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79 results for “research trends”
A 31-Year Bibliometric Review of Team Effectiveness Research: Evolution, Trends, and Future Directions (DATA).
<p>This dataset is derived from a comprehensive study of team effectiveness over three decades, utilizing bibliometric techniques to examine scholarly publications across multiple databases. The repository includes bibliometric data from 6,051 publications related to team effectiveness, featuring metadata such as authors, titles, publication years, citations, and keywords.</p> <h3>Repository Structure:</h3> <p>The data shared in this repository is organized as follows:</p> <ul> <li><strong>Bibliometrix database.xlsx:</strong> This excel file contains information on 6051 publications in the field of Team Effectiveness downloaded from the Scopus and Web of Science databases using specific search terms and inclusion criteria detailed in our associated paper (see our publication for more information on the methodology). The file contains the necessary headers for direct use in the Biblioshiny interface of the bibliometrix library for R.</li> <li><strong>List of stop words.txt:</strong> This file contains keywords, separated by commas, that we have decided to eliminate from our analyses due to their potential to introduce bias in the results.</li> <li><strong>List of synonyms.txt:</strong> This file contains groups of semantically synonymous keywords, separated by semicolons. Each line represents a group of synonyms, with the first keyword being the one that Bibliometrix will use to replace all other keywords in that line.</li> <li><strong>Appendix [A-H].pdf:</strong> List of appendices that complement the results of the study carried out.</li> </ul> <h3>Recommended Usage:</h3> <div> <div> <div> <div> <p>This dataset is ideal for researchers interested in conducting new analyses of research trends, co-authorship network analysis, and thematic evolution in the field of team effectiveness. For example, researchers can narrow the scope to focus exclusively on team effectiveness in educational contexts. Our publication provides all the necessary details to replicate or extend our methodology. For any queries related to the data, please contact <strong>Yeray Barrios-Fleitas</strong> at <em><a rel="noreferrer">y.d.c.barriosfleitas@utwente.nl</a></em>.</p> </div> </div> </div> </div> <div> </div> <h3>License and Citation:</h3> <p>The data are distributed under the CC BY license. Please cite any use of this dataset using the following format:<br>Barrios Fleitas, Y., Marcella A.M.G., Eysink, T.H., & Rensink , A. (2025). A 31-Year Bibliometric Review of Team Effectiveness Research: Evolution, Trends, and Future Directions. ZENODO, <a href="https://doi.org/10.5281/zenodo.12082529" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12082529</a></p>
TRENDS AND PATTERNS OF RESEARCH PUBLICATIONS AT KYAMBOGO UNIVERSITY BETWEEN 2003 TO 2020
<p>Data for the study was sourced from: Google Scholar, Emerald, Ebscohost, Taylor and Francis and Kyambogo University Scholars’ Space. This took place between June 2019 and June 2020. Data sourced was based on two specific criteria: having acknowledged to being a staff member of the University and having published within the period 2003 to 2020.</p>
Global trends and collaborations in electrochemical methods: a dataset on etching and deposition research
<p><span>This dataset supports the study "Electrochemical Etching vs. Electrochemical Deposition: A Comparative Bibliometric Analysis," which examines scientific publications on electrochemical etching and electrochemical deposition from 1970 to 2023. The dataset is derived from the Science Citation Index Expanded (SCIE) database and includes bibliometric information on publication trends, leading contributors, research areas, and keyword co-occurrences in both fields.</span></p>
Research4Life Landscape and Situation Analysis - Trends in the funding of research in and for LMICs PEST Analysis
<p>A PEST infographic summarising the key trends in the funding of research in and for low and middle-income countries, as identified in the report 'Research4Life Landscape and Situation Analysis' prepared by Research Consulting for the Research4Life partnership.</p>
Trends in research approaches and gender in plant ecology dissertations over four decades
<p>Dissertations are a foundational scientific product; they are the formative product that early-career scientists create and share original knowledge. Methodological approaches used in dissertations vary depending on the research field. In plant ecology, these approaches include observations, experiments (field or controlled-environment), literature reviews, theoretical approaches, or analyses of existing data (including 'big data'). This dataset was created to examine how the emphasis on each of these categories has changed over time, and whether male and female authors differ in the methods employed. The dissertations used for this study were gathered from the <em>Proquest Dissertations and Theses Global</em> (<a href="https://www.proquest.com/pqdtglobal)">https://www.proquest.com/pqdtglobal</a><a href="https://www.proquest.com/pqdtglobal)">)</a> database.</p>
Fig. 2 in Bats of Sicily: historical evidence, current knowledge, research biases and trends
Fig. 2 - Documents in the primary and secondary datasets and the cumulative number of documents issued between 1810 and 2022. / Documenti nei dataset primario e secondario e numero cumulativo di documenti prodotti tra il 1810 e il 2022.
Fig. 5 in Bats of Sicily: historical evidence, current knowledge, research biases and trends
Fig. 5 - Distribution of the primary dataset, secondary dataset, and total documents according to research areas and used methods. / Distribuzione del dataset primario, del dataset secondario e del totale dei documenti in base alle aree di ricerca e ai metodi utilizzati.
Fig. 1 in Bats of Sicily: historical evidence, current knowledge, research biases and trends
Fig. 1 - Flow diagram showing the selection steps of eligible documents obtained from primary and secondary datasets. / Diagramma di flusso che mostra le fasi di selezione dei documenti eligibili ottenuti dai dataset primario e secondario.
Fig. 4 in Bats of Sicily: historical evidence, current knowledge, research biases and trends
Fig. 4 - (A) Bar plot showing the distribution of studies across different habitats (caves, forest, urban area, riparian areas). (B) Species conservation status across Sicilian bat families classified according to the IUCN categories: Least Concern (LC), Vulnerable (VU), Near Threatened (NT), and Data Deficient (DD). / (A) Grafico a barre che mostra la distribuzione degli studi nei diversi habitat (grotte, foreste, aree urbane, aree ripariali). (B) Stato di conservazione delle specie delle famiglie di pipistrelli siciliani classificate secondo le categorie IUCN: Minima preoccupazione (LC), Vulnerabile (VU), Quasi minacciata (NT) e Carenza di dati (DD).
Fig. 3 in Bats of Sicily: historical evidence, current knowledge, research biases and trends
Fig. 3 - Variation of Bat Research Efficiency scores (BRE) in the nine Sicilian provinces. The colour gradient (darker to lighter) indicates a higher to lower BRE. / Variazione dei punteggi di efficienza della ricerca sui pipistrelli (BRE) nelle nove province siciliane. Il gradiente di colore (da più scuro a più chiaro) indica un BRE da più alto a più basso.
Nearly six decades of grazing research published by the Grassland Society of Southern Africa: Trends, recommendations, and gaps
<p>The dataset contains data about articles pulled from a search in Scopus and Google Scholar from the African Journal of Range and Forage Science between 1966 and 2023 using the search terms "grazing" AND"management"; "communal" AND "grazing". The associated R code contains code for natural language processing. </p> <p>The dataset is supplementary to the published journal article: <span>10.2989/10220119.2024.2397952</span></p>
Fig. 2. A in Conservation of Peninsular Malaysia's Karst Herpetofauna: A review of herpetological discoveries, research trends, and challenges
Fig. 2. A, Limnonectes blythii from an unnamed cave in Tambun, Perak; B, Limnonectes cf. macrognathus from Gua Wang Burma, Perlis; C, Phrynoidis asper from Gua Wang Burma, Perlis; D, Hylarana malayana from Gua Wang Burma, Perlis; E, Lycodon cavernicolus from Gua Wang Burma, Perlis; F, Elaphe taeniura ridleyi from Gua Wang Burma, Perlis; G, Cyclemys sp. from Gua Katak, Merapoh; H, Dogania subplana from Gua Wang Burma, Perlis. (Photographs by Liz Price [A & G] and Evan Quah [B–F & H]).
Fig. 1 in Conservation of Peninsular Malaysia's Karst Herpetofauna: A review of herpetological discoveries, research trends, and challenges
Fig. 1. Map of the main regions of karst in Peninsular Malaysia and selected areas of known herpetofaunal endemism. (1) Langkawi Archipelago, Kedah. (2) Nakawan Range that encompasses locations like Gua Wang Burma, Wang Kelian and Ulu Wang, as well as karst outcrops in Perlis like Bukit Cabang and Kuala Perlis. (3) Gunung Baling, Kedah. (4) Lenggong Valley, Perak. (5) Kinta Valley, Perak that encompasses locations such as Gua Kanthan, Gua Tempurung, Gunung Lanno, and Tambun. (6) Batu Caves and Dark Cave, Selangor. (7) Gunung Reng, Kelantan. (8) Gua Musang area, Kelantan that encompasses locations like Gua Bayu and Felda Chiku 7. (9) Merapoh, Pahang that encompasses locations like Gua Gunting and Gua Goyang. (10) Gunung Senyum, Pahang. (Map courtesy of Junn Kitt Foon and adapted from Liew et al. (2016)).
Fig. 3. A in Conservation of Peninsular Malaysia's Karst Herpetofauna: A review of herpetological discoveries, research trends, and challenges
Fig. 3. A, Cnemaspis cf. hangus from Felda Chiku 7, Kelantan; B, Cnemaspis biocellata from Gua Wang Burma, Perlis; C, Cyrtodactylus guakanthanensis from Gua Kanthan, Perak; D, Cyrtodactylus lenggongensis from Lenggong Valley, Perak; E, Cyrtodactylus hidupselamanya from Felda Chiku 7, Kelantan; F, Cyrtodactylus evanquahi from Gunung Baling, Kedah; G, Cyrtodactylus consobrinus from Curtain Cave, Pasoh, Negeri Sembilan; H, Calotes emma from Gua Kelam, Perlis. (Photographs by Ali Shamsul Bahar [G] and Evan Quah [A–F & H])
Fig. 5 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities
Fig. 5. Data sources according to epidemiological level and scale. Representation of the data sources (left column) used for each epidemiological level (central column), and the scale of the corresponding data sources (right). Colors of the left column correspond to general data-sources categories; for example, green corresponds to biological/biodiversity data sources (e.g., Biodiversity repositories and biological general source). Health-related sources are represented in purple (Health gov: governmental, init-program: initiative or programs). Using this broad categorization, most of the sources contribute with data related to the three epidemiological levels, although with an unpaired flow. For example, scientific literature has a lower contribution for hosts/ reservoirs, and biodiversity-biological sources have a minor contribution for pathogens. Most data sources have a global scale meanwhile governmental sources have a relevant contribution to pathogen data. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 3 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities
Fig. 3. Diseases explored in the studies according to the use of GBIF and the taxa class of the causal pathogen. In the right panel: positive studies (i.e., those studies that used GBIF-mediated data for at least one of the variables explored), negative studies in the left. Bars represent the number of studies exploring each disease, and filling colors represent the corresponding taxa class of the disease agent or causal pathogen (Purple scale, with lighter coloration for fungal diseases, followed by parasites, bacteria, and viruses with the darker purple). Abbreviations: the abbreviation Oth (Fungal Oth, Parasite Oth, Bacteria Oth, Virus Oth) represents a category with multiple species, merged to simplify the figure due to the low number of studies of each disease. Ricket-related: diseases related to Rickettsia species; Paras: parasites; Schistos: Schistosomiases; Leishm: Leishmaniases (both cutaneous and visceral); Dis: disease; Bact: bacteria; Fev: fever; V: virus; CoronaV: Coronavirus. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 2 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities
Fig. 2. Research areas identified in the studies. Research areas subcategories are represented in the left axis, and general research area groups in the right axis. Orange circles represent the number of positive studies, the blue circles the negatives, and the black lines between them represent the differences in the number of studies, in which larger lines represent larger differences between positive and negatives. Orange icons correspond to research areas with larger number of positive studies, i.e., positive studies were more related to Biology (Bio), Ecology (Ecol) and Other (Hum Soc: Human society; Phy Env Geo: Physical environmental geology; Earth Atm: Earth and atmospheric sciences). Negative studies were more frequent in research areas with blue icons, including Medical (Med) and Veterinary sciences (Vet: Veterinarian and agriculture). In the green icon (Eng Inf Mat: Engineering, informatics, and mathematics) there was no major differences between groups. Subcategories: Bio Zoo: Biology and zoology; Bio Evo Gen: Biology, evolution, and genetics; Bio Bioch: Biology and biochemistry; Env Mang: Environmental management and sciences; Eco App: Ecological applications; Math Stat: Mathematical statistics; Inf Comp: informatics and computing; Eng Geom: Engineer and geometrics; Microb: Microbiology; Pub Heal: Public health; Med Micro: Medical microbiology; Med Clin Heal: Medical clinical and health. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 1 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities
Fig. 1. General framework of analyses at study- and variable-levels. In the upper section (study-level, in grey), studies are divided in those that used GBIF-mediated data (positives, in orange) and those that did not (negatives, in blue). Positive studies were group according if GBIF was used as the only data source for all variables (2 studies), or if the variables were based on GBIF together with other data sources (105 studies). In the variable-level section (bottom, white background) the total 358 variables extracted from the positive and negative studies were categorized according to the specific use of GBIF, resulting in five types of variables, four of them extracted from the positive studies. Note that in those studies based on GBIF, the different variables could be based on GBIF alone (33 variables), GBIF together with other sources (85), or specific variables may not be based on GBIF-mediated data at all (81 variables). Finally, each variable was related to different epidemiological roles, resulting in a larger number hosts/reservoirs variables, mostly based on GBIF-mediated data, and a higher presence of pathogen species-variables not using GBIF. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 4 in Biodiversity data supports research on human infectious diseases: Global trends, challenges, and opportunities
Fig. 4. Variables according to taxon class (Y- axis) epidemiological level (bar colour) and the use of GBIF- mediated data. Bars represent the number of variables by each taxon class (Y-axis), separated in two panels according to the use of GBIF-mediated data. In the right panel, variables in which GBIF-mediated data was used (Used_GBIF), in the left panel variables in which data was not obtained from GBIF (NonGBIF). Next to the bars, the number of variables by each epidemi- ological level, and percentage in rela- tion to the total number of variables of each group (Used_GBIF: 120 and Non- GBIF: 238). Taxon classes are grouped by taxonomic associations (e.g., birds, primates, ticks, mosquitoes); however, some were merged to simplify the figure. For example, mamm/oth/var includes multiple mammal species which were sparsely mentioned; simi- larly, hosts/res var, vector other and path other grouped several species participating as hosts/reservoirs, vec- tors, and pathogens, respectively. Bar colors represent epidemiological levels (pathogens, vectors, hosts/reservoirs), and Other (in sienna) includes species participating as hosts' regulator, predators, among others. GBIF-mediated data was only used in three pathogen variables (purple), representing only 2.5% of the variables in which GBIF-mediated data was used, resulting in a remarkable difference with other sources (NonGBIF), in which pathogens represented a 59.7%. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Trends in research approaches and gender in plant ecology dissertations over four decades
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