Search Software Research Articles

Oil Pollution and Public Health: A Bibliometric Review of Trends, Key Contributions, and Future Perspectives

ABSTRACTOil spills constitute one of the gravest threats to ecosystems and living beings. Exposure to oil pollution has the potential to yield a range of adverse health consequences for individuals. The aim of this study is to examine the intricate relationship between oil pollution and public health through a bibliometric review. The proposed methodology is broken down into four stages: (1) Search criteria, (2) Data collection, (3) Data processing and software selection, and (4) Data analysis and interpretation. The results highlighted a rise in scientific production over time, peaking in 2019. Research production is predominantly led by the United States. Leading authors in this domain include D. P. Sandler and L. S. Engel, notable for their volume of publications and citations. Co‐citation and keyword co‐occurrence analyses revealed “oil spill,” “human health,” and “public health” as recurring terms, underlining the core focus of the literature on these subjects. This study concludes that research on oil pollution and public health has surged notably in recent decades, with deep and pressing academic engagement evident in the literature. Future research appears set to follow a robust trajectory, with an increasingly globalized and collaborative emphasis.

Research Trends of Students’ Mathematical Ability on Algebra Materials Based on Gender: Bibliometric Analysis

Mathematical skills are not only used in the world of education but also in various professional fields, such as science, technology, and economics. This study uses blibliometric analysis whose data source is obtained from Google Scholar and uses the keyword of students' mathematical ability in algebra material based on gender with a maximum of 1000 data. Researchers analyzed 948 out of 990 articles from 2019-2024. This study uses Publis or Perish software for data search through Google Scholar and VOSviewer to obtain analysis results that are by the objectives of this research was carried out to find out the research trends of mathematical abilities based on gender and to find out future research opportunities. This study shows that the most publications are in 2023 with 200 publications. The results of the research provide recommendations for variables that can be researched in the future, namely algebraic thinking and high mathematical ability.

Impact of Kiwifruit Consumption on Cholesterol Metabolism in Rat Liver: A Gene Expression Analysis in Induced Hypercholesterolemia.

Cholesterol is vital in various bodily functions, such as maintaining cell membranes, producing hormones, etc. However, imbalances, like hypercholesterolemia, can lead to diseases such as cancer, kidney disease, non-alcoholic fatty liver disease, and cardiovascular conditions. This study explores the impact of kiwifruit consumption, specifically Actinidia arguta cultivar Geneva and Actinidia deliciosa cultivar Hayward, on cholesterol and lipid metabolism in rat liver. Rats were divided into groups: a 1% cholesterol control group (Ch), a 5% Geneva kiwifruit-supplemented group (ChGENE), and a 5% Hayward kiwifruit-supplemented group (ChHAYW). Gene expression was analyzed using Gene Spring v.14. Gene ontology, pathway analysis, miRNA, and transcription factor prediction were performed using DAVID, Reactome, and miRNet. In addition, we used Agilent Literature Search software to gain further insights. Statistical analysis identified 72 genes in ChGENE-Ch and 2 genes in ChHAYW-Ch comparison. Key genes involved in cholesterol metabolism pathways, including PCSK9, SCD1, SLC27A5, HMGCR, and DHCR24, showed lower expression in the kiwifruit-supplemented groups. The genes mentioned above showed lower expression in the kiwifruit-supplemented group, probably contributing to the liver lipid level reduction. Further analysis identified miRNA-26a, miRNA-29a/b/c, miRNA-33a/b, and miRNA-155 targeting hub genes. Our findings suggest that dietary supplementation with kiwifruit, particularly the Geneva cultivar, reduces fat accumulation in the liver of rats with hypercholesterolemia, likely through downregulation of critical genes involved in cholesterol metabolism. These studies highlight the potential of kiwifruit as a part of a dietary strategy to manage cholesterol levels.

The Identity Algorithm: How the Most Popular Electron Ionization Mass Spectral Library Search Engine Actually Works.

The Identity algorithm implemented in the MS Search (NIST) software is widely used for library searches of gas chromatography/mass spectrometry data against electron ionization mass spectral databases. It has been available to researchers since 1993, with the release of MS Search 1.5a. Despite its extensive use, the operational details of the algorithm have remained ambiguous. Attempts to replicate the algorithm have been unsuccessful because, as found in this research, the description in the manual is neither fully complete nor accurate. The main novelty of this work is the development of a unique approach for deconstructing the Identity algorithm. It is purely based on analyzing library search results obtained from several groups of synthetic mass spectra, each tailored to isolate and examine specific components of the algorithm. This approach facilitated a comprehensive understanding of the Identity algorithm and led to the development of a custom implementation that fully replicates the results obtained from the original MS Search software. The custom implementation of the Identity algorithm is now available in the mssearchr R package, enhancing accessibility for researchers.

Kajian Pendidikan Indonesia di Era Revolusi Industri 4.0: Kearifan Lokal dalam Perspektif Bimbingan dan Konseling

The development of national education began after Indonesia’s independence day, and then the government gradually expanded national education, so that education in Indonesia has entered 3 eras, there was modern education, post-modern education, and millennial education. The most recent curriculum launched by the Ministry of Education and Culture is the Independent Curriculum during the Covid-19 pandemic. Essentially, we are required to follow the development of the era, but we must not abandon the foundations of Indonesian culture. Multicultural education based on local wisdom is a requirement that must be implemented, particularly in the field of guidance and counseling. Local wisdom has been established to improve good conditions for students at all levels of education. Research objective: to explain and describe the study of implementing education in the areas of guidance and counseling throughout the Industrial Revolution. Method: literature review by applying 7 steps of literature review by using collection information tools that as scientific literature search software. Results: The 10 journals determined that multicultural education based on local wisdom can increase positive aspects such as resilience, self-awareness, interpersonal communication, self-confidence, self-control, and self-openers when used in guidance and counseling services. Conclusion: In the industrial revolution age, local wisdom is significant and useful in applying guidance and counseling 4.0.

Competition concerns with foundation models: a new feast for big tech?

ABSTRACT The paper explores how Generative AI intersects with Competition Law, focusing on Foundation Models (FMs) and Large Language Models (LLMs). It examines industry dynamics and identifies key competition issues like entry barriers, tying, leveraging, and acquisitions. It highlights the supply chain's importance and looks at how FMs are integrated into search software, chatbots, and productivity tools, particularly noting entry barriers such as computing power and data collection. It suggests that FMs might require new approaches to market delineation, possibly creating a separate relevant market for data. The paper also discusses various cases pertaining to tying and leveraging and highlights the difficulty in proving tying due to the blurred lines between traditional search engines and AI chatbots. It illustrates how competition assessments for acquisitions may require changes due to data being a highly flexible commodity for the industry. The paper concludes by calling for increased scrutiny and regulation for the industry.

Natural Language Processing-based Comparative Study of Translating Characteristics from the Version of Tao Te Ching

This paper established a small-scaled corpus based on modern corpus technology, applied corpus search software Antconc3.5.8 and SPSS 16 to analyze two translating versions of Tao Te Ching from John C.H.Wu and Arthur Wayley in terms of vocabulary, syntax and context. The paper further analyzes and illustrates reasons of these two translating versions from original text understanding, translating purpose understanding and translating strategy so as to provide references with translating style study of Tao Te Ching, translators’ characteristics research and parallel corpus establishment.

Do firms’ performance act as a catalyst of innovation: empirical evidence from innovative Indian manufacturing firms

PurposeThis paper aims to explore whether firms’ performance determines innovation using a sample of Indian manufacturing firms. The impact of innovation on firms’ performance across specific countries has been discussed in the literature. However, the effect of firms’ performance on innovation output, especially for a developing country like India, remains an open question. Against this backdrop, this paper investigates whether firms’ performance determines innovation in Indian manufacturing firms.Design/methodology/approachThe authors use patent filing information to instrument innovation and total factor productivity to instrument firms’ performance. The patent data are collected from the Patent Search and Analysis Software database and firm-level data from the Centre for Monitoring Indian Economy’s Prowess database. The study uses a sample of 309 Indian manufacturing firms from 2005 to 2021. Given the count nature of the data set used in this study coupled with over-dispersion issues, the authors have used the negative binomial regression to estimate the empirical specification of the models. There could be a possible problem of endogeneity due to the contemporary nature of innovation and firms’ performance. Therefore, to address the possible issues of endogeneity in the model, the authors have used the Generalized Method of Moments (GMM) estimators for more robustness checks of the empirical results.FindingsThe empirical results exhibit a positive and significant impact of firms’ performance on the innovation output, validating that firms’ performance determines innovation in Indian manufacturing firms. The posterior estimation results using GMM estimation also corroborate that firms’ productivity is a determining factor for the innovation output of Indian manufacturing firms. Furthermore, empirical results exhibit that the ex ante innovativeness of the firms substantially affects the current innovation. This validates that the firms’ prior experience, learning by doing and past innovative efforts are more likely to precipitate more innovation in the current period.Originality/valueThis paper’s main contribution is empirically estimating whether firms’ performance determines innovation, which is hardly discussed in the existing innovation literature, specifically using Indian manufacturing industries. Further, it adds to the existing literature in two other prominent ways. First, this paper investigates whether firms require ex ante expertise to innovate or if a firm starting from scratch can innovate significantly without any hindrances. Second, it enriches the literature by instrumenting innovation in output terms with the patent application against input measures of innovation, such as research and development expenditures, acquisition of machinery and equipment, while discussing the relationship between firms’ performance and innovation, specifically in the context of a developing economy like India.

Fever of unknown origin: A 12-year case series in Colombia.

Fever of unknown origin (FUO) is a diagnostic challenge with highly heterogeneous causes. Its etiology can change according to the studied regions, and the chance of reaching a diagnosis depends on available resources. The aim of this study is to describe the clinical characteristics, etiology and the usefulness of diagnostic aids in cases of FUO managed over 12 years in a Colombian reference center. Single-institution retrospective case series. All cases of FUO between 2006 and 2017 were identified with the help of an electronic medical record search software. Cases of adults with fever for more than three weeks who remained undiagnosed after three days of hospitalization are described. Of 1,009 cases evaluated, 112 cases met the inclusion criteria (median age 43 years, 66% men). The etiologies identified were infectious (31.2%), inflammatory (20.5%), neoplastic (14.3%), and miscellaneous (2.7%) diseases. 31.2% remained without etiological diagnosis. The most frequent conditions were tuberculosis (17%), Hodgkin's lymphoma (7.1%), systemic lupus erythematosus (6.3%), disseminated histoplasmosis, and adult Still's disease. Contrast tomography and biopsies were the studies that most frequently supported or confirmed the final diagnosis. This series of contemporary Latin American cases suggests that the categories of FUO etiologies are similar to those reported in studies from developed countries, with tuberculosis being the most frequent cause in our setting. Our results highlight the importance of tomography-guided invasive studies in the diagnostic approach to FUO.

SATIN: a micro and mini satellite mining tool of total genome and coding regions with analysis of perfect repeats polymorphism in coding regions

BackgroundTandem repeats are specific sequences in genomic DNA repeated in tandem that are present in all organisms. Among the subcategories of TRs we have Satellite repeats, that is divided into macrosatellites, minisatellites, and microsatellites, being the last two of specific interest because they can identify polymorphisms between organisms due to their instability. Currently, most mining tools focus on Simple Sequence Repeats (SSR) mining, and only a few can identify SSRs in the coding regions.ResultsWe developed a microsatellite mining software called SATIN (Micro and Mini SATellite IdentificatioN tool) based on a new sliding window algorithm written in C and Python. It represents a new approach to SSR mining by addressing the limitations of existing tools, particularly in coding region SSR mining. SATIN is available at https://github.com/labgm/SATIN.git. It was shown to be the second fastest for perfect and compound SSR mining. It can identify SSRs from coding regions plus SSRs with motif sizes bigger than 6. Besides the SSR mining, SATIN can also analyze SSRs polymorphism on coding-regions from pre-determined groups, and identify SSRs differentially abundant among them on a per-gene basis. To validate, we analyzed SSRs from two groups of Escherichia coli (K12 and O157) and compared the results with 5 known SSRs from coding regions. SATIN identified all 5 SSRs from 237 genes with at least one SSR on it.ConclusionsThe SATIN is a novel microsatellite search software that utilizes an innovative sliding window technique based on a numerical list for repeat region search to identify perfect, and composite SSRs while generating comprehensible and analyzable outputs. It is a tool capable of using files in fasta or GenBank format as input for microsatellite mining, also being able to identify SSRs present in coding regions for GenBank files. In conclusion, we expect SATIN to help identify potential SSRs to be used as genetic markers.

Anti-Obesity Therapeutic Targets Studied In Silico and In Vivo: A Systematic Review.

In the age of information technology and the additional computational search tools and software available, this systematic review aimed to identify potential therapeutic targets for obesity, evaluated in silico and subsequently validated in vivo. The systematic review was initially guided by the research question "What therapeutic targets have been used in in silico analysis for the treatment of obesity?" and structured based on the acronym PECo (P, problem; E, exposure; Co, context). The systematic review protocol was formulated and registered in PROSPERO (CRD42022353808) in accordance with the Preferred Reporting Items Checklist for Systematic Review and Meta-Analysis Protocols (PRISMA-P), and the PRISMA was followed for the systematic review. The studies were selected according to the eligibility criteria, aligned with PECo, in the following databases: PubMed, ScienceDirect, Scopus, Web of Science, BVS, and EMBASE. The search strategy yielded 1142 articles, from which, based on the evaluation criteria, 12 were included in the systematic review. Only seven these articles allowed the identification of both in silico and in vivo reassessed therapeutic targets. Among these targets, five were exclusively experimental, one was exclusively theoretical, and one of the targets presented an experimental portion and a portion obtained by modeling. The predominant methodology used was molecular docking and the most studied target was Human Pancreatic Lipase (HPL) (n = 4). The lack of methodological details resulted in more than 50% of the papers being categorized with an "unclear risk of bias" across eight out of the eleven evaluated criteria. From the current systematic review, it seems evident that integrating in silico methodologies into studies of potential drug targets for the exploration of new therapeutic agents provides an important tool, given the ongoing challenges in controlling obesity.

A grape-supplemented diet prevented ultraviolet (UV) radiation-induced cataract by regulating Nrf2 and XIAP pathways

The purpose of this study is to investigate if grape consumption, in the form of grape powder (GP), could protect against ultraviolet (UV)-induced cataract. Mice were fed with the regular diet, sugar placebo diet, or a grape diet (regular diet supplemented with 5%, 10%, and 15% GP) for 3 months. The mice were then exposed to UV radiation to induce cataract. The results showed that the GP diet dose-dependently inhibited UV-induced cataract and preserved glutathione pools. Interestingly, UV-induced Nrf2 activation was abolished in the groups on the GP diet, suggesting GP consumption may improve redox homeostasis in the lens, making Nrf2 activation unnecessary. For molecular target prediction, a total of 471 proteins regulated by GP were identified using Agilent Literature Search (ALS) software. Among these targets, the X-linked inhibitor of apoptosis (XIAP) was correlated with all of the main active ingredients of GP, including resveratrol, catechin, quercetin, and anthocyanins. Our data confirmed that GP prevented UV-induced suppression of XIAP, indicating that XIAP might be one of the critical molecular targets of GP. In conclusion, this study demonstrated that GP protected the lens from UV-induced cataract development in mice. The protective effects of GP may be attributed to its ability to improve redox homeostasis and activate the XIAP-mediated antiapoptotic pathway.

A Needle in a Cosmic Haystack: A Review of FRB Search Techniques

Ephemeral Fast Radio Bursts (FRBs) must be powered by some of the most energetic processes in the Universe. That makes them highly interesting in their own right, and as precise probes for estimating cosmological parameters. This field thus poses a unique challenge: FRBs must be detected promptly and immediately localised and studied based only on that single millisecond-duration flash. The problem is that the burst occurrence is highly unpredictable and that their distance strongly suppresses their brightness. Since the discovery of FRBs in single-dish archival data in 2007, detection software has evolved tremendously. Pipelines now detect bursts in real time within a matter of seconds, operate on interferometers, buffer high-time and frequency resolution data, and issue real-time alerts to other observatories for rapid multi-wavelength follow-up. In this paper, we review the components that comprise a FRB search software pipeline, we discuss the proven techniques that were adopted from pulsar searches, we highlight newer, more efficient techniques for detecting FRBs, and we conclude by discussing the proposed novel future methodologies that may power the search for FRBs in the era of big data astronomy.

TransientX: A high-performance single-pulse search package

Context. Radio interferometers composed of a large array of small antennas possess large fields of view, coupled with high sensitivities. For example, the Karoo Array Telescope (MeerKAT) achieves a gain of up to 2.8 KJy−1 across its >1 deg2 field of view. This capability significantly enhances the survey speed for pulsars and fast transients. It also introduces challenges related to the high data rate, which reaches a few Tb s−1 for MeerKAT, and it requires substantial computing power. Aims. To handle the high data rate of surveys, we have developed a high-performance single-pulse search software called “TransientX”. This software integrates multiple processes into one pipeline, which includes radio-frequency interference mitigation, dedispersion, matched filtering, clustering, and candidate plotting. Methods. In TRANSIENTX, we developed an efficient CPU-based dedispersion implementation using the sub-band dedispersion algorithm. Additionally, TRANSIENTX employs the density-based spatial clustering of applications with noise (DBSCAN) algorithm to eliminate duplicate candidates, using an efficient implementation based on the kd-tree data structure. We also calculate the decrease of signal-to-noise ratio (s/N) resulting from dispersion measure, boxcar width, spectral index, and pulse-shape mismatches. Remarkably, we find that the decrease of S/N resulting from the mismatch between a boxcar-shaped template and a Gaussian-shaped pulse with scattering remains relatively small, at approximately 9%, even when the scattering timescale is ten times that of the pulse width. Additionally, the decrease in the S/N resulting from the spectral index mismatch becomes significant with multi-octave receivers. Results. We have benchmarked the individual processes, including dedispersion, matched filtering, and clustering. Our dedispersion implementation can be executed in real time using a single CPU core on data with 4096 dispersion measure trials, which consist of 4096 channels and have a time resolution of 153 µs. Overall, TRANSIENTX offers the capability for efficient CPU-only real-time single-pulse searching.

Data notes on the proteomics of Dendrobium huoshanense under pb treatment.

Pb stress has a negative impact on plant growth by interfering with photosynthesis and releasing reactive oxygen species, causing major risks such as heavy metal ion accumulation in the soil matrix. A proteomics experiment was conducted to determine whether protein levels of Dendrobium huoshanense changed in response to Pb stress seven to fifteen days after being sprayed with a 200mg/L Pb (NO3)2 solution. The proteomic data we gathered provides a model for investigations into the mechanisms underlying Dendrobium plant resistance to heavy metal stress. A label-free quantitative proteomics approach was employed to examine the variations in protein expression levels of D. huoshanense at different times of Pb(NO3)2 treatment. We submitted the raw data obtained from these proteomics sequencing experiments to the ProteomeXchange database with the accession number PXD047050. 63,194 mass spectra in total were compared after being imported into the Proteome Discoverer software for database search. A total of 12,402 spectral peptides were identified with a confidence level exceeding 99%, which resulted in the identification of 2,449 significantly differential proteins. These proteins can be utilized for screening, functional annotation, and enrichment analysis of differentially expressed proteins before and after heavy metal treatment experiments.

Exploring the phase change and structure of carbon using a deep learning interatomic potential.

Small-scale systems based on periodic boundary conditions often cannot accurately describe real-world situations, especially when conducting molecular dynamics simulations to study phase transitions, where it is very necessary to use large-scale systems. However, studying phase transitions in large-scale systems is an important and difficult task. Though ab initio molecular dynamics (AIMD), based on density functional theory (DFT), provides advantages in terms of accuracy, it is very difficult to study phase transitions in large-scale systems due to the considerable computational time required. In addition, although traditional empirical potentials are faster, their lower calculation accuracy makes it difficult to use them for phase transition studies. It is crucial to devise a method that has enabled a promising fusion of computational efficiency and precision to effectively investigate phase transitions in large-scale systems. In this work, the obtained machine learning potential function of carbon through deep neural networks not only demonstrates strong scalability but also effectively enables the study of the formation mechanisms of amorphous diamond and polycrystalline diamond using C60 crystals and graphene as precursors under high-pressure high-temperature conditions (HPHT). Furthermore, the structure search software (AIRSS) was used to generate numerous initial structures which were optimized using the machine learning potential, a process which led to finding new structural clusters of carbon. Interestingly, the predictive capabilities of the machine learning potential for symmetric and asymmetric carbon clusters aligned well with the Gaussian approximation potential (GAP), yet the former demonstrated higher computational efficiency, making it more suitable for carbon material research. The results of this work signify significant progress in the field of carbon transition study, opening up new possibilities for exploring and understanding carbon materials with improved computational efficacy.

Crystal structure prediction and property calculation of copper-oxygen compounds using innovative search software from first principles.

A Bayesian optimisation algorithm for deep learning crystal structure prediction software (CBD-GM) is used to predict the structures of Cu(I) and Cu(II) oxides of 2D and 3D materials. Two known 2D structures and two known 3D structures were anticipated, in addition to the prediction of 5 novel structures. All nine structures were optimised and analysed using density-functional theory (DFT). Firstly, DFT calculations using the PBE functional indicate that the structures should be thermodynamically and dynamically stable. Secondly, we calculated the elastic constants using the "stress-strain" method, and the predicted Young's modulus and Poisson's ratios of the materials suggest that they all should have excellent ductile mechanical properties. Calculations of the band structure of the materials performed using the Heyd-Scuseria-Ernzerhof (HSE) hybrid functional indicate that some of the materials should be semiconductors with useful bandgaps. The results therefore provide inspiration for the synthesis of new copper oxides for industrial applications.

Exploring Team Role Typologies and Taxonomies

The field of team role typologies and taxonomies is characterized by a lack of conceptual clarity and dominance of a single primary source. This study aims to shed light on the relationships between authors of team role typologies/taxonomies by conducting a systematic literature analysis. The focus is on analyzing authors listed in articles specifically addressing team roles. The resulting analysis provides a list of authors of typologies or taxonomies of team roles as a preparation for further phases of our research. The methodology employed involves a literature review. The selection of articles was based on the Scopus database, with additional searches conducted on publisher websites and ResearchGate to ensure comprehensive coverage. Advanced lexical search software facilitated the identification of authors associated with typologies and taxonomies. The study presents a waypoint list of probably relevant authors for further analysis. Furthermore, the potential of AI tools in literature review and the use of applications for creating schematic maps based on bibliographic records are discussed, with a promise of further investigation in future studies. Overall, this study provides a comprehensive summary of authors involved in the development of team role typologies and taxonomies, providing a foundation for future research to validate and expand upon the identified author relationships. It emphasizes the need to explore the diverse approaches in this field, while also recognizing the significant influence of seminal figures such as M. Belbin. By unraveling the complexities of author relationships, this study paves the way for deeper insights into team dynamics and roles within organizations.

Fast alignment of mass spectra in large proteomics datasets, capturing dissimilarities arising from multiple complex modifications of peptides

BackgroundIn proteomics, the interpretation of mass spectra representing peptides carrying multiple complex modifications remains challenging, as it is difficult to strike a balance between reasonable execution time, a limited number of false positives, and a huge search space allowing any number of modifications without a priori. The scientific community needs new developments in this area to aid in the discovery of novel post-translational modifications that may play important roles in disease.ResultsTo make progress on this issue, we implemented SpecGlobX (SpecGlob eXTended to eXperimental spectra), a standalone Java application that quickly determines the best spectral alignments of a (possibly very large) list of Peptide-to-Spectrum Matches (PSMs) provided by any open modification search method, or generated by the user. As input, SpecGlobX reads a file containing spectra in MGF or mzML format and a semicolon-delimited spreadsheet describing the PSMs. SpecGlobX returns the best alignment for each PSM as output, splitting the mass difference between the spectrum and the peptide into one or more shifts while considering the possibility of non-aligned masses (a phenomenon resulting from many situations including neutral losses). SpecGlobX is fast, able to align one million PSMs in about 1.5 min on a standard desktop. Firstly, we remind the foundations of the algorithm and detail how we adapted SpecGlob (the method we previously developed following the same aim, but limited to the interpretation of perfect simulated spectra) to the interpretation of imperfect experimental spectra. Then, we highlight the interest of SpecGlobX as a complementary tool downstream to three open modification search methods on a large simulated spectra dataset. Finally, we ran SpecGlobX on a proteome-wide dataset downloaded from PRIDE to demonstrate that SpecGlobX functions just as well on simulated and experimental spectra. We then carefully analyzed a limited set of interpretations.ConclusionsSpecGlobX is helpful as a decision support tool, providing keys to interpret peptides carrying complex modifications still poorly considered by current open modification search software. Better alignment of PSMs enhances confidence in the identification of spectra provided by open modification search methods and should improve the interpretation rate of spectra.

The Wikipedia imaginaire: a new media history beyond Wikipedia.org (2001–2022)

This paper presents a media biography of Wikipedia’s data that focuses on the interpretative flexibility of Wikipedia and digital knowledge between the years 2001 and 2022. To do so, I not only follow a strand of media historians who argue that the imagination is an important component for understanding how media change, but I also argue that Wikipedia’s data has been incorporated, re-imagined, and repurposed by sociotechnical projects in ways that have often been side-lined despite acting as the boundary lines of what is considered digital knowledge. It combines Patrice Flichy’s longitudinal theory of technical development as an imaginaire, Frederik Lesage and Simone Natale’s historical approach of biographies of media with an analysis of the interpretative flexibility of new media. Through an eclectic corpus of project websites, new articles, press releases, and blogs, I demonstrate the unexpected ways the online encyclopedia has permeated throughout digital culture over the past twenty years through projects like the Citizendium, Everipedia, Google Search and AI software. As a result of this analysis, I explain how this array of meanings and materials constitutes the Wikipedia imaginaire: a collective activity of sociotechnical development that is fundamental to understanding the ideological and utopian meaning of knowledge with digital culture.