocation of funding varies across disciplines, with life sciences and earth sciences receiving the highest percentage of funding (78.31%) and medicine having the highest multiplicity of funding (3.07). Engineering and computer science have seen relatively rapid growth in terms of universality and multiplicity of funding. Funded articles have a greater impact than unfunded ones. And this impact strengthens as the number of funding grants increases. Through regression analysis, the citation advantage of funding was also proven at the article level, although the usage advantage is not significant.\u0000 \u0000 \u0000 https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00315\u0000","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141361498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, we aim to investigate the publishing strategies adopted by the Brazilian scientific community, and how it is related with the researchers’ scientific capital. The “research productivity” grant (PQ grant) was taken as an indicator of scientific capital: the higher is the PQ grant a researcher receives the higher is his/her scientific capital. Personal data from 6,993 researchers linked to at least one Brazilian graduate program in biological sciences were obtained through the Sucupira Platform, data on articles published from 2000 to 2019 were retrieved from Lattes Platform and DOAJ was considered to classify articles as OA. Our main findings indicate that subscription-based journals are the most prevalent publishing strategy, but the proportion of OA publications is increasing over time, mainly with APC. We also observed that the lower is the level of PQ grant, the higher is the share of articles in OA journals. Finally, we observed a growing trend in the percentage of researchers with high and mid-high adherence to OA from all levels of PQ grant, but mainly with APC. Mapping the dynamics of publishing strategies can play an important step towards driving policies oriented to the promotion of OA. https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00308
{"title":"20 years of the Open Access Movement: a retrospective study on the relationship between publishing strategies and scientific capital of Brazilian researchers in Biological Science","authors":"D. Sarzi, J. Leta","doi":"10.1162/qss_a_00308","DOIUrl":"https://doi.org/10.1162/qss_a_00308","url":null,"abstract":"\u0000 In this study, we aim to investigate the publishing strategies adopted by the Brazilian scientific community, and how it is related with the researchers’ scientific capital. The “research productivity” grant (PQ grant) was taken as an indicator of scientific capital: the higher is the PQ grant a researcher receives the higher is his/her scientific capital. Personal data from 6,993 researchers linked to at least one Brazilian graduate program in biological sciences were obtained through the Sucupira Platform, data on articles published from 2000 to 2019 were retrieved from Lattes Platform and DOAJ was considered to classify articles as OA. Our main findings indicate that subscription-based journals are the most prevalent publishing strategy, but the proportion of OA publications is increasing over time, mainly with APC. We also observed that the lower is the level of PQ grant, the higher is the share of articles in OA journals. Finally, we observed a growing trend in the percentage of researchers with high and mid-high adherence to OA from all levels of PQ grant, but mainly with APC. Mapping the dynamics of publishing strategies can play an important step towards driving policies oriented to the promotion of OA.\u0000 \u0000 \u0000 https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00308\u0000","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141009723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Statistical modeling of scientific productivity and impact provides insights into bibliometric measures used also to quantify differences between individual scholars. The Q model decomposes the log-transformed impact of a published paper into a researcher capacity parameter and a random luck parameter. These two parameters are then modeled together with the log-transformed number of published papers (i.e., an indicator of productivity) by means of a trivariate normal distribution. In this work we propose a formulation of the Q model that can be estimated as a structural equation model. The Q model as a structural equation model allows to quantify the reliability of researchers’ Q parameter estimates, it can be extended to incorporate person covariates, and multivariate extensions of the Q model could also be estimated. We empirically illustrate our approach to estimate the Q model and also provide openly available code for R and Mplus. https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00313
{"title":"Latent Variable Modeling of Scientific Impact: Estimation of the Q Model Parameters with Structural Equation Models","authors":"Boris Forthmann, Steffen Nestler","doi":"10.1162/qss_a_00313","DOIUrl":"https://doi.org/10.1162/qss_a_00313","url":null,"abstract":"\u0000 Statistical modeling of scientific productivity and impact provides insights into bibliometric measures used also to quantify differences between individual scholars. The Q model decomposes the log-transformed impact of a published paper into a researcher capacity parameter and a random luck parameter. These two parameters are then modeled together with the log-transformed number of published papers (i.e., an indicator of productivity) by means of a trivariate normal distribution. In this work we propose a formulation of the Q model that can be estimated as a structural equation model. The Q model as a structural equation model allows to quantify the reliability of researchers’ Q parameter estimates, it can be extended to incorporate person covariates, and multivariate extensions of the Q model could also be estimated. We empirically illustrate our approach to estimate the Q model and also provide openly available code for R and Mplus.\u0000 \u0000 \u0000 https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00313\u0000","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141006557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There is a growing expectation for academics to go public, that is to actively engage with the media and supply policy advice for decision-makers. Data showing these interactions are scarce. By linking data from FRIS, BelgaPress and Overton, this study reveals a first snapshot of academics’ media mentions and policy citations for all active academics from Dutch-speaking universities in Belgium. Explorative analysis reveals distinct sector differences, with academics from Social sciences, Medical and Health sciences being most visible. A small minority of mostly male academics featured very often in media as media figures, contrasted by much more discrete policy pillars whose publications got cited often, but featured hardly in traditional media. https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00311
{"title":"Linking science with media and policy: the case of academics in Flanders, Belgium","authors":"Hans Jonker, Florian Vanlee","doi":"10.1162/qss_a_00311","DOIUrl":"https://doi.org/10.1162/qss_a_00311","url":null,"abstract":"\u0000 There is a growing expectation for academics to go public, that is to actively engage with the media and supply policy advice for decision-makers. Data showing these interactions are scarce. By linking data from FRIS, BelgaPress and Overton, this study reveals a first snapshot of academics’ media mentions and policy citations for all active academics from Dutch-speaking universities in Belgium. Explorative analysis reveals distinct sector differences, with academics from Social sciences, Medical and Health sciences being most visible. A small minority of mostly male academics featured very often in media as media figures, contrasted by much more discrete policy pillars whose publications got cited often, but featured hardly in traditional media.\u0000 \u0000 \u0000 https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00311\u0000","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141008631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Women and ethnic minorities underpopulate influential academic positions, even though these groups are increasingly represented at the doctorate level. Does this imply that gender and ethnic gaps in academic careers are closing? Prior studies on gender inequality in academia predominantly focus on single academic fields or restricted time periods. Longitudinal descriptions of ethnic inequality are even more rare. Using a novel dataset of a near-population of doctorates (N = 95,130) from Dutch universities across all academic fields between 1990–2021, and their publications, we extend descriptions on gender and ethnic inequality in academic publication careers in the Netherlands. Furthermore, we assess trends in inequality over approximately 30 years without focusing on established academics. We find that while women are as likely as men to start an academic publishing career after obtaining doctorate, their careers are shorter. Ethnic minority scholars are less likely to start an academic career after doctorate, and when they do, they stop sooner than ethnic majority researchers. We do not observe a trend towards more equality in academic publishing careers. In conclusion, efforts to increase diversity in Dutch academia have not yet paid off, and gender and ethnic parity in are likely not just a matter of time. https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00306
{"title":"A Matter of Time? Gender and Ethnic Inequality in the Academic Publishing Careers of Dutch PhDs","authors":"Anne Maaike Mulders, Bas Hofstra, J. Tolsma","doi":"10.1162/qss_a_00306","DOIUrl":"https://doi.org/10.1162/qss_a_00306","url":null,"abstract":"\u0000 Women and ethnic minorities underpopulate influential academic positions, even though these groups are increasingly represented at the doctorate level. Does this imply that gender and ethnic gaps in academic careers are closing? Prior studies on gender inequality in academia predominantly focus on single academic fields or restricted time periods. Longitudinal descriptions of ethnic inequality are even more rare. Using a novel dataset of a near-population of doctorates (N = 95,130) from Dutch universities across all academic fields between 1990–2021, and their publications, we extend descriptions on gender and ethnic inequality in academic publication careers in the Netherlands. Furthermore, we assess trends in inequality over approximately 30 years without focusing on established academics. We find that while women are as likely as men to start an academic publishing career after obtaining doctorate, their careers are shorter. Ethnic minority scholars are less likely to start an academic career after doctorate, and when they do, they stop sooner than ethnic majority researchers. We do not observe a trend towards more equality in academic publishing careers. In conclusion, efforts to increase diversity in Dutch academia have not yet paid off, and gender and ethnic parity in are likely not just a matter of time.\u0000 \u0000 \u0000 https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00306\u0000","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140710896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The surge in preprint server use, especially during the Covid-19 pandemic, necessitates a reex-amination of their significance in the realm of science communication. This study rigorously investigates discussions surrounding preprints, framing them within the contexts of systems theory and boundary objects in scholarly communication. An analysis of a curated selection of COVID-19-related preprints from bioRxiv and medRxiv was conducted, emphasizing those that transitioned to journal publications, alongside the associated commentary and Twitter ac-tivity. The dataset was bifurcated into comments by biomedical experts versus those by non-experts, encompassing both academic and general public perspectives. Findings revealed that while peers dominated nearly half the preprint discussions, their presence in Twitter dialogues was markedly diminished. Yet, intriguingly, the themes explored by these two groups diverged considerably. Preprints emerged as potent boundary objects, reinforcing, rather than obscuring, the delineation between scientific and non-scientific discourse. They serve as crucial conduits for knowledge dissemination and foster inter-disciplinary engagements. Nonetheless, the inter-play between scientists and the wider public remains nuanced, necessitating strategies to incor-porate these diverse discussions into the peer review continuum without compromising aca-demic integrity and to cultivate sustained engagement from both experts and the broader community. https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00302
{"title":"Public Engagement with COVID-19 Preprints: Bridging the Gap Between Scientists and Society","authors":"Justus Henke","doi":"10.1162/qss_a_00302","DOIUrl":"https://doi.org/10.1162/qss_a_00302","url":null,"abstract":"\u0000 The surge in preprint server use, especially during the Covid-19 pandemic, necessitates a reex-amination of their significance in the realm of science communication. This study rigorously investigates discussions surrounding preprints, framing them within the contexts of systems theory and boundary objects in scholarly communication. An analysis of a curated selection of COVID-19-related preprints from bioRxiv and medRxiv was conducted, emphasizing those that transitioned to journal publications, alongside the associated commentary and Twitter ac-tivity. The dataset was bifurcated into comments by biomedical experts versus those by non-experts, encompassing both academic and general public perspectives. Findings revealed that while peers dominated nearly half the preprint discussions, their presence in Twitter dialogues was markedly diminished. Yet, intriguingly, the themes explored by these two groups diverged considerably. Preprints emerged as potent boundary objects, reinforcing, rather than obscuring, the delineation between scientific and non-scientific discourse. They serve as crucial conduits for knowledge dissemination and foster inter-disciplinary engagements. Nonetheless, the inter-play between scientists and the wider public remains nuanced, necessitating strategies to incor-porate these diverse discussions into the peer review continuum without compromising aca-demic integrity and to cultivate sustained engagement from both experts and the broader community.\u0000 \u0000 \u0000 https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00302\u0000","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140384783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Increasing the availability of research datasets is a goal of many stakeholders in science, and monitoring related practices requires definitions of the entity in question. There are several, largely overlapping, definitions for open data. However, they have so far not been translated into operationalizations which would allow to detect in a structured and reproducible way, whether for a specific research article underlying data have been shared. Here, we propose a detailed set of criteria to enable such assessments, focusing on biomedical research. We have used these criteria to distribute performance-oriented funding at a large university hospital and to monitor data sharing practices in a dashboard. In addition to fully open data, we include separate criteria for datasets with restricted access, which we also reward. The criteria are partly inspired by the FAIR principles, particularly findability and accessibility, but do not map onto individual principles. The criteria attribute open data status in a binary fashion, both to individual datasets and, ultimately, articles with which they were shared. The criteria allow a verifiable assessment, based on automated and manual screening steps, which we have implemented and validated, as described elsewhere. Here, we focus conceptually on assessing the presence of shared data. https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00301
{"title":"Operationalizing open and restricted-access data – Formulating verifiable criteria for the openness of datasets mentioned in biomedical research articles","authors":"Evgeny Bobrov, N. Riedel, Miriam Kip","doi":"10.1162/qss_a_00301","DOIUrl":"https://doi.org/10.1162/qss_a_00301","url":null,"abstract":"\u0000 Increasing the availability of research datasets is a goal of many stakeholders in science, and monitoring related practices requires definitions of the entity in question. There are several, largely overlapping, definitions for open data. However, they have so far not been translated into operationalizations which would allow to detect in a structured and reproducible way, whether for a specific research article underlying data have been shared. Here, we propose a detailed set of criteria to enable such assessments, focusing on biomedical research. We have used these criteria to distribute performance-oriented funding at a large university hospital and to monitor data sharing practices in a dashboard. In addition to fully open data, we include separate criteria for datasets with restricted access, which we also reward. The criteria are partly inspired by the FAIR principles, particularly findability and accessibility, but do not map onto individual principles. The criteria attribute open data status in a binary fashion, both to individual datasets and, ultimately, articles with which they were shared. The criteria allow a verifiable assessment, based on automated and manual screening steps, which we have implemented and validated, as described elsewhere. Here, we focus conceptually on assessing the presence of shared data.\u0000 \u0000 \u0000 https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00301\u0000","PeriodicalId":34021,"journal":{"name":"Quantitative Science Studies","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140384808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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