dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , t-distributed stochastic network embedding (t-SNE) against Uniform Manifold Approximation and Projection (UMAP)  [36, 37]. Ultimately the user is encouraged to play with the plotting parameters to yield the best-looking clusters. Static plots or interactive environments. Cluster inspection tools are available that allow users to easily generate static plots tailored to pipeline-specific information as originally defined by the software package authors. However, the AnnData and LOOM specifications store these map projection data separately, enabling the use of a plethora of possible plotting tools, including HTML5-based interactive visualizations, such as cellxgene  [38] , that permit on-demand querying and rendering of individual cell features without the need to generate static images.
Pseudotime trajectory analysis Inferring developmental pathways. The cell pseudotime series analysis is often referred to as the trajectory inference stage because cells are ordered along a trajectory to reflect the continuous changes of gene expression along a developmental pathway under the assumption that the cells are transitioning from one pluripotent type to another less potent type. Pseudotime techniques. For the trajectory inference stage, there is the Partition-based Graph Abstraction (PAGA) technique championed by ScanPy  [39] , and there are also the FateID and StemID packages for the RaceID workflow  [40]. The former provides a level of graph abstraction to the datasets in order to infer a community graph structure, which it can use to learn the shape of the data and infer pathways between neighbourhoods.
Tutorial structure Tutorials usually consist of a hands-on workflow that guides the user through an analysis with Galaxy utilizing a step-by-step approach, often accompanied by a slide deck that either serves to explain stand-alone concepts more concisely or is used during workshops and trainings as a way to introduce the user to the topic. In an effort to maintain reproducibility in science, all tutorials require example workflows, and all materials needed to run the workflows and tutorials are hosted for free with open access at Zenodo with a permanent DOI tag.
List of Galaxy Interactive Environments In addition to interactive notebooks, the Galaxy Interactive Environments also boasts a selection of other interactive tools such as the aforementioned cellxgene featured in Fig. 3 , as well as SPARQL, a query language interface; BAM/VCF IOBIO, a file format analysis viewer  [49] ; EtherCalc, a web spreadsheet  [50] ; PHINCH, a metagenome visualizer  [51] ; Wallace, a species modelling platform  [52] ; WILSON, an omics visualizer  [53] ; IDE for materials science; Panoply, a netCDF viewer  [54] ; HiGlass, a Hi-C data visualizer  [55] ; and even an XFCE Virtual Desktop environment  [56].
Reproducible cloud-based analysis The advent of scRNA-seq analysis within the Galaxy framework re-echoes the efforts to standardize the analysis of scRNA-seq, with the promise of presenting reproducible research. The burden of computation on the ever-growing size of the datasets is shifted to the cloud computing resources, and as scRNAseq technology scales, more researchers are likely to migrate towards cloud-based solutions to reap the benefits of superior computing abilities and storage capabilities. Ultimately, the Galaxy framework abstracts the user from the many non-trivial technicalities of the analysis and exposes them to a legible interface of tools from which they can pick and choose.
Longevity and accessibility The community regularly comes together during scheduled code festivals (CoFests) or hackathons to review, contribute, and actively maintain the training materials. The number of community contributions has steadily increased over the past 4 years  [16] , and this growth trend ensures that the Galaxy resources will stay current and adapt to changes in scRNA-seq technology and analysis methods if necessary. The GTN also makes use of language translation tools to provide international interpretations of the training materials to reach a wider more internationally diverse audience.
Acknowledgements We thank the bioinformatics group at the  University of Freiburg  for the development and hosting of the European Galaxy server,  Monika Degen-Hellmuth  at the  Backofen Lab  for her assistance in the organization of the project, the  Institut Francais de Bioinformatique (IFB)  for its support of the ARTbio team,  Charles Girardot  at  EMBL Heidelberg  for his useful feedback, and we also acknowledge the worldwide contributions from users and de-velopers towards the Galaxy Project and all upstream authors and contributors of the software ecosystem that we use and rely on.
Sedimentation regimes on the Great Barrier Reef margin often do not conform to more conventional sequence stratigraphic models, presenting difficulties when attempting to identify key processes that control the margin's geomorphological evolution. By obstructing and modifying down-shelf and down-slope flows, carbonate platforms are thought to play a central role in altering the distribution and morphological presentation of common margin features. Using numerical simulations, we test the role of the carbonate platforms in reproducing several features (i. e. , paleochannels, shelf-confined fluvial sediment mounds, shelf-edge deltas, canyons, and surface gravity flows) that have been described from observational data (seismic sections, multibeam bathymetry, sediment cores, and backscatter imagery).
Our results demonstrate the essential role of carbonate platform topography in modulating key bedload processes. Therefore, they exert direct control on the development of various geomorphological features within the shelf, slope, and basin environments. Plain Language Summary The modern Great Barrier Reef sits atop the skeletal remains of its ancestors. These remains form large (50-200 km 2 ) columns of chalk (or carbonate platforms) that rest on the northeast Australian continental shelf. By comparing observational data with computer simulations, we find that these platforms majorly disrupt and modify the flow of rivers and deep-sea density currents during periods of lower sea level. When platforms are exposed, they become hills, forming steep topographic high points that are large enough to re-route rivers and promote incision on the continental slope.
Overview In this study, we simulate the geomorphological evolution of the Great Barrier Reef margin using pyBadlands  (Salles, Ding, Webster, et al. , 2018) , a code designed to deterministically model sediment transport and landscape evolution on long (millennial to tectonic) timescales. By capturing fluvial processes, turbidity currents, carbonate platform accretion, and wave-driven longshore currents, the code is ideally suited to simulating the principal bedload processes that operate on mixed carbonate-siliciclastic, high-energy margins. Johnson and Searle (1984). Figures  5b-5d  (gray lines) denote the locations of simulated seismic profiles. Figures  6  and 7  delineate the bounds of close-up renderings of 30 m resolution multibeam data  (Webster et al. , 2008). They also delineate the corresponding modeled areas shown in Figures  6  and 7  We note here that while there are many intriguing questions surrounding the behavior of suspended sediments on this margin (see  Dunbar et al.
Discussion A number of geomorphological features are successfully reproduced with pyBadlands when carbonate platforms are present in the initial topographic surface. Within each Discussion section, we will investigate the mechanisms that are responsible for forming them in the model space. We reproduce observations in data sets that both pre-date (i. e. , seismic reflection profiles and slope/basin sediment cores) and post-date (i. e. , seismic reflection profiles, shallow sediment cores, multibeam bathymetry, and backscatter data) the Great Barrier Reef's inception, lending legitimacy to our approximation of a bathymetric surface without carbonate platforms. This allows us to highlight the role of the reef structure in preconditioning and modifying some of these source-to-sink mechanisms.
Given these findings, we infer that the age of the canyons, at least in their current, dendritic-like morphological form, is constrained by the age of the platforms (~600 ka). Nevertheless, the proposed age for these canyons still remains tentative and could be clarified by a more extensive three-dimensional seismic survey. It is possible that canyon incision and maintenance continues during highstands in the central Great Barrier Reef region. This relatively recent activity is indicated both by the presence of carbonate-dominated turbidites at highstand  (Puga-Bernabéu et al. , 2014)  and the presence of sediment waves located within the axes of the canyon heads, which are imaged in multibeam bathymetric data  (Puga-Bernabéu et al. , 2013). Highstand canyons have been described in the cool-water carbonate Gippsland Basin system in southern Australia  (Mitchell et al.
Acknowledgments Thran was supported by the  University of Sydney DBH Scholarship. Salles was supported by both the  ARC Training Centre in Data Analytics for Resources and Environments (DARE)  and the  ARC Research Hub for Basin GEodyNamics and Evolution of SedImentary Systems (GENESIS). Backscatter imagery was collected during a survey on the HMAS Cook, July 1989. Coastal background satellite imagery was provided with the backscatter imagery by the  Australian Centre for Remote Sensing. Inland satellite imagery was provided by  Google  through the QGIS OpenLayers plug-in. The version of pyBadlands used in this study can be found online (zenodo. org/badge/latestdoi/192655024). The seismic boomer profile displayed in Figure 55a was reproduced under a CC BY 4. 0 Creative Commons license (http://creativecommons.
It consists of two parts: the firmware running on the motes and OpenVisualizer, a Python-based application running on a PC. The firmware implements the entire 6TiSCH protocol stack; OpenVisualizer acts as the bridge between the 6TiSCH low-power wireless network and the Internet. OpenWSN has been ported to 10 hardware platforms. In this paper, we present a port of OpenWSN onto SCµM. OpenWSN has very limited requirements for the hardware it runs on. All it needs is a single 32 kHz timer with a single compare register. As detailed in Section 3, SCµM was designed with OpenWSN is mind, and its timer structure is perfectly suited to run the OpenWSN TSCH state machine. The challenge is that SCµM has no stable time reference.
Frequency Synthesis and Clock Calibration The goal of this paper is to show a 6TiSCH network composed of one SCµM and one OpenMote. The challenge is that SCµM does not have an accurate sense of time, and therefore derives its time reference from OpenMote. This section describes how SCµM tunes the frequency it communicates on, and how we calibrate its clocks. We need to give SCµM a rough time reference so it can send frames that OpenMote can receive. The frequency of each of the oscillators is tunable. We designed the code running on the optical programmer board in such a way that, at the end of the bootloading process, the optical programmer repeatedly sends a sequence that causes a OPTICAL_ISR interrupt to be generated on SCµM. This interrupt fires every 100 ms for 2. 5 s. While this is happening, on SCµM, all the clocks are running.
Implementation and Experimental Results The port of OpenWSN on SCµM has a footprint of 54 kB. This includes the full protocol stack and drivers. It takes the optical bootloader 2-3 s to load that code onto SCµM. One of the goals of porting OpenWSN onto SCµM is to show that this platform is perfectly capable of running an off-the-shelf completely standards-based full stack. As a result, we made as little changes as possible to the OpenWSN implementation. There are, however, the following changes that we had to make. First, the port of OpenWSN on SCµM does not come with link-layer security, nor secure joining. This is because this version of SCµM does not come with an AES-128 cipher. Second, we were able to significantly simplify the OpenWSN TSCH state machine thanks to the RFTimer. On all other platforms OpenWSN is ported to, the time used by the TSCH state machine only has one compare register, and cannot automatically trigger radio actions.
Each time the FPGA configures the radio (at most twice per slot), it takes 16 ms for it to completely use the ASC. This is a limitation of the FPGA version only, which the next revision of SCµM will not have. Because of the rounding error in clock division detailed in Section 4, the slot duration of SCµM and OpenMote are slightly different, resulting in apparent relative drift. To measure this, we program the devices to toggle a pin at the beginning of each slot. We connect both pins to a logic analyzer, and have the devices run without communicating (i. e. , without resynchronizing). Figure  8  shows the evolution of the time offset between SCµM and OpenMote, over time. The drift (the slope of the line in Figure  8 ) is 567 ppm. The default guard time of the OpenWSN implementation is 1 ms.
Both mixtures (1. 5 µl) were spotted on a pad of 1% agarose in 1/3 LB and 2/3 phosphate-buffered saline). The pad was covered with a glass coverslip and incubated for 30 min at 30 °C before imaging. Fluorescence microscopy. For live-cell fluorescence microscopy, the same equipment was used as described previously  62, 63  ; a Nikon Ti-E inverted microscope with Perfect Focus System and a Plan Apo 1003 Oil Ph3 DM (NA 1. 4) objective lens, a SPECTRA X light engine (Lumencore), and ET-GFP (Chroma #49002) and ET-mCherry (Chroma #49008) filter sets to excite and filter fluorescence. Exposure time was set to 150 ms and LED powers to 20%. Images were recorded with a sCMOS camera pco. edge 4. 2 (PCO, Germany; 65-nm pixel size) and VisiView software (version 4. 4. 0. 10, Visitron Systems, Germany).
Two biological replicates were used in all experiments. For comparative statistics (Fig. 3b, d ), we used a twosample, two-sided t test, assuming data normality. No adjustments were made for multiple comparisons. To estimate effect sizes, we used Cohen's d measure, d ¼ μ 1 Àμ 2 ð Þ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðSD 2 1 þSD 2 2 Þ=2 p , where μ i and SD i are, respectively, data means and standard deviations for each sample i. All statistical calculations were performed in Matlab ® (version R2017a 9. 2. 0. 556344).
Acknowledgements We are grateful to  Elisa Granato ,  Oliver Meacock , and  Kat Coyte  for their insight and comments. W. P. J. S. , K. R. F. , and L. E. C. are supported by the  National Institutes of Health  (project numbers  R01AI093771  and  R01AI120633 ). K. R. F. is funded by  European Research Council  Grant  787932  and  Wellcome Trust   Investigator award   209397/Z/17/Z. Y. D. is funded by  European Research Council  Grant  803074. This project is supported by a  Templeton World Charity Foundation  grant.
Machine/Deep Learning (ML/DL) techniques have been applied to large data sets in order to extract relevant information and for making predictions. The performance and the outcomes of different ML/DL algorithms may vary depending upon the data sets being used, as well as on the suitability of algorithms to the data and the application domain under consideration. Hence, determining which ML/DL algorithm is most suitable for a specific application domain and its related data sets would be a key advantage. To respond to this need, a comparative analysis of well-known ML/DL techniques, including Multilayer Perceptron, K-Nearest Neighbors, Decision Tree, Random Forest, and Voting Classifier (or the Ensemble Learning Approach) for the prediction of parking space availability has been conducted. This comparison utilized Santander's parking data set, initiated while working on the H2020 WISE-IoT project.
Contribution In this paper, we analyze and evaluate various ML/DL models and determine the best predictive model among them for the parking space availability problem using the parking space data set of Santander, Spain. For comparison, we present different ML/DL-based solutions, including KNN, Random Forest, Multilayer Perceptron (MLP), Decision Tree, and a combined model called Voting Classifier (or Ensemble Learning). Although there are many ML/DL techniques available in the literature, we chose these five ML/DL techniques because they are, firstly, well-known and widely used in the community. Secondly, this is a preliminary work which we plan to extend for experimentation and demonstration of the prediction of parking space availability by integrating it into Santander, Spain's smart parking application for validation and to obtain user feedback.
Since they were dealing with the occupancy rate, while collecting the data they focused on information such as the number of occupied parking spaces. In terms of predicting the parking occupancy rate, Zheng et al. found that the Regression Tree method outperforms the other two algorithms they evaluated. Camero et al. [13]  presented a Recurrent Neural Network (RNN)-based approach to predict the number of free parking spaces. Their main aim was to improve the performance of the RNN. To do so, they introduced a Genetic Algorithm (GA)-based technique and searched for the best configuration for RNN using the GA approach. They utilized the parking data of Birmingham, U. K. , which contains the parking occupancy rate for each parking area given the time and date. Yu et al. [14]  selected the Auto Regressive Integrated Moving Average (ARIMA) model to predict the number of berths available.
ARIMA model is used for making time series forecast. Their experiment was based on a central mall's underground parking and they collected one month data (October 2010). As this is one month data, we believe it might not give clear insight as the parking occupancy pattern can vary in different months. We believe that different factors like public holidays or other kinds of holidays can affect the performance, so one month data might not be enough to have a clear view. Bibi et al. [15]  performed car identification in a parking spot. They collected the video from the camera and divided the parking spots into blocks. Their main contribution is to identify any parking spot it occupied or not using image processing. This processing is being done in real-time and does not provide any future prediction.
However, their approach can be used for data collection. Similarly, Tȃtulea et al. [16]  detected the parking spaces and identified if the parking spots are occupied or available using computer vision techniques and the camera as a sensor. In order to do that, they performed different steps, including Frame Pre-processing, Adaptive Background Subtraction, Metrics & Measurements, History Creation, Results Merging for Final Classification, and Parking Space Status. Again, this work is not about the future prediction of parking spots. In contrast to the above-mentioned works, we deal with the prediction of on-street parking in Santander, a smart city of Spain and our prediction models are based on less complex data features. Moreover, we are targeting individual parking spot's occupancy status and can make future prediction about such spots with a validity period of 10 to 20 min.
Results and Evaluation During this work, the algorithms described in the previous section have been used, fine-tuned, analyzed and compared with respect to the specific goal of the recommendation system: i. e. , to suggest drivers the most probable and closest location to their final destination for a free parking space by looking ahead in a specific time frame (e. g. , 20 min times frame). Data were collected by sensors deployed in a real environment, i. e. , the smart city Santander. In this section, we evaluate the performance of five ML/DL models for the prediction of parking space availability and provide a comparative analysis of the preliminary results, which we plan to extend by integrating them into a smart parking application for Santander, Spain for future experimentation.
Parking Space Data Set The data set for the prediction was obtained by collecting the measurements of sensors deployed in Santander, a smart city in Spain. Almost 400 on-street parking sensors are deployed in the main parking areas of the city center. These parking sensors [22] capture the status (i. e. , occupied or free) of the parking spots. Collected over a 9-month period, this data set was constructed as part of the WISE-IoT  [23] , an H2020 EU-KR project. In WISE-IoT, the parking sensor data was stored in an Next Generation Service Interface (NGSI) context broker  [24]. We accessed real-time Santander data; in the WISE-IoT project, in order to make data more consistent, we created a script that retrieves and stores the on-street parking sensor data every minute. The objective is twofold: to predict the parking spot availability within a time interval (validity) of 10 to 20 min, and to evaluate the prediction accuracy.
• Status: This feature represents the status of a parking space, e. g. , available or occupied. The above-mentioned features were further organized to be input features for our ML/DL model, as given in Table  1. 1  present the 10 or 20 min interval status for any particular parking spot. We collected our data set after every minute; therefore, in order to get the 10 and 20-min status and to provide predictions with 10 and 20 min validity, we used 10 and 20-min windows with 60% and 80% thresholds. For example, if specific parking spot had a 60% availability rate, then, for that 10 or 20-min window, the status of that particular parking spot would be considered available (Free). Similarly, for an 80% threshold, a parking spot would need to have an 80% availability rate in a 10-or 20-min window to be classified "Free".
Training Data Evaluation The size of a training data set can significantly influence the performance of an ML/DL Model. Therefore, in order to further evaluate all five ML/DL models, we performed another comparison, designed to observe how the size of the parking space training data set affects the performance of these models. We chose a subset of the total data set containing 1,252,936 records. We partitioned this data set into five equal folds and set one of the folds as the testing data. Hence, each fold contains 250,587 records. To ensure better observations, we began training our models with a very small number of records: 1000 records. We then added the next 40,000 records and trained the models with 50,000 records. For the third iteration, we trained the models with the 250,587 records of one fold.
We also conducted experiments to observe the affect of training data size on all five of the ML/DL algorithms compared in this paper. We plan to extend our work to (i) demonstrate the efficiency of the Decision Tree model by integrating it into the smart parking application of Santander, Spain and obtain user feedback, and (ii) use the Santander, Spain road traffic data set and offer recommendations for parking spot management based on traffic data. A recommendation system can integrate the prediction functionality by adopting the algorithm that is better aligned and predicts results with the needed precision. On this basis, additional features and functions can be added in order to improve the customer experience. Some features can be devoted to improve and simplify the search for an available parking space; however, in conjunction with the government of the city or considering some pollution related considerations, some novel policies for directing people to the "right" destination could be considered, implemented, and verified in the field.