dataesr/scientific-paragraphs-categorization huggingface dataset:
g. the movement of the bag containing the smartphone of two distinct users travelling in a bus would be quite similar). Therefore, we emphasized: i) collecting very long travel distance in vehicles at the expense of less users; ii) acquiring multimodal data; iii) rich high-quality annotations. We ensured annotation quality through multiple processes described in this paper, including continuous verification during the data collection campaign by a supervisor researcher. To summarize, our SHL dataset was collected by 4 smartphones simultaneously, it includes 15 sensors per phone, and it totals 2812 hours of annotated data. This makes it by far the largest publicly available dataset and the most diverse in terms of data sources. Additionally, our dataset is the only one that used a camera to enhance annotations post-collection, which allowed us to provide 28 type of annotations and to guarantee their high quality.
III. DATA COLLECTION The data collection process is sketched in Figure  1. First, the three participants (we refer to them as User 1, 2 and 3) were trained to use the dataset collection equipment in order to collect a precise annotated transportation dataset. In order to ensure a balanced dataset, we prepared a weekly outline of the activities (activity scenario) that the users would focus on, but we left the users choice of the activities to carry out for each day. During the execution of the activities, the users were using an Android application to label the appropriate activities and transportation modes. After the data collection, the users used a specialized annotation tool to check and correct the annotations. Finally, the dataset was curated and processed to be released in an easy to use format.
A. PARTICIPANT RECRUITMENT Previous work has shown that data can be collected from volunteers with or without financial incentive  [25]. However, to ensure the quality of the data collected and the corresponding annotations, we planned and executed the data collection with participants which were hired as employees of the University. This also provided the participant with insurance coverage (which was fortunately not used). The participants were chosen after a 45-minute long interview process. During the interview, we explained in significant detail the data collection process to the participant. We believe detailed explanations were instrumental in not having any participant withdrawing from the the data collection. While participants were employed specifically to perform a full-time, precise, and controlled data collection, we also explained them that they could and should attempt to go about their usual daily activities, as they would even without participating in this project, as this would provide a more natural dataset.
In the dataset this location is referred to as "Hand"; • the 2nd phone was carried in a jacket breast pocket (if available) or on a chest strap (as shown in Figure  2 ) or on the upper abdomen for User 3. In the dataset this location is referred to as "Torso"; • the 3rd phone was carried in the trousers front pocket. This location is referred as "Hips" in the dataset; • the 4th phone was carried in a backpack. The User 3 sometimes wore this phone in a side bag. The corresponding data is referred to as "Bag" in the dataset. The rationale for this sensor placement was to collect data from as many typical locations where phones are carried, so that the analysis and the models created on this dataset are general and cover most real-life situations. Additionally, the participants wore a front-facing body camera, which was used to verify label quality during a post-collection annotation procedure.
As a part of the dataset it will allow vision-based applications, such as object recognition. The camera was worn on the chest or backpack straps, generally facing the forward direction. In the car, we asked the participants to orient the camera to take pictures of the road, which later helped them to annotate the road condition and the presence in tunnels. The camera was set to take pictures at regular interval every 30 seconds, which is frequent enough to help the participant to recall the data collection details during the course of the day and, meanwhile, is less invasive to surrounding privacy than a continuous video recording.
C. SMARTPHONE APPLICATION AND SENSORS The phones were equipped with a custom data logging application 2   [27]  as shown in Table  2. The screenshot shows 4 parts. The top part displays the status of the data collection. The second part shows the status of the Bluetooth connection with the other 3 phones. The application synchronizes the 4 phones using Bluetooth using a masterslave communication protocol. The participant uses only the master phone (i. e. , the one in the hand) to interact with the application. The master phone will synchronize with the other 3 phones automatically. The third part shows the status of the most recent upload of the data to the server. The last part is the current annotation and label selection, where the user chooses the appropriate current activity (transportation mode), posture and location.
E. DATA COLLECTION PROCESS The daily data collection procedure started with a check list that the participants had to follow to properly start the data collection. That included restarting all the phones and connecting them with Bluetooth. Next, the phones and the camera were synchronized by the first photo taken by the camera. That is, when the camera took the first photo, the participant had to provide a recognizable acceleration pattern -putting the phones on a table and hitting the table with the hand 5 times. Later, we used the photo and the 5 acceleration peaks (maximum values on the acceleration graph) to synchronize the smartphone sensors and the camera photos. After the synchronization, the participants followed the outline of the daily activity scenario and performed and labeled the activities using the smartphone in the hand.
G. DATA CURATION The data which was downloaded from the phone was transformed into a format more manageable for future uses. We resampled all the motion sensor data (acceleration, gyroscope, etc. ) of all 4 phones onto a common 100Hz sampling grid. This simplifies future applications of signal processing techniques on the data. We used the high accuracy timestamps, which the Android system assigns upon sensor data acquisition for this purpose. As these timestamps are reset to zero upon reboot, we exploit the network time to ensure time synchronization across the multiple phones. The resulting data is stored in plain text format, which allows easy import in various scientific tools. In addition, the released dataset contains the time-lapse video and a visualization of the user's traces and activities for each day.
We found this metric to be satisfactory for all sensors. Additionally, we automatically detect possible reboots of a phone. As the application restarts logging automatically if a reboot occurs this measure is used to identify if a user manipulation error occurred (e. g. long-pressing the power button leading to a reboot). Finally, we converted these metrics into a binary decision about the quality of each sensor channels for each phone. In the final dataset we release recordings for which we obtained a positive decision.
B. SENSOR ANALYSIS Figure  8  shows a heatmap of the GPS location data. On the left side are all the visited locations, and on the right side is the zoomed map on the Brighton area, where the University of Sussex leading this study is located. Note that most of the time the users were around Brighton, with regular visits to London. Also, User 1 had a 2-day visit in Liverpool. In the Brighton area, most of the time the users were around the city center, and the University of Sussex campus. In total, the participants travelled 17562 km. Figure  9  shows the GPS coverage (i. e. the percentage of the dataset where one or more satellites are visible) according to each of the annotations for the main 8 activities. Because the GPS is logged every second, the coverage is calculated as a ratio between the total number of samples collected and the duration of the activities in seconds.
V. EXEMPLARY MACHINE LEARNING APPLICATION: LOCOMOTION AND TRANSPORTATION RECOGNITION One important motivation for collecting the SHL dataset is to create intelligent systems capable of recognizing the transportation mode of the user. In this section we show  an exemplary machine-learning pipeline performing transportation mode classification using the multimodal sensor data from this dataset. We use the data from User 1 with the four smartphone body positions (i. e. Hand, Torso, Hips, Bag), which amounts approximately to 360 hours per body position collected over 82 days (Fig. 5 ). For the analysis we considered only the 8 main activities: still, walk, run, bike, car, bus, train and subway. Figure  14  illustrates the processing pipeline for transportation mode recognition. We split the data into 10 folds (approximately 8 days in each fold) and performed a timedependent 10-fold cross validation (for each testing fold, we used the rest 9 to train the model).
subway). The reason for this is the similar motion patterns during these activities. Some confusion between still and the four vehicle activities (car, bus, train and subway) is also observed. Typically, some vehicle classes are recognized as still. This is possibly because the smartphones tend to be motionless when vehicle stops. For instance, bus is more frequently recognized as still than other vehicles which may be due to it stopping more frequently. In contrast, car is least recognized as still which may be due to it stopping less frequently. In this example we mainly aim to demonstrate the usefulness of the SHL dataset to the research in locomotion and transportation recognition rather than maximizing the recognition performance. Even though, several interesting observations can still be made from experimental results, such as the limitation of using motion sensors alone (which are present in most of the related datasets) for distinguishing the basic 8 types of transportation modes and the influence of sensor placement on the recognition performance.
Also, the WiFi and Cell scanning were insufficient during some of the early recordings due to relying on a "best effort" scanning of Android. We modified the logging software to force reporting of WiFi and Cell at 1 second interval. While this forces the operating system to issue sensor events to the application at that rate, Android does not guarantee that this changes the underlying driver WiFi and cells scan rate. A possible solution would be to use a dedicated firmware only for data collection, without interruptions from the standard Android system. All the participants agreed that carrying the equipment was comfortable enough in most cases, except for some situations such as during running. Additionally, participants agreed that the phones were too big (the Mate 9 has a 5. 9" screen), especially the one in the hand that was used for annotation.
This dataset provides rich data for wireless sensor network and mobile communication research, in particular it allows to explore and predict wireless coverage (WiFi or cellular) according to transportation modes, displacement speed, or user location. This can enable useful applications in adaptive data streaming to minimize the impact when a connection quality degrades. Similar analysis can be made on Satellite reception in function of the user's transportation modes. There are numerous additional applications for this dataset. A few examples are: • Road condition analysis and recognition. The dataset also contains labels about the road conditions, therefore it can allow research in this field, such as development of supervised machine learning model using the smartphone sensors to automatically detect he road conditions.
VII. CONCLUSION In this data collection campaign, we focused on obtaining a precisely annotated sensor-rich dataset, which is also representative of real-life. We encouraged participants to mesh their everyday routines with the data collection protocol as much as possible to ensure a dataset representative of everyday life. We used 4 high-end smartphones simultaneously placed on typical body locations to maximise the amount of data collected during recordings. We used a bodyworn camera to help us further improve the accuracy of the annotations. To ensure the quality of the annotations, the participants used a dedicated tool to check the annotations and introduce additional ones after the data collection completed. In total 28 labels were annotated, including the mode of transportation, participant's posture, inside/outside location, road conditions, traffic conditions, presence in tunnels, social interaction, and having meals.
The dataset comprises 703 hours of recordings, which correspond to 2812 total hours of labeled data collected given the simultaneous recording from 4 locations. This took place over 7 months with three participants who engaged in 8 different modes of transportation in the southeast of the United Kingdom. Even though the number of participants is limited to three, our focus was on the quality of the collected and annotated data (28 labels in total), and on collecting real-life data over long period (2812 hours of labelled data and 17562 km of traveled distance collected over 7 months). This longitudinal data collection allows studies about changes in behavior and transportation usage over time. The full dataset will be made available to the community in batches over 2018 as privacy verification is completed.
A preview of the dataset including 4x59=236 hours of data is already published at http://www. shl-dataset. org/. The full dataset will be released in the exact same file format. This allows studies done on the preview dataset to be seamlessly scaled up as the full data is released. The large number of included sensors at different body locations, the diverse set of activities and their precise annotation make this dataset a valuable foundation for various research fields. Besides the automatic recognition of transportation modes which we exemplified here, it can be used for research in detection of social interaction, road conditions detection, traffic conditions detection, localization and sensor fusion. Further applications are expected based on the recorded sound and the camera data such as recognizing surrounding objects, or recognizing activities and the wider in which they occur.
Assessment of Google's activity and transportation recognition API in comparison to novel methods developed based on this dataset. Researchers can compare to a state of the art commercial model developed by Google, and can introduce improvements. • Creating probabilistic mobility and locomotion models, which are commonly used in wireless sensor network research. • Novel localization techniques using dynamic fusion of sensors. This is also a topic of interest to many researchers. Improving the localization of the user by using non-intrusive sensors can bring many applications to improve the quality of life of the users. For example, improving the indoor localization by using data fusion techniques for smarpthone sensors can be used for elderly monitoring applications. • Image-based activity and transportation mode recognition, object recognition in everyday time-lapse video, context recognition from images (e.
DANIEL ROGGEN (M'04) is Associate Professor in Sensor Technologies at the University of Sussex, where he leads the Wearable Technologies Lab and directs the Sensor Technology Research Centre. His research focuses on wearable and mobile computing, activity and context recognition, and intelligent embedded systems. He has established a number of recognised datasets for human activity recognition from wearable sensors, in particular the OPPORTUNITY dataset. He is member Task Force on Intelligent Cyber-Physical Systems of the of the IEEE Computational Intelligence Society. He received his Masters degree (2001) and (2005) from the Ecole Polytechnique Federale de Lausanne, Switzerland. VOLUME 4, 2016.
• Assessment of Google's activity and transportation recognition API in comparison to novel methods developed based on this dataset. Researchers can compare to a state of the art commercial model developed by Google, and can introduce improvements. • Creating probabilistic mobility and locomotion models, which are commonly used in wireless sensor network research. • Novel localization techniques using dynamic fusion of sensors. This is also a topic of interest to many researchers. Improving the localization of the user by using non-intrusive sensors can bring many applications to improve the quality of life of the users. For example, improving the indoor localization by using data fusion techniques for smarpthone sensors can be used for elderly monitoring applications. • Image-based activity and transportation mode recognition, object recognition in everyday time-lapse video, context recognition from images (e.
1. 3 software and by FlowJo. Live FAPs were isolated as Ter119-/CD45-/CD31-/α7-integrin-/ CD34+/Sca-1+ cells. FAPs subpopulations were isolated from diaphragm and hindlimb muscles as described above and sorted by FACS after immune labeling as follows, using the primary antibodies anti-CD31-PacificBlue (RM5228, Life Technologies, 2 µg/ml), anti-CD45-eFluor450 (clone 30-F11, eBioscience, 4 µg/ml), anti-Ter119-eFluor450 (clone TER-119, eBioscience, 4 µg/ml), anti-Sca-1-FITC (clone E13-161. 7, BD Pharmingen, 10 µg/ml), anti-α7 integrin-Alexa Fluor 647 (clone R2F2, AbLab, 4 µg/ml), anti-Tie2-PE (clone TEK4, BioLegend, 8 µg/ml), and anti-Vcam1-PE•Cy7 (clone 429 MVCAM. A, BioLegend, 1 µg/ml) for 45 min at 4 °C, in a FACS Buffer containing 10% goat serum. After the immune-staining cells were processed for FACS analysis as described above.
FMO controls were prepared with aliquots of cells. Single color controls were prepared using UltraComp eBeads (eBioscience). Flow Cytometry studies were performed at the Sanford Burnham Prebys Medical Discovery Institute Flow Cytometry Core using LSRFortessa X-20 cell analyzer and FACSAria sorter (BD), and the data were analysed by FACSDiva 6. 1. 3 software. For macrophage isolation, hindlimb skeletal muscles were processed as described above and analyzed by FACS in LSRFortessa X-20, but also in DAKO-Cytomation MoFlo High Speed Sorter, analysing data by Summit V4. 3 software (Beckman Coulter). The staining-protocol is identical and the following antibodies were used: anti-CD45-eFluor450 (clone 30-F11, eBioscience), anti-CD11b-PE-Cy7 (clone M1/70, eBioscience, 1:200), anti-F4/80-PE (clone BM8, eBioscience, 1:50), and anti-GR1-e780 (clone RB6-8C5, eBioscience, 1:200).
All FACS data were further analysed by FlowJo version 10. 0. 4 (FlowJo LLC). Single cell capture and cDNA preparation. C1 Single-Cell Auto Prep System (Fluidigm) was used for isolation of FAPs single cell RNA (isolated from male mice), following the manufacturer's protocol (#100-4904, Fluidigm), using 10-17 µm medium size C1 IFC. Integrity of captured FAPs on the C1 IFC was determined under the confocal microscope. Captured cells were lysed and the RNA was reverse transcription and cDNA was pre-amplified using DELTAgene Assays targeting 87 selected genes (Supplementary Table  1 ). BioMark real-time PCR on single cell cDNA. Collected pre-amplified cDNA was analysed by real-time PCR using Fast Gene Expression Analysis protocol with EvaGreen® on the BioMark HD System with 87 selected gene-specific assays (Supplementary Table  1 ) (protocol #68000088, Fluidigm), skipping the exonuclease I treatment of C1 collected cDNA  20.
Single cell gene expression data analysis. Data analysis after BioMark qPCR is described in detail in our Methods and Protocols chapter  20. Briefly, we manually checked for data quality in the BioMark RT-PCR analysis software and we extracted the data. Data manipulation was performed in R, where we first eliminated all the data marked as "Fail" in the CtCall. During the quality control of single cell gene expression data analysis, genes that failed qPCR reactions and cells that either did not express FAP identification markers (Sca1 and Pdgfra) or expressed SC markers (Itga7) or endothelial marker (Pecam1/CD31) were excluded from the data set in order to avoid the inclusion of cell types different from FAPs in the analysis. We then calculated the gene expression level as Log 2 Ex (Log 2 Ex = Ct (LOD) -Ct (gene) ) and set low expressing cells (Log 2 Ex < 0) to zero.
We used a LOD of 24, as estimated by Livak et al. 43. All graphs were produced in R. SOM analysis was performed with kohonen package in R (v2. 0. 19). RNA isolation and qPCR analysis. RNA from FAPs was extracted using miR-Neasy Micro kit (Qiagen) following the manufacturer's protocol, including DNase treatment of the samples. cDNA was synthetized using QuantiTect Reverse Transcription kit (Qiagen) and analysed by real-time quantitative PCR (RT-PCR) using SYBR Green PCR Master Mix (Applied Biosystems), using primers provided in Supplementary Table  2. Relative expression was calculated using 2 -Δct method  44  , Gapdh housekeeping gene expression was used to normalize gene expression. RNA sequencing. SubFAPs and FAPs were isolated from hindlimb muscles of C57Bl/10 male mice in two independent experiments.
The number of cells ranged between 7700 and 230,000. RNA from FAPs was extracted using miRNeasy Micro kit (Qiagen) following the manufacturer's protocol. RNA was shipped to the sequencing facility as dry pellet in RNAstable® (Biomatrica). The libraries for sequencing were prepared using Ovation SoLo RNA-Seq System by NuGEN. For each experimental condition two independent experiments were carried out for the isolation of RNA. All biological duplicates are from different cohorts of mice, sorted at different times. RNA sequencing data processing. For sequencing alignment, we used the human reference genome assembly GRCm38/mm10 (http://ftp. ensembl. org/pub/release-76/fasta/mus_musculus/dna/), while for transcriptome annotation we used the version 85 of the GRCm38 (http://ftp. ensembl. org/pub/release-85/gtf/ mus_musculus/Mus_musculus.
GRCm38. 85. gtf. gz). We used the FASTQC package (v0. 11. 3) to assess the quality of sequenced libraries. All passed quality control. Because, we were using the Ovation SoLo RNA-Seq System by NuGEN, we adhered to the manual suggestions and trimmed 5 bases from the 5′ of the sequences. Reads were mapped to the reference genome using TopHat2 v. 2. 1. 1  45. We used the following non-default TopHat2 parameters: -p 48 -g 1 --library-type frfirststrand. The number of mapped reads ranged between 23 and 44 × 10 6 and the percentage of mapping was between 79 and 92%. The quality control of the reads distribution along transcripts was performed using infer-experiment. py from RSeQC package v2. 6. 3  46. All samples had a uniform distribution of reads along transcripts. The sequenced read counts per annotated gene were derived with the use of htseq-count script distributed with HTSeq v0.
5. 4p5  47. We used the R library package DESeq2 v. 1. 12. 4  48 for measuring differential gene expression between two different cell conditions, considering the two RNA-Seq experiments as biological replicates. We picked genes with adjusted p-value < 0. 01. Gene ontology analysis was performed using Ingenuity pathway analysis (IPA; http://www. 1ingenuity. com). All graphs were produced in R. Public RNA-seq data from mouse tissue-resident fibroblasts were downloaded from GEO with accession numbers GSM1223640 and GSM1223641 (cardiac fibroblasts), GSM521651 (lung fibroblasts), GSM2500874, GSM2500875 and GSM2500876 (synovial fibroblasts), and GSM2067698, GSM2067699 and GSM2067700 (skin fibroblasts) (see Supplementary Table  3 ). Data sets from Satellite cells (SRR7075694, SRR7075695, SRR7075698, SRR7075699, SRR7075710, SRR7075711), and macrophages (SRR7075704, SRR7075705, SRR7075706, SRR7075707, SRR7075708, SRR7075709) were kindly provided by S.