A global interdisciplinary platform for academic age-normalized researcher evaluation

Academic departments, research clusters and evaluators analyze author and citation data to measure research impact and to support strategic planning. We created Scholar Metrics Scraper (SMS) to automate the retrieval of bibliometric data for a group of researchers. The project contains Jupyter notebooks that take a list of researchers as an input and exports a CSV file of citation metrics from Google Scholar (GS) to visualize the group's impact and collaboration. A series of graph outputs are also available. SMS is an open solution for automating the retrieval and visualization of citation data.

Background: Recent articles have presented a bleak view of career prospects in biomedical research in the US. Too many PhDs and postdocs are trained for too few research positions, creating a “holding-tank” of experienced senior postdocs who are unable to get a permanent position. Coupled with relatively low salaries and high levels of pressure to publish in top-tier academic journals, this has created a toxic environment that is perhaps responsible for a recently observed decline in biomedical postdocs in the US, the so-called “postdocalypse”. Methods: To address the gulf of information relating to working habits and attitudes of UK-based academic biomedical researchers, a link to an online survey was included in an article published in the Guardian newspaper. Survey data were collected between 21st March 2016 and 6th November 2016 and analysed to examine discrete profiles for three major career stages: PhD, postdoc and principal investigator. Results: Overall, the data presented here echo trends observed in the US: The 520 UK-based biomedical researchers responding to the survey reported feeling disillusioned with academic research, due to the low chance of getting a permanent position and the long hours required at the bench. Also like the US, large numbers of researchers at each distinct career stage are considering leaving biomedical research altogether. Conclusions: There are several systemic flaws in the academic scientific research machine – for example the continual overproduction of PhDs and the lack of stability in the early-mid stages of a research career - that are slowly being addressed in countries such as the US and Germany. These data suggest that similar flaws also exist in the UK, with a large proportion of respondents concerned about their future in research. To avoid lasting damage to the biomedical research agenda in the UK, addressing such concerns should be a major priority.

BackgroundIntricate maps of science have been created from citation data to visualize the structure of scientific activity. However, most scientific publications are now accessed online. Scholarly web portals record detailed log data at a scale that exceeds the number of all existing citations combined. Such log data is recorded immediately upon publication and keeps track of the sequences of user requests (clickstreams) that are issued by a variety of users across many different domains. Given these advantages of log datasets over citation data, we investigate whether they can produce high-resolution, more current maps of science.MethodologyOver the course of 2007 and 2008, we collected nearly 1 billion user interactions recorded by the scholarly web portals of some of the most significant publishers, aggregators and institutional consortia. The resulting reference data set covers a significant part of world-wide use of scholarly web portals in 2006, and provides a balanced coverage of the humanities, social sciences, and natural sciences. A journal clickstream model, i.e. a first-order Markov chain, was extracted from the sequences of user interactions in the logs. The clickstream model was validated by comparing it to the Getty Research Institute's Architecture and Art Thesaurus. The resulting model was visualized as a journal network that outlines the relationships between various scientific domains and clarifies the connection of the social sciences and humanities to the natural sciences.ConclusionsMaps of science resulting from large-scale clickstream data provide a detailed, contemporary view of scientific activity and correct the underrepresentation of the social sciences and humanities that is commonly found in citation data.

Community-engaged and participatory methods are increasingly used by academic researchers. Power dynamics in academia shape the landscape in which researchers implement these methods, yet are rarely understood and intentionally addressed. Through the lens of social work researchers at different career stages, this article describes how power relations within academia present challenges to community-engaged and participatory research across four stages of the research process (participant/researcher relationship, study formulation, conducting research, and publication/dissemination). These challenges are located within four domains of power identified by intersectionality scholars: Institutional policies such as those for tenure and promotion and for research administration presented challenges with the structural domain. Decisions about how to implement and enforce policies and operationalize what counts for tenure and promotion were identified as challenges within the disciplinary domain. Norms about who belongs on a university campus and what types of research are valued exemplified the cultural domain, while dynamics about who is viewed as an expert exemplified challenges the interpersonal domain. Across the research process, the article identifies potential solutions and suggests strategies for intentional consideration by academic researchers and their institutions to conduct meaningful, inclusive, and socially just research that shares power with community.

Data sharing by researchers is a centerpiece of Open Science principles and scientific progress. For a sample of 6019 researchers, we analyze the extent/frequency of their data sharing. Specifically, the relationship with the following four variables: how much they value data citations, the extent to which their data-sharing activities are formally recognized, their perceptions of whether sufficient credit is awarded for data sharing, and the reported extent to which data citations motivate their data sharing. In addition, we analyze the extent to which researchers have reused openly accessible data, as well as how data sharing varies by professional age-cohort, and its relationship to the value they place on data citations. Furthermore, we consider most of the explanatory variables simultaneously by estimating a multiple linear regression that predicts the extent/frequency of their data sharing. We use the dataset of the State of Open Data Survey 2019 by Springer Nature and Digital Science. Results do allow us to conclude that a desire for recognition/credit is a major incentive for data sharing. Thus, the possibility of receiving data citations is highly valued when sharing data, especially among younger researchers, irrespective of the frequency with which it is practiced. Finally, the practice of data sharing was found to be more prevalent at late research career stages, despite this being when citations are less valued and have a lower motivational impact. This could be due to the fact that later-career researchers may benefit less from keeping their data private.

Fishery resources in Nigeria are experiencing a rapid decline, underscoring the urgent need for effective biodiversity surveys and comprehensive species documentation to guide conservation and management efforts. However, traditional methods for fish surveys, such as gillnets and trawl surveys, are intrusive, time-intensive, and costly. Environmental DNA (eDNA) metabarcoding has emerged as a non-invasive method for surveying fish diversity, yet its application in Nigerian aquatic ecosystems remains limited. This study evaluated the potential and limitations of 12S rRNA-based eDNA metabarcoding to survey fish communities across three water bodies (Jebba Hydroelectric Power Dam, Asa River, and Lagos lagoon). We also compared fish community composition obtained from our eDNA samples with previously published inventories based on traditional sampling techniques. Our study revealed several advantages of using eDNA metabarcoding for fish biodiversity surveys in Nigeria, including the rapid and non-invasive identification of fish taxa, the detection of differences in community composition and biodiversity metrics, the identification of species overlooked by traditional methods, and the ability to detect both threatened and invasive species. However, we identified three major constraints that must be addressed for the successful integration of eDNA sampling techniques in Nigeria. First are the logistical challenges, including determining the optimal number of replicates, selecting the appropriate water volume per site, determining the required number of sampling sites per study area, and identifying the most suitable time within the season for sampling. Second is the lack of a comprehensive DNA reference database for accurate species identification. Third are the issues with primer and marker specificity. Until a comprehensive regional DNA reference library is developed, suitable molecular markers are optimized, and standardized field protocols for eDNA sampling are established, we recommend using eDNA metabarcoding as a complementary survey tool rather than a replacement for conventional fish survey methods in Nigeria.

The introduction of OpenAI ChatGPT has sparked a lively discussion among researchers in academia, investors in business sector, and governments in policymaking circles. Numerous concerns have been expressed by Commentators from various scientific domains. Therefore, this study is an attempt to review the existing research pertaining to OpenAI's ChatGPT using bibliometric analysis. The study reviewed 210 research collected from Web of Sciences covering all disciplines. The analysis was based on (i) co-occurrence (keywords), (ii) citations and (iii) co-authorship. We found, keywords such as “ChatGPT, artificial intelligence, OpenAI, AI, education, chatbots, natural language processing, medicine, and management” are the most frequently used in OpenAI ChatGPT. Moreover, we identified that areas including cryptocurrencies, healthcare, future nursing, information value, non-human authors, algorithmic bias, clinical otolaryngology, clinical decision-making, large language models, generative pre-trained transfer are not covered deeply investigated using OpenAI ChatGPT. Finally, we found around 155 authors from 109 institutions located in 28 countries led by United Sated and Germany are actively engaged in producing research using OpenAI ChatGPT.

Background: The international standing of a nation is based on its status in science production. Objectives: We aimed to assess the scientific growth and global ranking of Iran regarding the domain of medical sciences, based on the Scopus database. Methods: In the scientometric evaluation, we considered two indicators for the evaluation of Iran's global ranking in medical sciences and medicine, as a subcategory of medical science namely the number of documents and citations using the Scopus database (ranking of countries was extracted from the SCImago website). Results: Iran’s trend in the number of documents and citations was progressive. This improvement trend had a slower slope in the medical sciences and emergency medicine. Conclusion: The results showed Iran's scientific growth to be consistent. It is recommended that indexing Iranian journals in the Scopus database, promoting scientific collaborations, and providing the necessary funding could promote Iran's scientific growth.

Specific and general information sharing among competing academic researchers

Environmental DNA (eDNA) metabarcoding is a powerful tool that can enhance marine ecosystem/biodiversity monitoring programs. Here we outline five important steps managers and researchers should consider when developing eDNA monitoring program: (1) select genes and primers to target taxa; (2) assemble or develop comprehensive barcode reference databases; (3) apply rigorous site occupancy based decontamination pipelines; (4) conduct pilot studies to define spatial and temporal variance of eDNA; and (5) archive samples, extracts, and raw sequence data. We demonstrate the importance of each of these considerations using a case study of eDNA metabarcoding in the Ports of Los Angeles and Long Beach. eDNA metabarcoding approaches detected 94.1% (16/17) of species observed in paired trawl surveys while identifying an additional 55 native fishes, providing more comprehensive biodiversity inventories. Rigorous benchmarking of eDNA metabarcoding results improved ecological interpretation and confidence in species detections while providing archived genetic resources for future analyses. Well designed and validated eDNA metabarcoding approaches are ideally suited for biomonitoring applications that rely on the detection of species, including mapping invasive species fronts and endangered species habitats as well as tracking range shifts in response to climate change. Incorporating these considerations will enhance the utility and efficacy of eDNA metabarcoding for routine biomonitoring applications.

Environmental DNA (eDNA) metabarcoding can rapidly characterise biodiversity, yet its accuracy and effectiveness are limited by incomplete DNA barcode reference databases. We evaluated how comprehensive reference databases that include sequence variation within genomes (intragenomic) and across individuals and species (intergenomic) improve eDNA-based biodiversity assessments. We collected coral tissue and water samples at deep sites offshore Puerto Rico for reference barcoding and eDNA metabarcoding. Genome skimming coral specimens yielded 28S barcodes for 314 of 346 samples (90.8%) and revealed divergent intragenomic 28S lineages in multiple octocoral families. Incorporating local reference barcodes substantially changed ASV taxonomic classifications: 22 ASVs (8.9%) gained genus-level resolution, 19 ASVs (7.7%) were reassigned to different genera, and 14 ASVs (5.7%) lost incorrect genus-level classifications. Thus, incomplete reference databases produce not only unclassified ASVs but also false positive detections and ecologically meaningful misclassifications. When intragenomic 28S lineages were excluded from the reference database, 18 ASVs (7.4%) could not be classified to family or genus, demonstrating that unrecognised intragenomic variation can be mistaken for unsampled taxa. Integrating reference genome skimming and eDNA metabarcoding expanded known coral family richness by 36% at depths shallower than 1000 m and by 181% at depths greater than 1000 m. eDNA also detected two coral families previously unknown off Puerto Rico and nearby islands, underscoring its potential for biodiversity discovery.

The investigation of wildlife trade and crime has benefitted from advances in technology and scientific development in a variety of fields. Stable isotope analysis (SIA) represents one rapidly developing approach that has considerable potential to contribute to wildlife trade investigation, especially in complementing other methods including morphological, genetic, and elemental approaches. Here, we review recent progress in the application of SIA in wildlife trade research to highlight strengths, shortcomings, and areas for development in the future. SIA has shown success in species identification, determination of geographic provenance, and differentiating between captive-bred and wild individuals. There are also emerging applications of SIA in wildlife trade research including the use of labelling for traceability, more in-depth analyses such as compound specific isotope analysis (CSIA), the use of trace metal isotopes, and monitoring the health of individuals (e.g. dietary history and nutritional status). While these applications have shown the utility of SIA in wildlife trade investigations, there are a number of limitations and issues where standardisation of analytical procedures would improve the comparability and interpretation of results. First, there is high variation within many stable isotopes geographically and within tissues - this variation presents opportunities for tracking and monitoring but can also challenge detection of patterns when variation is high. Second, the choice of isotopes and tissues within an organism (and ideally, multiple isotopes and tissues) should be considered carefully as different isotopes and tissue types have variable strengths and weaknesses depending on the research question. Third, validation of SIA methods remains underutilised in the field but is critical for applying SIA broadly to wildlife trade investigations and, particularly, for applications in forensics and in court. Fourth, standards are essential for comparisons across studies. Fifth, while some reference databases exist for the use of SIA in wildlife trade research (e.g. ivory), there are still few comprehensive reference databases available. Development of robust reference databases should be a priority for advancing the use of SIA in wildlife trade research, and ecological study more broadly. Ultimately, further recognition of these primary challenges (and development of solutions) within wildlife SIA research will improve the potential for this technique in tackling the threat of overexploitation to global biodiversity - particularly in concert with the application of other investigative techniques such as genetics and elemental analysis.

De novo sequencing offers an alternative to database search methods for peptide identification from mass spectra. Since it does not rely on a predetermined database of expected or potential sequences in the sample, de novo sequencing is particularly appropriate for samples lacking a well-defined or comprehensive reference database. However, the low accuracy of many de novo sequence predictions has prevented the widespread use of the variety of sequencing tools currently available. Here, we present a new open-source tool, Postnovo, that postprocesses de novo sequence predictions to find high-accuracy results. Postnovo uses a predictive model to rescore and rerank candidate sequences in a manner akin to database search postprocessing tools such as Percolator. Postnovo leverages the output from multiple de novo sequencing tools in its own analyses, producing many times the length of amino acid sequence information (including both full- and partial-length peptide sequences) at an equivalent false discovery rate (FDR) compared to any individual tool. We present a methodology to reliably screen the sequence predictions to a desired FDR given the Postnovo sequence score. We validate Postnovo with multiple data sets and demonstrate its ability to identify proteins that are missed by database search even in samples with paired reference databases.

The comprehension of deformation mechanisms and polycrystalline alloy properties is of great importance in materials science. One major challenge remains the measurements of the mechanical field components at the nanoscale. For the past twenty years, the development of nanotechnologies had a great impact on various scientific domains. Here we propose to take advantage of nanotechnologies in order to perform major breakthroughs in material strain determination. Indeed, in this study we investigate the use of metallic nanoparticles gratings to map local strains at the surface of materials. Nanoparticles can play the role of nanogauges, giving access to surface strains at the nanoscale by tracking their displacements during tensile tests performed under Scanning Electron Microscope (SEM). This original method is particularly helpful to map surface strains but also to evidence local heterogeneities as well to provide a direct comparison between local and macroscopic mechanical fields evolution. Therefore, this approach should be considered as an alternative technique both for academic and industrial researchers.

LLM-based representation learning is widely used to build effective information retrieval systems, including scientific domains. For making science more open and affordable, it is important that these systems support multilingual (and cross-lingual) search and do not require significant computational power. To address this we propose SciRus-tiny, light multilingual encoder trained from scratch on 44 M abstracts (15B tokens) of research papers and then tuned in a contrastive manner using citation data. SciRus-tiny outperforms SciNCL, English-only SOTA-model for scientific texts, on 13/24 tasks, achieving SOTA on 7, from SciRepEval benchmark. Furthermore, SciRus-tiny is much more effective than SciNCL: it is almost 5x smaller (23 M parameters vs. 110 M), having approximately 2x smaller embeddings (312 vs. 768) and 2x bigger context length (1024 vs. 512). In addition to the tiny model, we also propose the SciRus-small (61 M parameters and 768 embeddings size), which is more powerful and can be used for complicated downstream tasks. We further study different ways of contrastive pre-training and demonstrate that almost SOTA results can be achieved without citation information, operating with only title-abstract pairs.