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                "en": "This dataset includes data files and supplementary files related to the review study \"The concept of competence in educational research: a knowledge map of main research traditions and topics\". It consists of the following documents.\r\n\r\nSupplementary file 1: Search process. This text document outlines a) our general search strategy, presented in table format, b) the search query we used to retrieve 62 systematic competence reviews from Scopus, c) the search terms employed by those systematic reviews, presented in table format, and d) the search terms we included in our own query, presented in table format. \r\n\r\nSupplementary file 2: Retrieved documents. This data file lists the 135,298 documents retrieved through the search, along with their corresponding bibliometric information.\r\n\r\nSupplementary Files 3a-b: VOSviewer map and network files (csv), generated using VOSviewer software, contain results from the direct citation network analysis. Using these files with VOSviewer, our direct citation network graph can be reconstructed in an interactive form.\r\n\r\nSupplementary File 3c: DCNA cleaned map file (xlsx) provides a list of documents by cluster (research tradition), along with their bibliometric details and related statistics calculated by VOSviewer. With this file, the data can be easily read, filtered and sorted. When we refer to the extensive library of further reading in the article, we are referring to this file.\r\n\r\nSupplementary file 4: Yearly number of documents in the main traditions of competence research (jpg/png). \r\nThis supplementary figure illustrates the historical development in the number of documents within each research tradition. It was used to support the writing of the Results section.\r\n\r\nSupplementary file 5: Data preprocessing algorithm (term co-occurrence analysis). \r\nConducting the term co-occurrence analysis separately for each research tradition presented a key challenge: VOSviewer only accepts files that strictly follow the format of standard database export files. Therefore, we needed to create a separate file for each research tradition, containing all documents assigned to that tradition and formatted to match the structure of a Scopus export file. \r\n\r\nWe addressed this by developing a custom Python code that first extracted cluster assignments from the direct citation network analysis (Supplementary Files 3a-c). It then processed the full dataset (Supplementary File 2), assigning each row to cluster 1, 2, 3, 4, or none, based on matches in DOI, author name, and document title.\r\n\r\nSupplementary files 6a-l: VOSviewer results files (term co-occurrence analysis). These files are similar to Supplementary Files 3a–c, but pertain to the term co-occurrence analysis instead. For each research tradition, there are three corresponding files: map files, network files, and cleaned map files. \r\n\r\nThe map files (csv) and network files (csv), generated using VOSviewer software, contain results from the term co-occurrence analysis. Using these files with VOSviewer, our term co-occurrence graphs can be recreated in an interactive form. \r\n\r\nThe cleaned map files (xlsx) contain the 500 most frequently occurring terms for each research tradition, along with related statistics calculated by VOSviewer. With these files, the data can be easily read, filtered and sorted."
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                "en": "This dataset originates from a two-day MEG study investigating the neural mechanisms of artificial orthography learning. The project aimed to examine how adult participants form and generalize novel letter–speech sound (LSS) associations and how these newly learned associations interact with higher-level linguistic and semantic processing. The dataset includes MEG recordings and behavioral data from 32 adult participants who learned associations between unfamiliar Georgian letters and familiar Finnish phonemes through structured audiovisual training. Participants completed multiple tasks across two consecutive days: audiovisual learning of letter–sound pairs (AV_Learn), a generalization test with novel syllables (Syllable_Test), a LSS test task involving newly learned and native LSS pairs (AV_Test), and a semantic learning task linking syllables to pictorial meanings (Semantic_Learn). Resting-state MEG data and standardized cognitive assessments were also collected on both days. The dataset consists of raw and preprocessed MEG recordings, cognitive test scores, behavioral performance of the MEG tasks, stimulus files and experimental presentation scripts.\r\nParticipants: A total of 36 participants were recruited through email lists and posters. Data from four participants were not collected due to cancellations. The remaining 32 participants (22 females; 2 left-handed; 2 ambidextrous; mean age = 24.22 years, SD = 3.43 years; age range = 19–36 years) were included in the dataset.\r\n\r\nMain Experiments: 1. Audiovisual Learning Experiment (AV_Learn)\r\nParticipants learned to associate novel visual symbols (Georgian letters) with Finnish phonemes through a structured training paradigm over two consecutive days. On Day 1, participants completed 12 training and testing blocks, and on Day 2, they completed 6 training and testing blocks.\r\nThe auditory and visual stimuli consisted of 12 Georgian letters and 12 Finnish phonemes, divided into two sets. For participants with odd ID numbers, Set 1 served as the Learnable (LB) set and Set 2 as the Control (CT) set. For participants with even ID numbers, the assignment was reversed.\r\nSet 1 included the following pairs: ჸ, ჵ, ჹ, უ, დ, ჱ → a, ä, e, t, s, k.\r\nSet 2 included the following pairs: ც, ჴ, ნ, ფ, ღ, წ → o, ö, i, p, v, d.\r\n2. Syllable Matching Experiment (Syllable_Test)\r\nThis experiment tested whether participants could generalize the newly learned letter–speech sound (LSS) mappings to decode and match novel two-letter consonant–vowel (CV) syllables. (Analysis ongoing.)\r\n3. Letter–Sound Matching Experiment (AV_Test)\r\nThis experiment compared the neural processing of newly learned LSS pairs with overlearned native Finnish LSS pairs.\r\n4. Semantic Learning Experiment (Semantic_Learn)\r\nIn this task, participants learned to associate novel two-letter Georgian syllables with pictorial meanings. (Analysis ongoing.)\r\nProcedure: On Day 1, participants first completed cognitive tests (Part 1; approximately 30 minutes), followed by a 5-minute resting-state recording (eyes open 2.5 minutes + eyes closed 2.5 minutes). They then performed the AV_Learn task (12 blocks) and the Syllable_Test task (8 blocks).\r\nOn Day 2, participants first completed cognitive tests (Part 2; approximately 30 minutes) and a 5-minute resting-state recording (eyes open 2.5 minutes + eyes closed 2.5 minutes). They then performed the AV_Learn task (6 blocks), the AV Test task (6 blocks), and the Semantic_Learn task (12 blocks)."
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                            "en": "Xu, W., Kolozsvari, O. B., Oostenveld, R., & Hämäläinen, J. A. (2020). Rapid changes in brain activity during learning of grapheme-phoneme associations in adults. <i>NeuroImage</i>, <i>220</i>, Article 117058. <a href=\"https://doi.org/10.1016/j.neuroimage.2020.117058\" target=\"_blank\">https://doi.org/10.1016/j.neuroimage.2020.117058</a>",
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                "fi": "Tutkimuksessa selvitämme mekanismeja, joiden kautta hyvin pienenä keskosena syntyminen on yhteydessä aikuisiän terveyteen ja hyvinvointiin. Tutkimukseen osallistuu sisaruspareja, joista toinen on syntynyt alle 1500 g painavana keskosena ja toinen täysiaikaisena. Tutkimuksessa on mukana 79 samaa sukupuolta olevaa sisarusparia, joista toinen on syntynyt pikkukeskosena (syntymäpaino < 1500 g) ja toinen täysiaikaisena. Pikkukeskosena syntyneet tutkittavat ovat syntyneet vuosina 1978–1990 pääkaupunkiseudulla, Turun ja Tampereen seuduilla ja Pohjois-Suomessa.\nVuosien 2014–2017 aikana tutkittavat kävivät kattavissa kliinisissä tutkimuksissa usean päivän aikana (ml. magneettikuvaus, neuropsykologinen tutkimus, kudos- ja verinäytteitä sekä liikunta- ja lihasvoimamittauksia), ja heiltä kerättiin raskautta, syntymää ja lapsuuden kasvua ja kehitystä koskevat tiedot sairauskertomuksista.\n\nEpigenomin laajuinen DNA-metylaatio analysoitiin verestä, rasvakudoksesta ja lihaskudoksesta käyttäen menetelmää Infinium Human Methylation EPIC BeadChip (850K) 1.0. Kehon koko ja kehonkoostumus mitattiin. Lisäksi tutkittavat läpikävivät 2 tunnin glukoosirasituskokeen (75g, OGTT). Terveyskäyttäytymiseen liittyviä tietoja, kuten tupakointi, alkoholin käyttö ja fyysinen aktiivisuus, kerättiin kyselylomakkeilla. Fyysinen kunto mitattiin polkupyöräergometritestillä."
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