dataesr/scientific-paragraphs-categorization huggingface dataset:
The web server also includes a selection of prebuilt collections of reference tracks that may be useful in a broad variety of settings, including collections generated by the authors of the LOLA and Giggle tools  (9, 13). These are here referred to as core databases, and can be selected directly in the Coloc-stats tool. At last, collections of tracks for specific epigenomic marks or relating to specific cell types can be easily constructed from datasets available in public repositories like ENCODE  (18)  or Roadmap Epigenomics  (19)  (tool:'Create a GSuite from an integrated catalog of genomic datasets'). Certain tools (4,6,9) accept a third track that defines which regions of the genome are to be included in the analysis. This track typically represents the universe of regions that could have possibly ended up in the genomic tracks of interest being queried for colocalization.
System architecture The Coloc-stats web server is built upon a sophisticated infrastructure. Each tool is containerized as a Docker image (https://www. docker. com/) and wrapped by Common Workflow Language (https://www. commonwl. org/). The web interface is based on the Galaxy framework (200), which facilitates reproducible and transparent research by allowing users to easily share analysis histories as well as inspecting data and parameter selections underlying results of interest. The GUI selections are mapped to the lowerlevel architecture using Python. The web server is running in a virtual machine within a national cloud solution. We have made our codebase for integrating the various methods available for download as an easily installable package 'pycolocstats'.
CONCLUSION Statistical assessment of colocalization often represents a final conclusive stage of an analysis, which entails many complications that if ignored can directly lead to erroneous conclusions in the form of false findings. It is thus advised to assess robustness of findings by employing alternative methodologies and carefully consider their underlying assumptions. We have thus developed Coloc-stats, which integrates a large number of published colocalization analysis tools, provides a unified user interface based on explicit selection of modeling assumptions and includes help pages and FAQs to guide on good scientific conduct for this setting. An easily accessible user interface allows multiple methods to be selected based on compatibility with analysis assumptions and run in parallel on datasets of interest.
Get datasets, either: (a) Upload query and reference tracks (bed-files) to the Galaxy history (b) Import sample data to the Galaxy history (c) Upload only a single query track (bed-file) of interest, and evaluate the colocalization of the query against multiple reference tracks from a built-in collection (d) Use functionality from GSuite HyperBrowser to create track collections from files provided by the user or downloaded from online repositories following metadata search ii. Go to the coloc-stats tool, to either (a) Use the basic mode to assess colocalization of your datasets according to the analytical approaches inherent in each selected tool (b) Use the advanced mode to assess colocalization of your datasets according to more narrowly specified analytical settings and assumptions iii.
i. Get datasets, either: (a) Upload query and reference tracks (bed-files) to the Galaxy history (b) Import sample data to the Galaxy history (c) Upload only a single query track (bed-file) of interest, and evaluate the colocalization of the query against multiple reference tracks from a built-in collection (d) Use functionality from GSuite HyperBrowser to create track collections from files provided by the user or downloaded from online repositories following metadata search ii. Go to the coloc-stats tool, to either (a) Use the basic mode to assess colocalization of your datasets according to the analytical approaches inherent in each selected tool (b) Use the advanced mode to assess colocalization of your datasets according to more narrowly specified analytical settings and assumptions iii.
While the English language dominates the internet, native English speakers are far outnumbered by speakers of English as a foreign language. Consequently, a vast amount of static and dynamic web content is continuously generated by nonnative writers. Developing methods for identifying the native language of nonnative English authors on social media outlets is therefore an important and pertinent goal. ⇤ *Work done while the second author was at the University of Haifa. We address the task of native language identification in the context of user generated content (UGC) in online communities. Specifically, we use a large corpus of English Reddit posts in which the L1 of authors had been accurately annotated  (Rabinovich et al. , 2018). On this dataset, we define three closely-related tasks: (i) distinguishing between native and nonnative authors; (ii) determining to which language family the native language of nonnative authors belongs; (iii) identifying the native language of nonnative authors.
Related work The NLI task was introduced by  Koppel et al. (2005) , who worked on the International Corpus of Learner English  (Granger, 2003) , which includes texts written by students from Russia, the Czech Republic, Bulgaria, France, and Spain. The same experimental setup was adopted by several other authors  (Tsur and Rappoport, 2007; Wong and Dras, 2009, 2011). The task gained popularity with the release of nonnative TOEFL essays by the Educational Testing Service  (Blanchard et al. , 2013) ; this dataset has been used for the first NLI Shared Task  (Tetreault et al. , 2013)  and also for the 2017 NLI Shared Task  (Malmasi et al. , 2017). Our task is closely related to the task of dialect identification, in which the goal is to discriminate among similar languages, language varieties and dialects.
Classic machine learning classification methods are usually applied for this task, often with SVM models. The best reported features include word and character n-grams, part of speech n-grams and function words  (Zampieri et al. , 2017; Malmasi and Zampieri, 2017). The current state of the art in NLI, according to  Malmasi and Dras (2017) , utilizes some variant of contemporary machine learning classifier with the following types of features: (i) word, lemma and character n-grams, (ii) function words (FW), (iii) part-of-speech (POS) n-grams, (iv) adaptor grammar collocations, (v) Stanford dependencies, (vi) CFG rules, and (vii) Tree Substitution Grammar fragments. The best result under crossvalidation on the TOEFL dataset, which includes 11 native languages (with a rather diverse distribution of language families), was 85.
2% accuracy. Applying these methods to different datasets (the ASK corpus of learners of Norwegian  (Tenfjord et al. , 2006)  and the Jinan Chinese Learner Corpus  (Wang et al. , 2015) , 10-11 native languages in each) resulted in 76. 5% accuracy for the Chinese data and 81. 8% for the Norwegian data, with LDA-based classification yielding top results. Notably, all these works identify the native language of learners. Identifying the native language of advanced, fluent speakers is a much harder task. Furthermore, our dataset includes texts by native speakers of 23 languages, more than double the number of languages used in previous works; and our L1s are all European, and often typologically close, which makes the task much harder. Two recent works address the task of NLI on UGC in social media.
Dataset Reddit is an online community consisting of thousands of forums for news aggregation, content rating, and discussions. Content entries are organized by areas of interest called subreddits, ranging from main forums that receive much attention to smaller ones that foster discussion on niche areas. Subreddit topics include news, science, arts, and many others. An increasing body of work has used Reddit data for social media analysis  (Jurgens et al. , 2015; Newell et al. , 2016, and many more). We used the Reddit dataset released by  Rabinovich et al. (2018). It includes Reddit posts (both initial submissions and subsequent comments), focusing on subreddits (Europe, AskEurope, Euro-peanCulture) whose content is generated by users specifying their country as a flair (metadata attribute).
We refer to these subreddits as European. Following  Rabinovich et al. (2018) , we view the country information as an accurate, albeit not perfect, proxy for the native language of the author. Rabinovich et al. (2018)  justified their trust in the accuracy of the L1 annotation; we conducted an additional validation of the data. 2 We used a specific Reddit thread in which users were asked to comment in their native language. We collected the comments in this thread of all the users in our dataset. Then, we used the Polyglot language identification tool to determine the language of the comments. We filtered out short comments, comments for which the tool's confidence was low, and comments in English of users from non-English speaking countries. Of the remaining 572 users, 479 (84%) contributed comments in the language that we considered their native.
We inspected the remaining users, and for many (albeit not all) we attribute the mismatch to errors in the tool (i. e. , comments in Serbian written in the Latin alphabet are wrongly predicted to be in closely-related Slavic languages). We conclude that the accuracy of the L1 annotation is high; finally, we note additionally that any noise in this labeling can only work against us in this work. We filtered out data from multilingual countries (Belgium, Canada, and Switzerland). Rabinovich et al. (2018)  showed that the English of reddit nonnative authors is highly advanced, almost at the level of native speakers, making the NLI task particularly demanding. All the posts in the dataset are associated with a unique user ID. The dataset also contains submissions and comments in other subreddits that were written by the same authors, based on their user ID.
Preprocessing Each sentence in the dataset is tagged with the author's user ID, the subreddit it appeared in and the author's country. We segmented the dataset into chunks of 100 sentences, each chunk containing sentences authored by the same user. 3 The sentences were kept in their original order in the posts; users with fewer than 100 sentences were filtered out. We also left out native languages with fewer than 100 users (after the initial filtering). The resulting dataset includes 23 native languages spanning 29 countries, and consists of 34,511 unique users, almost 200M sentences and over 3B tokens. The countries and languages reflected in the dataset are listed in Table  10  (see Supplementary Materials). All chunks were annotated for part-of-speech using Spacy. We used Aspell to spell-check the texts; every misspelled word in the original chunk was annotated with the first correction suggested by the spell checker.
Methodology We cast NLI as a supervised classification task and used logistic regression (as implemented in Scikit-learn) as a classification model. We defined several features that had been proven useful for similar tasks; some of them are general stylistic features that are presumably content-independent: these include function words, POS n-grams, simplification measures such as sentence length, etc. (Rabinovich and Wintner, 2015; Volansky et al. , 2015). Other features are content based; most obviously, token n-grams, but also character n-grams  (Avner et al. , 2016). We expect content-based features to be highly accurate but also highly domaindependent, and in the case of our dataset, topicdependent. Content-independent features are expected to be weaker yet more robust. In addition, we used features that reflect spelling and grammar errors.
Content features Authors are more likely to write about topics that are related to their country and their culture, hence features that reflect content may help distinguish among authors from different countries (and, therefore, languages). For example, the word 'Paris' is more likely to occur in texts written by French authors, while the word 'canal' is more likely to occur in texts of Dutch authors. We defined features that take text content into account. We expect these features to yield high accuracy when testing on the training domain, but much lower accuracy when testing on different domains. Character tri-grams The top 1000 most frequent character 3-grams in the dataset were used as features. For each chunk the value of a certain character 3-gram feature was the number of its occurrences in the chunk normalized by the total number of character 3-grams in the chunk.
For each chunk, the value of this feature is the number of occurrences of each substitution (a character pair), insertions, and deletions in the chunk. We only used the top-400 most frequent substitutions. We initially classified spelling errors as contentindependent features, assuming that they would reflect transfer of linguistic phenomena from L1. However, having analyzed this feature type, we observed that many of the mis-spelled words turned out to be non-English words, which apparently are abundant in our dataset even after removing non-English sentences. We therefore view this feature as content dependent. Grammar errors We hypothesize that grammar errors made by nonnatives may reflect grammatical structures revealing their L1. We therefore used LanguageTool, a rule-based grammar checker, to identify grammatical errors in the text.
We extracted both types of karma scores for all users in the dataset and used each of them (specifically, the user's monthly average scores) as a feature. Average score Reddit calculates a score for each submission as the number of up-votes minus the number of down-votes the submission received. We used the user's average score per month as a feature. Average number of submissions We counted for each user the total number of submissions he or she authored. For each chunk the value of this feature is the user's average number of submissions per month. Average number of comments Same as the above, but counting user's comments (responses to submissions) instead of submissions. Most popular subreddits Finally, we assume that native speakers of the same language tend to interact more with each other than with others, and we also assume that they are more likely to be interested in similar topics, influenced by their country and culture; specifically, we hypothesize that the forums in which users post most will be common for users from the same country.
Therefore, we extracted for each country in the dataset the most popular subreddits among users from this country. For each country, we sorted subreddits according to the number of users from this country who posted at least once in this subreddit. The 30 most popular subreddits of each country 7 The Reddit Wiki. were taken as features. The unique list of popular subreddits contains 141 subreddits. For each chunk the value of a certain subreddit feature was a binary value indicating whether or not the author of this chunk has posted in this subreddit.
Conclusion We described a system that can accurately identify the native language of highly-advanced, fluent nonnative authors as reflected in the social media Reddit corpus. This is among the first studies to perform NLI in the highly challenging scenario of user generated content, particularly at such a large scale. We showed that while content-dependent features yield more accurate results, features that abstract away from content tend to be more robust when tested out of the domain of training. The in-depth analysis of spelling and grammar errors demonstrates that mistakes made by nonnative speakers reflect traces of their native language. We also illuminated some of the social characteristics of native and nonnative authors on social media outlets. Our future plans include adaptation of the trained models to additional corpora, e.
Spurious oc-currences of 'e' were also very common among all nonnative users, especially authors whose L1 was French: 'governement', 'unemployement', 'ex- a. , in New Zealand English: 'Organisation', 'Recognise', 'Realise', 'Criticise', etc. Replacing 's' with 'z' was not as common in the dataset, and was present mostly in texts of French plicitely'. Deletions of 'e' were also very common, especially in the context of words that end with 'ely' : 'definitly', 'completly', 'extremly', 'absolutly', etc. Spurious instances of 'u' were mostly present in texts of authors with Germanic and Romance L1s, e. g. : 'languague', 'percentuage'.
I. INTRODUCTION The recent technological advances in smartphones allow to collect rich sensor data, which can be used to discover knowledge about the user's activities and context. This enables new applications providing tailored context-aware services to the user  [1] ,  [2]. For example, if an intelligent system is aware that the user is driving it can provide traffic information and suggest better routes  [3]. In recent years, there have been numerous studies analyzing multimodal sensor data collected from smartphones during locomotion and motorized transportation  [4]. Analyzing such multimodal data enables context-aware applications in fields such as localization  [5] , activity and health monitoring  [6] , parking spot detection  [7] , content delivery optimization  [8] ,  [9] , etc.
While there are numerous datasets related to gait and activity analysis with wearable sensors  [10] ,  [11] , there are only a limited number of datasets for a more general analysis of locomotion and usage of transportation modes (e. g. public transport, bicycle, etc. ). To our knowledge, only two transportation datasets collected with wearable sensors are publicly available. The first one is Microsoft's GeoLife dataset  [12] , which provides 50176 hours of data collected by 182 users. The dataset however contains only GPS traces which prevents its use in multimodal analytics. The other available dataset is the recent US-Transportation dataset  [13] , which contains the data of 9 sensors from a smartphone but contains only 31 hours of data. It is recorded with a single smartphone, which prevents using it to assess the effect of device placement on the resulting sensor data.
More generally, the lack of a publicly available dataset with sufficient duration, large number of sensor modalities, and rich high-quality annotations obviously holds back research advances in this area. To overcome this, we designed the University of Sussex-Huawei Locomotion-Transportation (SHL) dataset 1 , which aims to be a highly versatile dataset suitable for a wide range of studies in fields such as transportation mode recognition, mobility pattern mining, localization, tracking and sensor fusion. It is designed to support reproducible research through its versatility, multimodality, large size, and its public availability. The availability of such a dataset enables research groups to compare methods on identical data while leaving significant room for wide variety of new studies. To achieve this versatility, we designed a large-scale longitudinal data collection campaign, and collected 2812 hours of labelled data over a period of 7 months.
The SHL dataset contains multimodal locomotion and transportation data, which was recorded by three participants engaging in 8 different modes of transportation in reallife settings, travelling 17562 km in total. Even though the number of participants is three only, our focus was on the multimodality of the collected data, the quality and richness of the annotations, and on the collection of real-life 1 Available at www. shl-dataset. org data over a long period of time so that we can also study changes in behavior and usage of transportation modes. Each participant carried four smartphones simultaneously at common body locations, which results in 4x703=2812 hours of annotated data. Each smartphone was logging the data of the 15 sensors available in the smartphone (e. g. , inertial sensors, GPS, ambient pressure sensor, ambient humidity).
Beside the smartphones, the participants also wore a front-facing camera, which allowed us to verify and correct annotations and to introduce additional postcollection annotations. This resulted in 28 total annotation types, including 8 modes of transportation, the participant's posture, inside/outside location, road conditions, presence in tunnels, social interaction, and having meals. A preview of the dataset is already available, which consists of 4x59=236 hours of labeled data. The complete version of the dataset will be published together with a detailed benchmark of a transportation mode recognition pipeline. The contributions of the paper are: • a review of the existing transportation datasets collected with wearable sensors and their characteristics (Section II); • a detailed description of the data collection procedure and the data quality check-up techniques (Section III); • the analysis and statistics of the dataset's annotations and the sensors (Section IV); • an exemplary use of machine learning to recognize modes of transportation (Section V); • the lessons learned and some of the best practices we developed for data collection, assuring data quality and reducing loss (Section VI); • a discussion of the other applications which can be developed using this richly-annotated dataset (Section VI).
II. STATE OF THE ART In contrast to the numerous datasets for activity recognition and gait analysis  [10] ,  [11] , the number of datasets that deal with the analysis of locomotion and usage of various transportation modes is rather limited. Table  1  lists the related locomotion and transportation datasets and their characteristics, including the number and type of devices simultaneously worn by the participants during the dataset recording, the type of sensor data collected, the number of participants, the amount of data, the kind of annotations and the availability of the dataset. There are two datasets which offer more hours of data than the SHL dataset we introduce here. The first is the Microsoft's GeoLife dataset  [12] , which is one of the largest publicly available datasets (with 50176 traces) but which contains GPS data only.
The other is the HTC dataset  [14] , which has a duration up to 8311 hours but only contains the data from 3 inertial sensors. Even though the authors claimed that a small part of the data is publicly available, we were not able to access it. The SHL dataset which we introduce here and the one collected by Reddy  [20]  are the only ones that were collected with multiple devices worn by the same participant. This allows to characterize the effect of the placement of the device on various types of analyses (e. g. satellite reception), or to create placement-independent recognition models. Our SHL dataset contains a significantly larger number of sensors (15 smartphone sensors and a time-lapse video) compared to the others, which mainly contain accelerometer, GPS, and in some cases gyroscope, magnetometer and barometer.
The US-Transportation dataset has been published recently and it contains data from 9 sensors collected by a single smartphone, but it has a limited duration of only 31 hours. In the SHL dataset, the wide variety of sensors modalities, and the availability of the sensors at 4 locations simultaneously allows a wide range of analyses about the contribution of combinations of sensors modalities to the problem being addressed. These problems could include transportation recognition, multimodal localization, sensorbased energy-efficiency analysis, energy saving through sensor duty cycling and others. Our dataset was collected by only three participants, which is less than the other datasets. However, the variability in the sensor signal during transportation is primarily stemming from the motion of the vehicle as the movements of users within a vehicle are constrained (e.