dataesr/scientific-paragraphs-categorization huggingface dataset:
Data The English National Cancer Registry (CR) was individually linked to Clinical Practice Research Datalink (CPRD). These data were released under national statutory approvals from The Confidentiality Advisory Group (CAG): PIAG 1-05(c)2007, PIAG 3-06(f) 2008 and national ethical approvals from the Research Ethics Committee (REC): 13-LO-0610, 08-H1102-46. CPRD is a complex, comprehensive database of anonymised electronic health records for individual patients registered at general practices (GP) across the UK. It contains patient demographic information, clinical diagnoses, prescriptions, immunisations, test results and certain information on health and health-seeking behaviours. It is one of the largest sources of continuous patient records available and is broadly representative of patient and practice characteristics in the UK.
Case definition and control selection Cases were defined as patients aged 15-99 years diagnosed with primary pancreatic cancer (ICD-10 code C25) between January 1, 2005 and June 30, 2009 whilst registered with a CPRD practice who had been submitting UTS data for at least two years before their diagnosis (S1 Fig). Controls were patients diagnosed with an unrelated primary cancer at least 18 months after the index date (date of diagnosis of matched PC case) who were similarly registered within a CPRD practice submitting UTS data during the identical chronological period. We excluded patients diagnosed with cancers of the lip, oral cavity and pharynx (ICD-10 codes C00-14), digestive organs (C15-26), respiratory and intrathoracic organs (C30-39), breast (C50) and, female genital organs (C51-58) which share at least one symptom or risk factor with pancreatic cancer.
Variable coding We derived symptoms and health statuses of patients known to be associated, or possibly associated with a subsequent diagnosis of pancreatic cancer during the two-year period prior to diagnosis (S1 Table ). First, an exhaustive list of medical or product (drug) codes associated with each symptom was derived from the Code Browser using broad search terms. Code lists for frequently studied symptoms and drug use were also obtained via correspondence with authors of published papers as well as from the LSHTM Data Compass [20], a curated digital repository of research outputs produced by staff and students at the London School of Hygiene & Tropical Medicine (LSHTM) and their collaborators. We also searched for clinical codes on CALIBER  [21]  which is a comprehensive, open access resource for UK electronic health records data.
Medical and drug prescription codes along with their event dates up to two years prior to index date of pancreatic cancer were extracted using Clinical and Therapy files provided by CPRD. We also searched for useful information in the Additional, Referral and Test files. Codes were assigned to an appropriate symptom to build an analysable data structure. These symptoms aimed to be as comprehensive as possible; derived from both extensive literature searches as well as discussions with our clinical collaborator (SPP). After establishing a clean and exhaustive code list for each symptom and drugs prescribed, the codes were checked for non-repetition across all the different variables. Fifty-three symptoms were coded in this fashion. We derived patients' status in relation to smoking, body mass index (BMI)  [22] , diabetes and, alcohol consumption using previously-published code lists.
We used ecologically-derived measures of income deprivation for each person using quintiles of the income domain score from the English Indices of Multiple Deprivation 2004, 2007 and, 2010  [23]. Deprivation categories were derived from the score temporally closest to each person's index date on the basis of their residential address. The consultation file was used to calculate the total number of appointments a patient had with a doctor at the GP surgery within the time period under study. All medical records referring to administration and routine examination codes were excluded since they did not contain any clinical information. Symptoms relating to the eyes, ears, skin and, skeleton were also excluded. Finally, we visually examined all the remaining non-assigned codes occurring more than ten times in the database.
Strengths and limitations This study has a number of strengths. First, we used prospectively collected data and a highquality database. Under-recording or inaccurate reporting may be present but is unlikely to be differential. We evaluated temporal patterns by establishing the predictive accuracy of our model at separate time points throughout the two-year period leading up to diagnosis. We used the machine learning setting to carry out statistical analyses, rather than multivariable regression methods or conditional logistic regression as has previously been implemented  [26, 27]. Machine learning techniques are more powerful in settings such as this one where they are more likely to identify numerous weak signals which are only predictive when used in complex combination with each other.
This implies that the controls are not representative of the general population, and explains why our models predict a high number of false positives. However, in the light of this limitation, our results are still promising: the models achieve a relatively high sensitivity and the specificities of the final models are in excess of 55%. Repeating the analyses using full population-based controls would lead to a much higher specificity and is the first priority in progressing this research. Our results suggest that further age differentiation is required to truly evaluate the utility of this approach in patients under 60 years, which would be possible in a larger dataset. In particular, it would be important to evaluate performance separately for patients aged 40-49 and 50-59 amongst whom the potential effectiveness of our models is currently obscured by the very low incidence amongst 15-59-year olds.
Implications for future research This study is preliminary, and thus needs to be expanded and extended in order for these findings to be confirmed. First, it is essential that future analyses use population-based controls so that true specificity of the algorithm within a population-based database can be derived. Second, analyses should differentiate between diabetics and non-diabetics, as well as in a more refined manner by age. A notable result was the relative importance of diabetes, over timevarying symptoms, in predicting later pancreatic cancer diagnosis, which is consistent with previous research  [31]. Since studies have established a particularly increased risk amongst new onset diabetics  [1] , stratified analyses by diabetic history hold promise to improve the predictive ability of the model for all patients.
Acknowledgments "This work uses data provided by patients and collected by the  NHS  as part of their care and support. Using patient data is vital to improve health and care for everyone. There is huge potential to make better use of information from people's patient records, to understand more about disease, develop new treatments, monitor safety, and plan NHS services. Patient data should be kept safe and secure, to protect everyone's privacy, and it's important that there are safeguards to make sure that it is stored and used responsibly. Everyone should be able to find out about how patient data is used. "The above has been developed by use MY data (http:// www. usemydata. org), a movement by patients, relatives and carers harnessing the patients' voice to build confidence in the use of patient data to improve outcomes and save lives.
Motion Capture Corpus The data used in the present investigation were taken from a previously reported study  (Bigand et al. , 2020). In brief, each of six deaf native and fluent signers had freely described the content of 25 pictures (as shown in examples in Supplementary Material 1) using LSF. The dominant hand of all signers was the right hand. Using a mocap system equipped with 10 cameras (Optitrack S250e), the data consisted of the upper-body movements recorded at 250 fps in three dimensions. Further details (e. g. , picture content, type of SL discourse, and mocap equipment) are available from the original mocap corpus  (Benchiheub et al. , 2016a,b). From the 25 mocap recordings, only 24 were taken into account in the present study, as one of them was not available for one signer. Moreover, from the 27 original body markers, we derived 19 virtual markers that optimally describe the major joints of the body.
-Size normalization (SI): Reference "T" postures were recorded for each signer (Figure  1 ). An overall reference "T" posture was computed by averaging across the six signers. The slope of the regression between each reference posture and the overall reference posture was then computed. These slopes defined relative sizes  (Troje et al. , 2005)  for each signer: 1. 000, 1. 075, 0. 924, 1. 003, 0. 996, and 1. 003. After dividing the position coordinates of the signer by their sizes, they all had the same size. This normalization kept intact shape (i. e. , the relative positions of the articulations). -Shape normalization (SH): New reference "T" postures were computed from the size-normalized data of each signer. New overall reference "T" posture was defined. Shapenormalized data were obtained by subtracting individual reference postures from each frame, then adding the overall reference posture.
After that transformation, all signers had the same reference "T" posture (i. e. , same relative positions of the articulations). -Posture normalization (POST): This last normalization was applied to shape-normalized data, which are also sizenormalized. Posture-normalized data were obtained by subtracting the average posture of each signer (averaged over all their mocap examples) from each frame, then adding the average posture computed over all signers. After these three normalizations, all signers had the same size, same shape, and same average posture.
Feature Extraction: A Statistical-Based Approach The machine learning workflow is displayed in Figure  3. The mocap data of the pelvis marker were ignored as it was set as the origin, which leads to zero vectors. The position and velocity of the 18 other markers were used as temporal features. Velocity was estimated by time differentiation of the mocap position coordinates (ORI, SI, SH, or POST). Then, we measured the statistics of these temporal features. Based on previous research investigating the perception of auditory and visual textures  (Portilla and Simoncelli, 2000; McDermott and Simoncelli, 2011) , we measured the first four moments of position and velocity (Equation  1 ), and covariances of velocity between body markers (Equation  2 ). The first four moments of position and velocity described their statistical distributions, which may vary from one individual to another, as shown for expert gesture analysis  (Tits, 2018).
Kinematic Features of Importance To further understand which kind of information is useful for signer identification from POST motion, we examined the PCs used by the classifier. The identification model was run on the whole dataset with the 69 components, which allowed reaching the highest performance. Discriminant PCs were described following equation 5 (as shown in Methods). The statistical patterns (referred to as d n in Equation  5 ) of some highly discriminant PCs are displayed in Figure  6. PC1, PC2, and PC4 contributed to 38. 9%, 10. 4 and 7. 6% of the cumulative correct identification, respectively (Figure  5 ). Principal component 1 mainly described relationships between movements along vertical (Z) and anteroposterior (Y) axes, except between hand markers along the Z-axis and head markers along the Y-axis (Figure  6C ).
Moreover, no overall advantage for the frontal view has been reported by  Westhoff and Troje (2007) , whose gait PLDs were totally deprived of structural information. Whereas, for now, the ability of human perceivers to identify signers have only been studied using frontal views  (Bigand et al. , 2020) , half-profile and profile views may provide critical information, especially for kinematics. This observation is consistent with the recent machine learning model of dancer identification proposed by  Carlson et al. (2020) , which reported kinematic features of importance along all three dimensions. Similar to  Carlson et al. (2020) , the discriminant PCs revealed distinct identity-specific patterns over sensors and dimensions. For instance, whereas PC1 reflected differences in the kinematics of all body markers along the anteroposterior axis, differences along the vertical axis concerned only the trunk (e.
Eye-tracking data from reading represent an important resource for both linguistics and natural language processing. The ability to accurately model gaze features is crucial to advance our understanding of language processing. This paper describes the Shared Task on Eye-Tracking Data Prediction, jointly organized with the eleventh edition of the Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2021). The goal of the task is to predict 5 different token-level eyetracking metrics from the Zurich Cognitive Language Processing Corpus (ZuCo). Eyetracking data were recorded during natural reading of English sentences. In total, we received submissions from 13 registered teams, whose systems include boosting algorithms with handcrafted features, neural models leveraging transformer language models, or hybrid approaches.
, 2016; Hollenstein et al. , 2019; Toneva and Wehbe, 2019). Thanks to the recent introduction of a standardized dataset  (Hollenstein et al. , 2018 (Hollenstein et al. , , 2020)) , it is now possible to compare the capabilities of machine learning approaches to model and analyze human patterns of reading. In this shared task, we present the challenge of predicting eye word-level tracking-based metrics recorded during English sentence processing. We encouraged submissions concerning both cognitive modeling and linguistically motivated approaches (e. g. , language models). All data files are available on the competition website. 1.
Later work on the topic found that the perplexity of a language model is the primary factor determining the fit to human reading times  (Goodkind and Bicknell, 2018) , a result that was confirmed also by the recent investigations involving neural language models such as GRU networks  (Aurnhammer and Frank, 2019)  and Transformers  (Merkx and Frank, 2020; Wilcox et al. , 2020; Hao et al. , 2020). Using an alternative approach, Bautista and Naval (2020) obtained good results for the prediction of eye movements with autoencoders. In addition to the ZuCo corpus used for this shared task (see Section 4), there are several other resources of eye-tracking data for English. The Ghent Eye-Tracking Corpus (GECO;  Cop et al. , 2017)  is composed of the entire Agatha Christie's novel The Mysterious Affair at Styles, for a total of 54, 364 tokens, it contains eye-tracking data from 33 subjects, both English native speakers (14) and bilingual speakers of Dutch and English (19), and comes with the Dutch counterpart.
Data We use the eye-tracking data recorded during normal reading from the freely available Zurich Cognitive Language Processing Corpus (ZuCo;  Hollenstein et al. , 2018 Hollenstein et al. , , 2020)). ZuCo is a combined eye-tracking and EEG brain activity dataset, which provides anonymized records in compliance with an ethical board approval and as such it does not  contain any information that can be linked to the participants. The eye-tracking data was recorded with an Eye-Link 1000 system in a series of naturalistic reading experiments. Full sentences were presented at the same position on the screen one at a time. The participants read each sentence at their own reading speed. The reading material included sentences from movie reviews from the Stanford Sentiment Treebank  (Socher et al.
, 2013)  and a Wikipedia dataset  (Culotta et al. , 2006). For a detailed description of the data acquisition, please refer to the original publications. An example sentence is presented in Figure  1. We use the normal reading paradigms from ZuCo, i. e, Task 1 and Task 2 from ZuCo 1. 0, and all tasks from ZuCo 2. 0. We extracted the eyetracking data from all 12 subjects from ZuCo 1. 0 and all 18 subjects from ZuCo 2. 0. The dataset contains 990 sentences. All sentences were shuffled randomly before splitting into training and test sets. The training data contains 800 sentences, and the test data 190 sentences.
Outlook & Conclusion We presented the results of the first shared task on predicting token-level eye-tracking features recorded during natural sentences reading. We hope the CMCL Shared Task makes a lasting contribution to the field of linguistic cognitive modelling by providing researchers with a standard evaluation framework and a high quality dataset. Despite the limited size of the test set, many previously reached conclusions can now be tested more thoroughly and future models can be compared on a shared benchmark. For future editions of this shared task, we see the following improvement opportunities: (1) providing an official development set during the training phase; (2) using additional metrics for assessment, such as R 2 to achieve a better understanding of the submitted models; (3) extending the dataset to include additional eye-tracking data from other English corpora, as well as including data from other languages such as Dutch or Russian (e.
We present Multitrack Contrapuntal Music Archive (MCMA, available at https://mcma. readthedocs. io), a symbolic dataset of pieces specifically curated to comprise, for any given polyphonic work, independent voices. So far, MCMA consists only of pieces from the Baroque repertoire but we aim to extend it to other contrapuntal music. MCMA is FAIR-compliant and it is geared towards musicological tasks such as (computational) analysis or education, as it brings to the fore contrapuntal interactions by explicit and independent representation. Furthermore, it affords for a more apt usage of recent advances in the field of natural language processing (e. g. , neural machine translation). For example, MCMA can be particularly useful in the context of languagebased machine learning models for music generation.
Despite its current modest size, we believe MCMA to be an important addition to online contrapuntal music databases, and we thus open it to contributions from the wider community, in the hope that MCMA can continue to grow beyond our efforts. In this article, we provide the rationale for this corpus, suggest possible use cases, offer an overview of the compiling process (data sourcing and processing), and present a brief statistical analysis of the corpus at the time of writing. Finally, future work that we endeavor to undertake is discussed.
For those based on machine learning, data quality is, clearly, of uttermost importance. We posit that current trends in deep learning applications to music generation (requiring vast amounts of data) might encourage blind and/or non-critical usage of readily available datasets, which, in turn, might affect the musical results obtained. As computational musicology and machine learning become increasingly coupled, it is crucial to adopt a critical position towards the usage of large datasets and to adapt, modify, and curate the vast amounts of musical data to fit the context-specific analytic or generative task one undertakes  (Huron, 2013). MCMA is our contribution to the subject.
g. , a G# and an Ab are considered the same note). Moreover, MIDI keyboard input has proven to be an error-prone method. A study of a random sample of such scores from classicalarchives. com found that 8% of the encoded pitches did not match with the published score  (Shanahan & Albrecht, 2013). In particular, voice separation in contrapuntal music seems to be a marginal concern when compiling a new dataset. It is true that there are available sets of string quartets and choral music that are written with independent parts  [5]. But most available scores of keyboard music, for example, collapse the notes of a polyphonic passage into a single staff, making the discernment of voices a difficult task, unless one has formal musical analysis skills. Empirically analyzing counterpoint music is nearly impossible without voice separation (e.
Separating musical voices can also be valuable in an educational context. At the moment, counterpoint studies often focus on vocal parts; perhaps access to instrumental scores where voices are separated could allow students to more directly identify contrapuntal techniques. Additionally, instrumental scores typically comprise a wider array of intervallic motion and duration/subdivisions than does vocal repertoire (which results from the physical limitations of the human voice). Figure  1  shows a trivial example. This affordance could then be further enhanced if coupled with the usage of score annotation software, for example, Dezrann  (Giraud et al. , 2018). We also envisage uses of MCMA in the domain of generative models. For example, the relation between two voices in a contrapuntal piece becomes paramount if one wants to employ natural language processing models and treats one voice as the musical response to a "query.
THE MCMA CORPUS MCMA aims at being Findable, Accessible, Interoperable, and Reusable, in adherence to the FAIR principles  [3]. It has an extensive metadata protocol, which is described in detail in its documentation  [2]. The metadata is registered in comma-separated value files, one per collection: each musical work is assigned a globally unique and persistent identifier and is richly described with a plurality of accurate and relevant attributes. We sourced the pieces in MCMA from disparate readily available online datasets/databases under free-content licensing. For most of the files presented in MCMA, we started our curation process from symbolic scores already available in the Werner Icking Archive [4], KernScores [5], MuseScore  [6] , and other online sources  [7] [8] [9]. Regardless of the provenance's encoding format, we then proceeded to convert every piece in MCMA to compressed musicXML (mxl), which supports correct pitch spelling (a sine qua non for musicological analysis) and is natively supported by most visual editors, potentially catering for a less techno-affluent demographic.
CONTRIBUTIONS & FUTURE WORK MCMA's core archive is rather small at the time of writing. Rather than aiming to release a completed corpus with sporadic version updates, we decided to extend a call for contributions to the wider community. We anticipate this to be an endeavor of interest for the music information community and for musicologists, in general. In the project's documentation [2], detailed instructions for contributors are outlined, and the functionality of the utility assets is explained. The latter include Python scripts for the automatic generation of the metadata for a given musical collection (contingent upon the invariance of some of the metadata fields, such as the provenance, the reference edition, etc. ), the statistical analysis of the corpus, and metadata templates. If the contribution challenge were to attract sufficient interest from the community, MCMA's size could grow considerably, making it even more apt for machine learning applications (large data is a deciding factor in this context).