dataesr/scientific-paragraphs-categorization huggingface dataset:
8-83. 6; P<. 001). Sensitivity was superior for men at 2 centers (Nantes: 87%, 95% CI 85. 1-89 vs 83. 6%, 95% CI 81. 4-85. 8; P=. 006; Rennes: 98. 6%, 95% CI 98. 1-99. 2 vs 96%, 95% CI 94. 9-97. 1; P<. 001) and for patients born in France at all centers (Nantes: 85. 8%, 95% CI 84. 3-87. 3 vs 74. 9%, 95% CI 72. 8-77. 0; P<. 001). The DLD-based algorithm revealed significant differences in sensitivity among centers (Nantes, 85. 3% vs Lille and Rennes, 97. 3%, P<. 001). Specificity was >98% in all subgroups. Our algorithm matched tens of millions of death records from BDWs, with parallel computing capabilities and low RAM requirements. We used the Inseehop open-source R script for this measurement. Conclusions: Overall, sensitivity/recall was 11% higher using the DLD-based algorithm than that using the direct algorithm.
Introduction Vital status is important information for medical research. While real-world evidence from the analysis of biomedical data warehouse (BDW) records has gained popularity in recent years  [1] , the longitudinal value of the information is weakened by the uncertainty of patients' vital statuses. This information is often limited to inpatients who died while hospitalized. France has a long tradition of administrative centralization inherited from the Napoleonian era. When a resident dies on French territory, French city halls are required to send a death report to the Institut National de la Statistique et des Études Économiques (INSEE), translated to the French National Institute of Statistics and Economic Studies  [2]. This report is used to complete the French National Mortality Database (FNMD).
This database, which contains tens of millions of records, is updated monthly and has been open access since 2019  [3]. Record linkage (also referred to as data matching or entity resolution) is the process of quickly and accurately identifying records corresponding to the same individual entity from one or more data sources  [4]. A recent literature review by Bounebache et al  [5]  presents record linkage and its multiple challenges. Two different approaches exist: (1) deterministic record linkage, which uses expert knowledge and possible statistical learning  [5] ; and (2) probabilistic linkage, which relies on a statistical model to evaluate the contribution of each variable in the record linkage strategy  [6, 7]. Matching large-scale BDW records with the FNMD presents multiple challenges.
The first is the absence of a unique common identifier, such as a social security number. Second, surnames are shared within families and may change over one's lifetime based on varying cultural practices regarding marriage. Additionally, first and middle names can be confounded or compound. Third, clerical errors can occur when identities are administratively recorded in both databases  [4]. Furthermore, in practice, the exponential number of comparisons often prohibits direct matching of millions of database records to the FNMD, which contains tens of millions of records. Consequently, little has been published on the computational performance of record linkage, its accuracy, and its determinants. Moore et al  [8]  state that record-linkage performance must be evaluated to validate statistical analyses.
For example, they showed that a specificity of <95% prevents estimating a significant risk ratio of 2. Previous studies  [9, 10]  have used record linkage with the FNMD. Most of these studies used suboptimal references to evaluate algorithm performance, small databases (ie, <20,000 patients), were monocentric, or did not share their source code. Bannay et al  [11]  linked a BDW with Système National des Données de Santé (SNDS), translated to the French National Health Database. As the extraction from the SNDS was anonymized in accordance with national legislation, they implemented a semideterministic record linkage procedure based on the variables of the hospital discharge report (ie, sex, year of birth, month of birth, admission and discharge dates, diagnoses, etc). To routinely update vital status in BDW records from the FNMD on a large scale, we developed a deterministic matching algorithm based on Damerau-Levenshtein distance (DLD) and compared its performance with that of a direct-matching algorithm as a reference for 3 regional hospital BDWs.
Data and Databases FNDM files were downloaded from the national open data website  [12]  and included the following fields: birth surname, first name, middle names, birth date, sex, city and country of birth, death date, and zip code of the place of death. We found 11,490,867 records for the period between 2001 and 2020. Three university hospitals in France were involved in this study: Lille, Nantes, and Rennes. Each hospital's BDW contains administrative, clinical, biological, and drug data. In the Lille BDW, vital status information was available on June 1, 2021, for 1,609,515 patients who had at least 1 hospital stay between January 1, 2008, and June 1, 2021. The data showed that 1,570,320 (98%) patients were living, and 39,195 (2%) were deceased. For the Nantes BDW, vital status information was available on January 14, 2021, for 2,035,805 patients who had at least 1 hospital encounter during the previous 20 years.
The data showed that 1,974,786 (97%) patients were living and 61,019 (3%) were deceased. For the Rennes BDW, vital status information was available on January 4, 2021, for 1,262,072 patients. The data showed that 1,221,817 (97%) patients were living, 37,986 (3%) were deceased, and 346 had no recorded vital status. Hereafter, samples extracted from the BDWs are referred to as "local databases. ".
DLD-Based Algorithm as a Deterministic Solution The DLD between 2 strings is the sum of necessary operations to transform string 1 into string 2, among insertion of a character, deletion of 1 character, substitution of 1 character by another, or transposition of 2 adjacent characters  [13]. Examples are available in Multimedia Appendix 2. Distances were calculated between the local database and FNMD for first name, surname, birth date, sex, and city of birth. For sex, a distance of 0 indicates that the sex is the same in both records, and a distance of 1 indicates a mismatch between the 2 records.
Data Cleaning In the FNMD, birth dates may be expressed with a missing day and month (eg, 1956-00-00). In these cases, the algorithm automatically attributed January 1 to the date to obtain a valid date format. If the birth date was invalid, the month and day were inverted and tested before choosing January 1. For example, the date 1960-31-03 is invalid (ie, there are not 31 months in a year); however, the date 1960-03-31 is valid, so 1960-03-31 was chosen. Another example is the date 1959-32-33: the dates 1959-32-33 and 1959-33-32 are also invalid; thus, the date 1959-01-01 was chosen. Characters other than letters (eg, numbers and special characters) were removed from the local database and FNMD. All letters were changed to lowercase, and accents were removed. In both the local database and FNMD, mentions of the district were suppressed, and only the city of birth was used.
New Variable Creation In the local database, a transformed city of birth variable was created wherein abbreviations were transformed into full text. For example, "St-Martin-sr-Ocre" was transformed into "saintmartinsurocre. " In the FNMD, the variable fnmd_firstname_0 was created from the first element of the first name (eg, "pierre" from "Pierre-Olivier"), and the variable fnmd_firstname_12 was created from concatenation of the first name and middle name (eg, "marieclaire" from first name "Marie" and middle name "Claire"). Other examples are available in Table  1.
Pair Validation Records from the local database and the FNMD matched if all the following conditions were valid: (1) the DLD of the first name was ≤ the maximal first name DLD, (2) the DLD of the surname was ≤ the maximal surname DLD, (3) the DLD of the birth date was ≤ the maximal birth date DLD, (4) the DLD of the sex was ≤ the maximal sex DLD, and (4) the total sum of the 4 previous DLDs (ie, the total DLD) was ≤ the maximal of the total DLD. The DLD chosen for the surname was the shorter DLD among (1) the birth surname in the local database and the surname in the FNMD and (2) the current surname in the local database and the surname in the FNMD. The DLD chosen for the first name was the shorter DLD among (1) the first name in the local database and fnmd_firstname_0, (2) the first name in the local database and fnmd_firstname _1, and (3) the first name in the local database and fnmd_firstname_12.
The DLD chosen for the birth city name (option for the more pertinent pairs selection) was the shorter DLD among (1) the original city of birth in the local database and city of birth in the FNMD and (2) the transformed city of birth in local database and city of birth in the FNMD. Matching 2 databases (A and B) without a common identifier implies evaluating the match or nonmatch status of every element of AxB, called a pair  [5]. The number of pairs to compare is given by the number of records in A multiplied by the number of records in B. This formula is particularly concerning when matching BDWs with the FNMDs, both of which potentially contain tens of millions of records, potentially leading to quadrillions of pairs to compare. Blocking techniques reduce the number of pairs to compare  [5]  and thus the execution time and RAM requirements.
Data Sampling for Statistical Learning, Performance Evaluation, and Large-scale Testing For all individuals, the following variables were extracted: birth surname, current surname, first name, birth date, sex, city and country of birth, vital status, and, if present, death date. To learn the optimal parameters of the DLD-based algorithm, we randomly selected 3600 patients from the Nantes BDW, with 450 per stratum:  (1)   For the DLD-based algorithm, a maximal DLD of 2 was learned for the first name, 1 for the surname, 1 for the birth date, 1 for sex, and 2 for the total DLD. To evaluate the specificity and sensitivity of both the DLD-based and direct algorithms, samples of 8000 patients were randomly extracted from each of the 3 BDWs. The sample from Nantes did not contain patients used for statistical learning of the DLD-based algorithm parameters (ie, the maximal DLDs).
This was not a problem because to evaluate specificity, we only used deaths registered in the FNMD between January 1, 2001, and January 1, 2016. To evaluate sensitivity, we used all deaths registered in the FNMD between January 1, 2001, and December 31, 2020. Specificity and sensitivity were calculated for each maximal total distance parameter of the DLD-based algorithm, from 0 to 5. Our gold standard for the matching algorithms was for it to be reliable both for sensitivity and specificity evaluation. First, it was completely independent from the FNMD. Second, deceased status in the hospital databases was reliable because vital status at discharge is a necessary information for the stay fee payment to the hospital by public health insurance in France. Finally, alive status at a certain time was also reliable because it was searched for between 2 distinct encounters.
Implementation and Execution Time Evaluation We developed an R package to run on parallel cores and automatically select by default the most efficient number of cores to use depending on the number of records to match, the number of available cores, and the available RAM. The number of cores used still fit in the parameters. We used the packages "future" and "future. apply" to enable Linux and Windows compatibility. We measured the execution time to successively match 200, 2000, 20,000, 200,000, and 2 million patients from the Nantes BDW with 11 million records from the FNDM. We tested various core numbers on 3 cores and 15 GB of RAM (1 core and 1 GB of RAM on the laptop used were left free for the operating system).
Performances That Increased Sensitivity/Recall and Reduced Differential Biases Overall, sensitivity/recall was approximately 11% higher with the DLD-based algorithm than with the direct algorithm. This highlights the importance of advanced data cleaning and knowledge of a naming system through DLD use. Moreover, sensitivity was approximately 12% higher for the Lille and Rennes evaluation samples than for the Nantes sample, possibly owing to differences in the BDW data quality or less efficient death report management by regional city halls. Hence, when possible, each center interested in reusing our algorithm should compute its own FNMD-matching performance evaluation. Sensitivity was approximately 3% lower for women than for men in the Nantes and Rennes samples. This may have been because women are more likely to change their surname after marriage, whereas most men do not; thus, women's birth surnames are not always registered.
Specificity was ≥98% for both sexes, birth country, and hospital, which greatly reduced the risk of differential biases between groups. Blocking the birth date and then concatenating the first 4 characters of the first name and the first 4 characters of the surname reduced the time needed to match 2 million patients from ~366 years to 78 hours. However, records from the local database cannot be compared with those from the FNMD if both these blocking criteria differ; they can only be compared if only 1 differs or both are equal.
Large-scale Matching on a Daily Routine Basis With Minimal Local Computing Capabilities The program implemented in R software to work on parallel cores was able to run with 2 million patients from the Nantes BDW and 11 million deceased people from the FNMD on 15 GB of RAM and 3 cores in a reasonable duration. Execution time could be improved with higher performance platforms; our laptop was not ideal due to its low computing capabilities and overheating problems during the 3 cores calculations. Because data stayed on hospital computers and no external service was involved, confidentiality was optimal. Moreover, only popular R packages were necessary to run it, which is useful for users who lack administrator rights on their machines.
Quality of the Gold Standard As described earlier in this paper, our gold standard was reliable both for sensitivity and specificity evaluation because (1) it was completely independent from the FNMD, (2) deceased status in the hospital databases was reliable, and (3) alive status at a certain time was searched between 2 distinct encounters. For some patients in our sample, names, surnames, birth date, or sex may have been incorrect, as in every database. Nevertheless, this was not a problem because our algorithm could manage these kinds of errors.
Acknowledgments This work was supported in part by the cluster  DELPHI -NExT  financed by  French National Research Agency (Agence Nationale de la Recherche, ANR) , integrated to France 2030 plan, by  Région Pays de la Loire  and by  Nantes Métropoles. We thank  Drs David Lair ,  Romain Casey , and  Florance Molinié  for their suggestions.
Data collection A georeferenced database of ornament assemblages from the Iberian Peninsula ascribed to either to the Early or Late Mesolithic was elaborated using specialised publications and grey literature. This database is part of a much more extensive relational database drawn up for the ERC project Paleodem. Table  1  summarises the information recorded in the database (see Supplementary Tables  2  and 3  for details). The original database contains a total of 106 assemblages from 62 archaeological sites. Considering ornament assemblages by chrono-cultural period, we have a higher number of Late Mesolithic sites compared to Early Mesolithic sites. Initially, each of the contexts was assigned to one of the two phases of the Mesolithic based on the chrono-cultural attribution of their material assemblage as it was specified by the researchers originally studying the materials.
-Level 2: Designates contexts with a clear cultural attribution but not directly associated with absolute radiocarbon dates. These contexts can be framed using the radiocarbon dates from stratigraphically close contexts. -Level 3: This code was assigned to ornament assemblages from contexts with a reliable cultural attribution that cannot be associated to any radiocarbon date nor framed with any stratigraphically close contexts. Also, those contexts with inconsistent radiocarbon dates were coded with this value. -Level 4: Those contexts with unreliable cultural attribution and no radiocarbon dates available were labelled with this code. The context assigned to this level was not included in any analytical step. Once the archaeological contexts of ornament assemblages' provenance were ranked, we unified contexts of the same site that were ascribed to the same Mesolithic phase, either the Early or Late Mesolithic, i.
2017). Given the extensive presence of Columbella rustica in the Iberian Mesolithic record (Álvarez Fernández 2008a), the Brainerd-Robinson coefficient was not appropriate for our dataset. Jaccard indices were calculated using Past statistical software  (Hammer et al. 2001). Similarity matrices were transformed into networks to prepare them for social network analysis (SNA). In this approach, social actors and their interactions are mathematically quantified and graphically represented as nodes and links. SNA offers a rigorous conceptual framework to describe the structure of networks together with methods to analyse and interpret them  (Mills 2017). Specifically, we constructed a similarity network for each period of interest on the assumption that ornament assemblage similarities reflect shared cultural practises and, therefore, some level of social interaction.
Consequently, we decided not to impose any threshold to network links between nodes. We used igraph R package  (Csardi 2006)  to construct and analyse the networks. As part of the process of network construction, we needed to assess the impact of archaeological data reliability on the network structures. To this end, we constructed and compared several networks, capturing different levels of reliability. First, we constructed one network for each period (the Early Mesolithic and Late Mesolithic) using archaeological contexts that reached the chrono-cultural reliability levels 1 and 2. A second pair of networks was then elaborated by adding the assemblages from level 3 contexts. Assemblages classified as level 4 were not used in any analytical step due to their significant unreliability. Next, we calculated the overall structure of our four networks using density, average degree, weighted average degree, clustering coefficient and average path length metrics (see S.
Data variability and frequency distribution We observed geographical and chronological patterns of the taxa used for the elaboration of ornaments. Assemblages were ascribed to one of the five geographical units (i. e. Cantabria, Atlantic, Mediterranean, Interior and Ebro Valley) which are traditionally used by Iberian prehistorians as units of analysis (e. g. Utrilla and Montes 2009; Álvarez-Fernández 2006). In Southern Portuguese sites, the most common taxon was Theodoxus fluviatilis, a freshwater snail widely distributed from Europe to Central Asia. In the Cantabrian sites, the most numerous taxa were Trivia sp. , followed by Littorina obtussata of Atlantic origin. The Mediterranean record is dominated by Columbella rustica, an exclusively Mediterranean species. Finally, in the Ebro Valley, we found a mixture of the ornaments documented in the Cantabrian and Mediterranean geographical units.
These analyses have required the introduction of two methodological contributions that are novel to the application of SNA in prehistoric archaeology. First, in order to incorporate as much reliable archaeological data as possible, we developed a procedure that classifies the information according to its chrono-cultural reliability (see 'Data Collection' subsection within the 'Data and Methods' section). It is common practice amongst scholars using archaeological data to construct networks, to accept its chrono-cultural reliability as stated by the original researchers. However, when the corpus of data is the results of a long research tradition and has been produced by different teams (as it is our case in this article), one cannot assume a homogeneous level of accuracy across the dataset.
Funding Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work was funded by the European Research Council (ref. ERC-CoG 2015) under the European Union's Horizon 2020 research and innovation programme (grant agreement 683018). J. F. -L. d. P was also supported by grant no. 2018/040 from the CIDEGENT Excellence programme of Generalitat Valenciana, and S. L. by Generalitat de Catalunya through the SGR programme (2017 SGR 1466). Data availability All data needed to reproduce this work are provided in the Supplementary Materials.
We speculated that DT mainly involves key cerebral regions of the DMN (i. e. , medial prefrontal areas, the parietal lobe, and the cuneus), which are always activated in conditions of low cognitive load and automatic processing. Meanwhile, areas like prefrontal cortices and the amygdala-hippocampal networks closely related to cognitive control and emotion processing might be responsible for activity during insight. Therefore, by interpreting these two creativity subsets, this study aimed to offer new neural evidence for the dual-processes theory. org/;  Page et al. , 2021). This search was carried out using databases such as the Web of Science (WOS), PubMed, PNAS, PLOS, Elsevier, APA, Scopus, and SAGE. Keywords such as "divergent thinking," "creativity," "creative thinking," "semantic processing," "idea generation," and "original ideas" were combined with technical terms such as "functional magnetic resonance imaging" and its abbreviation to yield 1399 papers.
e. , noise). Using these strategies, we could find the real convergence of activation foci, and random clustering can be differentiated  (Eickhoff et al. , 2012; Turkeltaub et al. , 2002). All confirmed coordinates were analyzed using GingerALE 3. 0. 2 software (http://brainmap. org/). Initially, all coordinates were converted into MNI space using the tal2icbm_spm transformation  (Laird et al. , 2010; Lancaster et al. , 2007). The current study selected a conservative mask of the MNI152 space. In total, 631 activation foci were analyzed, with the DT and insight dataset comprising 212 foci (10 foci outside the mask); and 419 foci (19 foci outside the mask), respectively. The Mango and the Colin27 template (1 mm Â 1 mm Â 1 mm; http://ric. uthscsa. edu/mango) as well as mricroGL (https://www.
nitrc. T A B L E 1 (Continued) org/projects/mricrogl), were utilized to inspect and display the results. First, we performed two single meta-analyses on the DT and the insight datasets alone. In each dataset, the qualified contrasts from an independent article were gathered into one group as long as they were derived from the same sample  (Müller et al. , 2018). Reported foci originating from an identical sample were integrated once only to avoid repeated effects  (Eickhoff et al. , 2016; Turkeltaub et al. , 2012). The initial single meta-analyses contained DT > control and insight > noninsight. Data processing was corrected by multiple comparisons using a Family Wise Error Rate of p <. 05 at cluster-level and permutated 3000 times with a cluster forming threshold of p <. 001 (Müller et al.