dataesr/scientific-paragraphs-categorization huggingface dataset:
It is now available for eleven languages (Dutch, English, French, German, Italian, Norwegian, Polish, Portuguese, Spanish, Turkish, and Welsh) and three keyboard layouts (AZERTY, QWERTY, and QWERTZ). To allow for comparisons across languages, we created a set of guiding design principles and implemented them as consistently as possible. Subsequently, however, in the analysis section we will only refer to the Dutch copy-task corpus. Below we briefly describe the Inputlog copy task. For a more detailed (technical) description, see Van Waes,  Leijten, Pauwaert, and Van Horenbeeck (2019)  and the materials on the Github repository (https://github. com/lvanwaes/Inputlog-Copy-Task).
Implementation The copy task is a web-based application (platform independent), developed as a separate JavaScript component. Using the design principles described above, the task has been developed for eleven languages and implemented by considering national keyboard layouts. The stored logging file is an XML-file which can be used for further analysis. Within the Inputlog analysis component, a default copy-task analysis provides a large variety of perspectives to interpret the logged data. About 900 variables are being provided representing descriptive statistical data for within-bigram transition time (or inter-key interval) at the component level, trial level and related to the manipulated bigram characteristics (frequency, adjacency, repetition, hand combination needed to type the bigram).
Participants and age distribution For the present analysis, we used the Dutch corpus of copy-task data. The data were collected from 1682 participants (1107 females, 495 males, 80 unknowns) aged between 13 and 83 years old (median = 21 years; SD = 11. 81) in the context of various research projects, research courses and training schools. The age groups are not equally represented because most of these studies took place at secondary schools or universities: 78. 94% of the participants are 23 or younger. The age distribution of the copy-task corpus can be found in Figure  2A  showing a bimodal, positively skewed distribution. This figure shows that the majority of our participants were younger than 23, a large proportion was between 20 and 30, and the right tail showing a smaller number of participants older than 30.
Note, these initial observations are merely descriptive and not central to this paper but open for further investigations. shows the distribution of inter-keystroke intervals and the relationship between age and interkeystroke intervals (IKIs), both log-scaled. For visualisation data were aggregated on participant-level and IKIs were capped at 3,500 msecs. Lexical tasks were indicated by solid lines and non-lexical tasks were displayed with dashed lines. Age information was available for 1,612 participants.
Materials The Dutch copy-task corpus contains 1,447,310 inter-keystroke intervals (per logfile: mean = 700. 54; SD = 78. 56) characterized by a set of variables (see Section 2. 2). Rscripts, Stan code and dataset are available on Github (https://github. com/ jensroes/Copy-task-analysis) and can be used for reproducing and extending the presented analysis for further investigation involving the copy-task data.
Data analysis The aim of this analysis is to provide population parameter-estimates for all copytask components. This is achieved in two steps: First, we assessed the test-retest reliability of the copy-task data. Second, and importantly, we inferred interkeystroke intervals for each copy-task component after controlling for variability specific to the sample (i. e. bigrams and participants) and accounting for process disfluencies. The inferred data were then used to determine differences in interkeystroke intervals between copy-task components. For example, typing differences are specific to frequency (HF vs LF bigrams), lexical information (Tapping vs HF bigrams), syntactic structure (HF bigrams in an adjective-noun combination vs. a simple sentence) or increased cognitive demands (LF bigrams vs.
Consonants task). The inter-keystroke intervals (IKIs), i. e. the latency between two consecutive keystrokes, were analyzed in Bayesian linear mixed-effects models (see, e. g. ,  Gelman & Hill, 2007; Gelman et al. , 2014; Kruschke, 2014; Lambert, 2018; McElreath, 2016)  using the Stan package in R  (Carpenter et al. , 2016; Hoffman & Gelman, 2014; Stan Development Team, 2015a , 2015b). These models were extended to a mixture model  (Farrell & Lewandowsky, 2018; Vasishth, Chopin, Ryder, & Nicenboim, 2017). 1  The rationale for using mixture models for the copy-task data can be illustrated with the following example. Baaijen, Galbraith, and de Glopper (2012) used a mixture model approach to analyze pause length in text production. They found that pause length is determined by a combination (i.
First, our analysis focuses on analyzing data with a minimum amount of aggregation to address known sources of random variance. A realistic estimation of random variation in Bayesian models is computationally difficult. Using a subset is beneficial to reduce the computational power needed. Second, 500 participants is a large sample for the investigation of typing processes. In practice, most researchers will not be able to use a sample as large as the entire copy-task corpus. For the aims of the analysis outlined below, there are no benefits of extending the analysis to the entire corpus. The presented results should be reproducible with any other subset obtained from the corpus. All scripts are available online for further investigations of the entire copy-task corpus.
Test-retest reliability Measure stability was assessed in a test-retest design: 245 participants completed the copy task twice with a time-lag interval of at least one week (195 males, 45 females, 5 unknowns; median age = 22 years, range: 15, 73). We used Bayesian linear mixed-effects models to evaluate whether, first, participants were faster during the completion of the second session, and second, whether we could predict the IKI data from the second session from the IKIs produced in the first session. Models were fitted with the copy-task component as fixed effect and random intercepts for participants and bigrams. We calculated the estimated population mean and the 95% highest posterior density interval (HDPI) expressing the range of values that contains the true parameter value with a probability of 95% (see Appendix B).
Model convergence was tested via the Rubin-Gelman statistic  (Gelman & Rubin, 1992) , traceplots and cross-validation  (Vehtari, Gelman, & Gabry, 2015 , 2017). 3. For an example of how this analysis of the copy task can be used as a diagnostic tool, see Appendix D. 4. Hoaglin and Iglewicz (1987)  defined outliers based on the differences between quartile 3 (Q3) and quartile 1 (Q1). This interval is multiplied by factor 2. 2. The upper threshold for outliers is then calculated by adding this score to Q3. We applied this formula to the slowest component, i. e. the Consonant task. This resulted in the following calculation: ((Q3 -Q1) × 2. 2) + Q3 = ((772. 93 -293. 59) × 2. 2) + 772. 93 = 1,827. 47. code is downloadable via GitHub (https://github. com/lvanwaes/Inputlog-Copy-Task). For a more elaborate, technical description, see Van Waes,  Leijten, Pauwaert, and Van Horenbeeck (2019).
The default copy task described in this paper is made available in eleven languages. However, if researchers want to translate/transpose the default copy task into another language or want to expand or customize the current copy task, they can use the so called 'Copy task creator', also made available as an integrated tool in Inputlog 8. Moreover, this copy task creator is also made available as an isolated, stand-alone tool (see Github). The copy task creator consists of several building blocks that can be combined into a customized task flow. can be understood to represent the intervals that would contain the true parameter value if we were to repeat an experiment a large, if not infinite, number of times under the same conditions. Therefore, 95% CIs cannot be understood as intervals that contain a true parameter value with a probability of 95%, whereas Bayesian posterior intervals do have this interpretation.
Acknowledgements For the technical development of the Inputlog copy task, we would like to thank  Tom Pauwaert ,  Eric Van Horenbeeck  and  Sebastian Fierens. For collecting the Dutch corpus of copy tasks, we would like to thank all the researchers that contributed to this corpus. Especially thanks to  Nina Vandermeulen  ( NWO-LIFT  project),  Lise Paesen  and  Catherine Meulemans  ( FWO/BOF  project) and master students of  Multilingual Professional Communication (University of Antwerp ) for making their copy task data available for this study. We also thank  Thomas Quinlan  for proofreading an earlier version of this manuscript. Within the context of the  European literacy network (ELN) COST Network  ( IS1401 ) and EarlyWritePro (Developing methods for understanding early writing through analysis of process disfluencies) the copy task has been developed in different languages.
g. , 1/ Ph. Eur. 2. 9. 19. [36]. While preparing the manuscript, an extensive literature research was conducted (until 31. 03. 2021) via the most common databases: ISI Web of Knowledge, Google Scholar, and SciFinder. The search terms included, among others, microplastic, analysis, identification, quantification, characterization, vibrational spectroscopy, Raman, infrared, and drinking water. Then the publications related to guidelines for Raman and IR analysis of microplastics were selected and discussed in this review manuscript. In this paper, all relevant methodological steps toward a valid and reliable MP identification are described and discussed. It includes precautions and advice on sampling and sample preparation, avoidance of sample contamination, measurement methods, data processing, and method validation.
Match acceptance criterion between sample spectra and database reference If the laboratory is using a fixed limit for automatic acceptance of spectral matching with the database (e. g. , hit quality index, HQI): The minimum limit can be set at a matching result of, for example, > 70% The lab has to approve initially that the automatic identification for spectral matching above this value is correct, e. g. , through operator/human review of the characteristic peaks in the spectra. Pay attention to the spectra of nylon and proteins, which are very similar in IR and Raman spectra (compare "Data processing" and ESM, section S1). The same kind of spectral similarities could issue with Polyethylene and molecules containing long CH-chains, e. g. , stearates leading to potential false-positive identification.
All measurement parameters must be the same as for samples. Furthermore, the number of co-added background scans must be at least as high as the number of sample scans  [54, 79]. The instrument software usually automatically corrects the sample spectrum through subtraction or division of the background spectrum  [77, 80]. μ-FTIR analysis can be very time-consuming, when measuring hundreds or thousands of particles one by one. Operator input time can be reduced with automated particle detection and FTIR measurement by using appropriate commercial or open-source software (e. g. , GEPARD; see "Raman microspectroscopy"). Measurement time can be accelerated significantly by coupling a focal plane array (FPA) detector to the FTIR microscope. These detectors comprise up to 256 × 256 MCT (mercury cadmium telluride) detector elements.
Consequently, FPA detectors allow the simultaneous and therefore rapid acquisition of thousands of IR spectra in parallel. Furthermore, these spectra are spatially resolved, resulting in so-called IR images of well-defined sample areas. In this way, IR images of entire sample filters can be recorded in relatively short times compared to single-point detectors. Thus, for particle-rich samples, this area-based FTIR imaging can be quicker than a particle-by-particle approach and can be performed automatically, using, e. g. , open-source software siMPle  [79]. Automated FTIR imaging avoids the need for sub-sampling of the filter surface, but produces huge amounts of data that need to be analyzed. Furthermore, flatness of substrates is even more important (see above). A very seldom used, yet noteworthy, technique is μ-ATR or ATR imaging, which combines μ-FTIR spectroscopy, ATR microscope objectives, and FPA detectors.
The entire measured spectral region has to be also covered by the databases, applied for the assignment of MP spectra. Thus, a reliable detection, identification, and quantification of MPs can be achieved. Additionally, not only synthetic polymers, but also additives (e. g. , pigments), can be identified by means of RM. Furthermore, complementary information about the sample can be obtained by combining RM with μ-FTIR analysis (e. g. , identification of pigment(s) and acrylic resin in paint particles  [86] ). Raman imaging is usually performed as point-by-point measurements and is thus time-consuming  [86] , especially when particles smaller than 10 μm are to be analyzed. In contrast to an FPA-FTIR approach, Raman imaging is likely to be less effective than particle-by-particle measurement  [62].
Data processing Spectroscopic methods provide information not only on chemical identity but also on particle characteristics (e. g. , size, shape, color). Thus, data processing has to evaluate large sets of spectral as well as particle-related data. Due to the large number of particles, we suggest using computational, highly automated solutions for time-efficient analysis. Instrument manufacturers are only beginning to offer particle analyzer programs that not only allow for the acquisition of large numbers of spectra automatically but also provide highperformance data-processing tools. Meanwhile, research groups have developed their own data-processing routines based on commercial software  [104]. Furthermore, opensource solutions for both μ-FTIR imaging (e. g. , siMPle  [79] ) and RM (TUM-Parti cleTyper  [ 62] ; see " Raman microspectroscopy") or for RM and μ-FTIR spectroscopy (GEPARD  [49] ; see "Raman microspectroscopy") are used.
In the following, general guidelines for spectra evaluation and reporting for any data-processing approach are described. Regardless of the applied measurement method, large numbers of spectra are generated. They must be evaluated while assuring both correct identification and time-efficient processing. Ideally, the software tools do not only ensure equal treatment of all spectra of a dataset, but they also are fast and stable when processing large datasets and provide convenient formats of data output as well as (easy to use) tools for the operator to evaluate and double-check the identification of the automatically acquired spectra. There are various approaches to the evaluation of spectra, such as correlation to spectral databases  [29, 74, 105] , modelbased classification  [104] , or descriptive methods (e.
g. , multivariate curve resolution-alternating least squares (MCR-ALS) and independent component analysis (ICA)) (El Rakwe et al. , in prep. ). Database searches rely on similarity measures, such as the absolute distance, the Euclidean distance, least squares  [106] , or the Pearson correlation coefficient to find an unknown spectrum's match in a database. For large datasets, the correlation of spectra to databases can become very slow or require high computational power. Reducing the number of spectra in the database can reduce the processing load. Nevertheless, database searches can reach method-intrinsic performance limits, especially for spectra from environmental samples with low signal-to-noise ratios or other deviations from the ideal polymer spectra. Alternatively, training a set of chemometric classifiers, i.
e. , of random decision forest (RDF) classifiers, in combination with applying spectral descriptors for data preprocessing was proposed  [64, 104]. Classifiers are complex algorithms that sort data into categories of information (classes). They learn to predict the class affiliations of unknown data from labeled training data. For the identification of polymer spectra, a training dataset is used which is comprised of a sufficient number of representatives from all the polymer classes that shall be detected. For further details, reference may be made to Hufnagl et al. [104]. Spectral classifiers or descriptive methods can reduce the computational demands, but their implementation is not trivial and becomes more challenging as the number of substance classes increases. To date, the most common approach is the correlation of spectra to databases.
During this process, the search parameters (e. g. , raw or derivative spectra, spectral range, model of comparison  [106] ), data treatment (e. g. , background correction, spectral normalization), and spectral quality can significantly influence the results  [107]. Likely, the best control of whether a polymer spectrum has been identified correctly is an experienced spectroscopist's evaluation. Such a manual QC can, however, be time-consuming due to the large number of spectra. As an alternative, quality criteria for the automated spectral identification can be defined. Database software frequently provides a quantitative criterion for the similarity of a spectrum and a database spectrum, which is often referred to as the hit quality index (HQI). Using a HQI of 70% as a threshold for accepting a result as correct has been suggested  [68, 108].
Consequently, the data pretreatment, the applied correlation algorithm, as well as the database to which the spectra were correlated needs to be reported in studies  [109]. As the HQI depends on many different factors, a general numerical value cannot be defined as a quality criterion. If a laboratory chooses to use only the HQI as quality criterion, determination and validation of the HQI thresholds with respect to the instrumental capabilities and the analyzed sample types is obligatory. To the best of our knowledge, there are no standard operation protocols on how to accomplish validation of HQI thresholds, but a thought experiment based on statistical hypothesis tests could be considered and is described in the ESM, section S3. It should be noted that it has not been tested in routine analysis, so its purpose is to provide the reader with a suggestion on how to approach the task.
We strongly encourage the laboratories to thoroughly test and modify the approach and to share their findings. Current experience is not yet sufficient to provide an assurance to readers that statistical hypothesis tests allow a failure-safe determination of a correct HQI but we encourage verifying one's HQI thresholds with a suitable approach instead of picking an unsubstantiated number. If approaches other than correlating spectra to databases are used, these recognition models have to be validated and reported just as the HQI values described above. As a standard set for spectral identification, we suggest to include the following main polymers: polyethylene (PE), poly (ethylene terephthalate) (PET), PP, PS, PC, poly (vinyl chloride) (PVC), poly (methyl methacrylate) (PMMA), PTFE, polyamide (PA, nylon), and polyurethanes (PU).
The latter can possess a variety of side groups depending on the monomers they are made from, resulting in differing IR and Raman spectra. It is thus recommended that different kinds of PU are included into the set of standard polymers. Since spectra of polymers can differ significantly due to weathering of MPs, it is also advisable to include spectra of aged polymers in the database  [112]. Each laboratory is furthermore recommended to augment this set with further polymers (e. g. , acrylonitrile butadiene styrene (ABS), natural rubber, copolymers) as well as non-synthetic and/or non-polymeric materials that can commonly occur in samples (e. g. , amorphous carbon, cellulose, proteins, inorganic materials such as quartz, and other minerals). As stated in "Raman microspectroscopy," Raman spectroscopy can identify additives such as pigments and dyes.
In the case of these colorants, their typically sharp peaks can be of higher intensity than polymer peaks. Therefore, optimized measurement parameters have to be applied for identification of colorants/pigments and polymers. In order to recognize these potentially polymeric particles, the inclusion of a library of common dyes for polymers can be useful. Spectral libraries are commercially available, but many software tools for database search allow the creation of personalized libraries. This can be advantageous as database and sample spectra are obtained with the same instrument, and spectra of potential contaminants occurring just in a certain lab can be included  [80]. Very recently,  Cowger et al. developed a new open-source library (Open Specy) for MP identification for both measurement techniques, RM and IR.
II. Verification of qualitative polymer identification at minimal size Verification of qualitative polymer identification should be performed using the most commonly applied polymers (PE, PET, PC, PP, PVC, PS, etc. ) and analyzing at the claimed minimal detectable size. For validation, MPs from real samples (with previously identified polymer type), commercially available polymer standards  [61] , or in-house-produced reference polymer particles  [64, 69]  may be used. Very recently, the JRC published their method on how they produced reference material for the above-mentioned ILC  [120]. The lab is required to achieve goodquality spectra. If an automated database algorithm for polymer identification is used, acceptable quality indices for the library match should be documented (see HQI, "Data processing").
As additional information or upon request, the report might contain: & Details about blank samples (number, type of polymer, size range) & Shape (e. g. , fragment, fiber, sphere) and color of particles & (Microscopic) images of measured filters & Number of (potential) MPs not included in the minimum set of polymer classes proposed in "Data processing" (e. g. , colored polymers, pigment or dye particles) sorted by type or by size range, shape, color, etc. & Total number of non-plastic particles identified (e. g. , minerals, proteins, cellulose) sorted by type or size range, shape, color, etc. & Total number of non-identified particles & Software and routines used for -Determining particle properties -Spectra processing -Spectral identification; in the case of database search information about the database (commercially, homemade); if a HQI was used, the algorithm used and how the threshold was determined Laboratories must only express quantitative results, if they exceed the LOD at the size limit of the method.
Acknowledgements and funding B. E. Oßmann thanks the  Bavarian State Ministry of the Environment and Consumer Protection  for funding the projects " Detection of microplastics in selected foods" and "Expansion of the analytics of microplastics in food. "S. Lacorte acknowledges funding from the  Ministry of Science and Innovation of Spain  (project  PID2019-105732GB-C21 ). J. Weisser ,  K. Glas ,  T. Hofmann ,  N. P. Ivleva , and E. von der Esch receive funding from the  Bavarian Research Foundation (Bayerische Forschungsstiftung (BFS) ,  AZ-1258-16  ("MiPAq"). N. P. Ivleva  thanks the  Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung (BMBF) , projects  02WPL1443A  " SubμTrack " and  03F0851B "HOTMIC" ) for financial support. D. Fischer  and  F. Fischer  acknowledge funding from the projects MicroCatch_Balt, PLASTRAT, PLAWES by the  BMBF in the call "Plastics  in the environment," as well as from the  BONUS BLUE BALTIC  project  MICROPOLL , which has received funding from  BONUS  ( Art 185 ), funded jointly by the  EU  and the  BMBF.