dataesr/scientific-paragraphs-categorization huggingface dataset:
Step 4 -Create Heatmap Illustrating Customer Origin Density, Weighted by Number of Boardings • Using the dataset as exported above, create a heatmap, weighted by number of boardings, to illustrate where customer origin is most intense. o Heatmaps can only be created using point features. Since postal code addresses are currently stored as a polygon feature, convert the polygon feature class to a point feature class using the "Feature to Point" tool. The points created are referenced to the centroid of each postal code polygon included in the analysis and will still indicate the postal code address of the user and number of boardings associated with this location. o Create a heatmap using the newly created point feature class. Complete this using the "Point Density" tool. Select the "Number of Boardings" field in the "Population" drop down menu to ensure that the heatmap is weighted by the number of boardings associated with each postal code address.
Instructions outlining this process were obtained from the ArcGIS guidebook (ESRI, 2020). These steps are summarized below. • Download a shapefile of the road network within the study area. For the purpose of this study, a shapefile of Ontario's road network was downloaded from the Province of Ontario's geospatial portal (insert citation). o Add "RestrictPedestrians" field (text field) -leave values "null" o Add "ROAD_CLASS" field (short integer field) -leave values "null" o These columns will allow pedestrians to walk on all streets, and will allow the software to configure directions using the road network once the Network Analyst tool is used. • Download and unzip General Transit Feed Specification files o Download General Transit Feed Specification files for the time period in which you want to obtain feeder bus connection quality estimates.
For example, to obtain feeder bus connection quality estimates for the board period spanning January -March 2016, download the GTFS files as indicated in the Jan-Mar 2016 reference paragraph above. The metadata of these files has been checked and confirmed that these files and the transit schedules associated with these transit providers were in-effect between January and March, 2016. o Once downloaded, unzip the GTFS files. • Create a file geodatabase and feature database that the GTFS files can reference. To create a file.
at origin (+), Jobs at destination (+), Origin at a terminus (+), Population at destination (+), BART provided by Metrolinx only specifies the postal code address of the user. The dataset needs to be joined with a postal code baselayer, which delineates all postal code locations and boundaries in the study area, so that it can be georeferenced to the appropriate location. This dataset can be obtained by contacting Canada Post, or in this case was obtained from the University of Waterloo's Geospatial Centre. • Load the postal code boundary shapefile into ArcMap. It is important to note that postal code boundaries are not mutually exclusive. For example, several polygons within the shapefile could represent a single postal code address, as natural and physical breaks in the environment can prevent postal code boundaries from being continuous.
Convert the road network shapefile to a geodatabase. In ArcGIS: o Open the Catalog pane, o Right-click on the folder where you want to store all data / files relevant to the analysis, o Click New > File Geodatabase, o Name the new geodatabase appropriately (ex. Ontario Road Network). o Right-click the Ontario Road Segment shapefile in the table of contents pane, o Select Data -> Export Data, o Under save type, select File and Personal Geodatabase feature class, o Navigate to the geodatabase that was previous created -export the road network shapefile into the new geodatabase o Save the new road network geodatabase appropriately • Add appropriate columns in roads geodatabase. o Open the roads geodatabase.
Open the catalog pane, o Right-click the databases folder -click New File Geodatabase, o browse to location where you want to store the database, enter an appropriate name (ex. GTFS Analysis Jan-Mar2016) and save, • To create a Feature Database: o Right-click on the newly created File Geodatabase, o Click New > Feature Dataset o Select a coordinate system identical to the one that the road network geodatabase is currently referenced too • Place the road network geodatabase into the newly created Feature Database o Load road network geodatabase into ArcGIS o Copy and paste into the Feature Database o Ensure that it is named "Streets" -the GTFS files will not reference the roads baselayer if it is not named "Streets" • Obtain schedule, stop, and route information from previously downloaded GTFS files o Upload GTFS files using GTFS to Network Dataset Transit Sources tool o Select unzipped GTFS feeds that you will use to construct the virtual transit network o Under Target Feature Dataset dropdown menu, select the Feature Database previously created o Run the tool -a virtual Network Dataset will be created • Georeference schedule, stop, and route information from GTFS files to previously uploaded street network geodatabase o Use the Connect Network Dataset Transit Sources To Streets tool o Select the previously created Network Dataset o Reference this file to the Streets file geodatabase previously created • Create virtual transit network o Download the Transit Network Template provided in the ESRI Network Analyst Tutorial o Select the Create Network Dataset From Template tool o Under the input dropdown menu, select the downloaded template o Under the output dropdown menu, select the Feature Dataset previously created o Run the tool • Build the network o Open the Build Network Tool o Select the Feature Dataset previously created • A fully functioning virtual transit network should now be constructed Step 4 -Obtain Travel Time Estimates • Now that a virtual transit network has been constructed, transit travel times between origin and destination pairs can be estimated using the schedule, stop, and route information that has been extracted from the previously downloaded GTFS files • Uploaded the identified points of customer origin as estimated in Step 2 Uploaded a point shapefile of the station location -station locations were obtained from the Metrolinx Open Data Portal • Select the OD-Matrix tool under the Network Analyst toolbox • For origins, select the shapefile containing the identified points of customer origin.
measures of central tendency were also used to track the age of the population throughout the study period. Median age was reported at the dissemination area scale in the 2011 census, whereas average age was reported at this scale in the 2016 census. However, both average and median age were reported at the provincial scale in the 2016 census. These two values were compared, and a difference of 0. 73% was estimated between the two measures. As described in Appendix D all average age values identified in the 2016 census at the dissemination area scale were adjusted by this value to reduce any inaccuracies in the dataset. Additionally, a central measurement of age was not reported in the 2006 census altogether. Values provided from the 2011 census were carried over to 2006 to account for this void.
• Convert the road network shapefile to a geodatabase. In ArcGIS: o Open the Catalog pane, o Right-click on the folder where you want to store all data / files relevant to the analysis, o Click New > File Geodatabase, o Name the new geodatabase appropriately (ex. Ontario Road Network). o Right-click the Ontario Road Segment shapefile in the table of contents pane, o Select Data -> Export Data, o Under save type, select File and Personal Geodatabase feature class, o Navigate to the geodatabase that was previous created -export the road network shapefile into the new geodatabase o Save the new road network geodatabase appropriately • Add appropriate columns in roads geodatabase. o Open the roads geodatabase.
o• Open the catalog pane, o Right-click the databases folder -click New File Geodatabase, o browse to location where you want to store the database, enter an appropriate name (ex. GTFS Analysis Jan-Mar2016) and save, • To create a Feature Database: o Right-click on the newly created File Geodatabase, o Click New > Feature Dataset o Select a coordinate system identical to the one that the road network geodatabase is currently referenced too • Place the road network geodatabase into the newly created Feature Database o Load road network geodatabase into ArcGIS o Copy and paste into the Feature Database o Ensure that it is named "Streets" -the GTFS files will not reference the roads baselayer if it is not named "Streets" • Obtain schedule, stop, and route information from previously downloaded GTFS files o Upload GTFS files using GTFS to Network Dataset Transit Sources tool o Select unzipped GTFS feeds that you will use to construct the virtual transit network o Under Target Feature Dataset dropdown menu, select the Feature Database previously created o Run the tool -a virtual Network Dataset will be created • Georeference schedule, stop, and route information from GTFS files to previously uploaded street network geodatabase o Use the Connect Network Dataset Transit Sources To Streets tool o Select the previously created Network Dataset o Reference this file to the Streets file geodatabase previously created • Create virtual transit network o Download the Transit Network Template provided in the ESRI Network Analyst Tutorial o Select the Create Network Dataset From Template tool o Under the input dropdown menu, select the downloaded template o Under the output dropdown menu, select the Feature Dataset previously created o Run the tool • Build the network o Open the Build Network Tool o Select the Feature Dataset previously created • A fully functioning virtual transit network should now be constructed Step 4 -Obtain Travel Time Estimates • Now that a virtual transit network has been constructed, transit travel times between origin and destination pairs can be estimated using the schedule, stop, and route information that has been extracted from the previously downloaded GTFS files • Uploaded the identified points of customer origin as estimated in Step 2 Uploaded a point shapefile of the station location -station locations were obtained from the Metrolinx Open Data Portal • Select the OD-Matrix tool under the Network Analyst toolbox • For origins, select the shapefile containing the identified points of customer origin.
However, they also create a level of indirection which introduces additional delay (which may be negligible with only a few milliseconds in local networks, or significant with several tens or even hundreds of milliseconds over the internet or wireless networks). Finally, recording of the exchanged data requires distinct solutions as well. High-performance, high-reliability databases form an essential requirement for many modern businesses. Thanks to this demand, a large body of established techniques exists, from which users can select the right tool for their specific needs. Binary medical data (images, videos, etc. ) can be stored on premise in modern PACS systems, which provide extensive support for data annotation, storage and exchange. For clinical metadata, the selection of technology typically depends on the level of standardization of the recorded data.
Highly standardized data can possibly be stored directly through interfaces of e. g. the IHE family of standards. If the target data are not standardized, but homogeneous, then a database model for classical database languages (e. g. Structured Query Language (SQL)) may be suitable. Use cases where a wide array of highly heterogeneous data is recorded may choose modern NoSQL databases. These databases do not (or not exclusively) rely on classical tabular data models, but instead allow the storage and querying of tree-like structures. The JavaScript Object Notation (JSON) Format is a popular choice for NoSQL databases for its wide support in toolkits and the immediate applicability with regard to Representational State Transfer (REST)-APIs. While initially applications of these databases were geared towards data lakes because of the relative ease of application, NoSQL databases have recently seen widespread application in big data and ML  (Dasgupta, 2018).
Key initiatives and achievements One of the most successful initiatives fostering access to open data sets is Grand Challenge which provides infrastructure and tools for organizing challenges in the context of biomedical image analysis. The platform hosts several challenges including data sets and also serves as a framework for end-to-end development of ML solutions. Notably, the Endoscopic Vision Challenge EndoVis, an initiative that takes place at the international conference hosted by the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society, is the largest source of SDS data collections  (Bernal et al. , 2017 ; EndoVis'15 Instrument Subchallenge Dataset; EndoVis-GIANA;  Allan et al. , 2019; Hattab et al. , 2020; ALHAJJ et al. , 2021; Allan et al. , 2020; Maier-Hein et al.
, 2021; Allan et al. , 2021; EndoVis-Workflow and Skill; Roß et al. , 2021b; Zia et al. , 2021; Huaulmé et al. , 2021; HeiSurf; GIANA21; CholecTriplet21; FetReg; PETRAW; SimSurgSkill). It consists of several sub-challenges every year which support the availability of new public data sets for developing and benchmarking methods. Generally speaking, however, quality control in biomedical challenges and data sharing is still an issue  (Maier-Hein et al. , 2018b , 2020). The importance of public data sets in general is illustrated through new journals dedicated to only publishing high quality data sets, such as Nature Scientific Data. An important contribution in this context are the FAIR data principles  (Wilkinson et al. , 2016) , already introduced in the context statement above. Recently, the Journal of the American Medical Association (JAMA) Surgery partnered with the Surgical Outcomes Club and launched a series consisting of statistical methodology articles and a checklist that aims to elevate the science of surgical database research  (Haider et al.
, 2018). It also includes an overview of the most prominent surgical registries and databases, e. g. the National Cancer Database  (Merkow et al. , 2018) , the National Trauma Data Bank  (Hashmi et al. , 2018)  or the National Surgical Quality Improvement Program  (Raval and Pawlik, 2018). Annotation of data sets requires consistent ontologies for SDS. The OntoSPM project  (Gibaud et al. , 2014)  is the first initiative whose goal is to focus on the modeling of the entities of surgical process models, as well as the derivation LapOntoSPM  (Katić et al. , 2016a)  for laparoscopic surgery. OntoSPM is now organized as a collaborative action associating a dozen research institutions in Europe, with the primary goal of specifying a core ontology of surgical processes, thus gathering the basic vocabulary to describe surgical actions, instruments, actors, and their roles.
, 2021)  showed that knowledge of confounding variables is crucial to the development of successful predictive models. Conversely, a striking recent example of a confounder rendering results meaningless can be seen in the many papers using a particular pneumonia dataset as a control group in the development of COVID-19 detection and prognostication models. Since this dataset solely consists of young paediatric patients, any model using adult COVID-19 patients and these patients as a control group would likely overperform merely by detecting children  (Roberts et al. , 2021). Other examples of confounders (also called hidden variables) are chest drains and skin markings in the context of melanoma  (Winkler et al. , 2019)  and pneumothorax diagnosis  (Oakden-Rayner et al. , 2020). Recognizing and minimizing potential biases in SDS by enhancing data sets with, for example, relevant metadata is thus of eminent importance.
Recent advances in federated learning could reduce security and privacy concerns since they rely on sharing machine learning models rather than the data itself  (Kaissis et al. , 2020 ) (see Sec. 3). A complementary strategy for bypassing current hurdles related to data sharing is data donation. Medical Data Donors e. V. , for example, is a registered German non-profit organization, designed to build a large annotated image database which will serve as a basis for medical research. It can be supported by the public via donation of medical imaging data or by shopping at Amazon Smile. In the broader context of data donation, the SDS initiative discussed the concept of a data donation card in analogy to the existing organ donation card. With such a card, patients could explicitly state which kind of data they are willing to share with whom and under which circumstances.
Overall, making progress on large public databases will require establishing an interlocking set of standards, technical methods, and data analysis tools tied to metrics to support reproducible SDS  (Nichols et al. , 2017)  and provide value for the community. Clinical registries provide a good example of such a mechanism. In a registry, a specific area of practice agrees on data to be shared, outcome measures to be assessed, and standardized formats as well as quality measures for the data  (Arts et al. , 2002). Identifying areas of SDS where the value proposition exists to drive the use of registries would provide much-needed impetus to create data archives. So would creating more monetary and non-monetary incentives for institutions, clinical staff and patients to share and annotate data, although particularly the issue of incentivizing patients to share data presents an ethical gray area.
, relating overall operating room efficiency and utilization. Business Intelligence (BI)  (Chen et al. , 2012)  tools are a typical form of descriptive analysis tools which comprise an integrated set of IT tools to transform data into information and then into knowledge, and have been used in healthcare settings  (Ward et al. , 2014 ) (e. g. Sisense™ (Sisense Ltd. , New York City, NY, USA), Domo™ (Domo, Inc. , American Fork, UT, USA), MicroS-trategy™ (MicroStrategy Inc. , Tysons Corner, VA, USA), Looker™ (Looker Data Sciences Inc. , Santa Cruz, CA, USA), Microsoft Power BI™ (Microsoft Corporation, Redmond, WA, USA) and Tableau™ (Tableau Software Inc. , Seattle, WA, USA)). These tools often incorporate features such as interactive dashboards  (Upton, 2019)  that provide customized graphical displays of key metrics, historical trends, and reference benchmarks and can be used to assist in tasks such as surgical planning, personalized treatment, and postoperative data analysis.
In this section, we focus on ML frameworks and the regulatory aspects of software development for SDS. ML frameworks and model standards: ML is today one of the central themes of SDS analytics, and many frameworks are used by the SDS community. The scikit-learn library in Python is the most widely used framework for MLbased classification, regression and clustering using non-DL models such as Support Vector Machines (SVMs), decision trees and multi-layer perceptron (MLPs). DL, the sub-field of ML that uses Artificial Neural Networks (ANNs) with many hidden layers, has exploded over the past 5 years, also due to the mature DL frameworks. The dominating open-source frameworks today are TensorFlow by Google and PyTorch by Facebook (Menlo Park, CA, USA). These provide mechanisms to construct, train and test ANNs with comprehensive and ever-growing APIs and they are backed up by large industrial investment and community involvement.
Other important, but less widely used frameworks include Caffe, Caffe2 (now a part of PyTorch), Apache MXNet, Flux, Chainer, MATLAB's Deep Learning Toolbox and Microsoft's CNTK. Wrapper libraries have been constructed on top of several frameworks with higher level APIs that simplify DL model design and promote reusable components. These include Ten-sorFlow's Keras (now native to TensorFlow), TensorLayer, TFLearn and NiftyNet (specifically for medical image data), and PyTorch's TorchVision  (Nguyen et al. , 2019). Other useful tools include training progress visualization with Tensorboard, and AutoML systems for efficient automatic hyperparameter and model architecture search, such as H2O, autosklearn, AutoKeras and Google Cloud AutoML. NVIDIA DIGITS takes framework abstraction a step further with a web application to train DL models for image classification, segmentation and object detection, and a graphical user interface (GUI) suitable for non-programmers.
Such tools are relevant in SDS where clinical researchers can increasingly train standard DL models without any programming or ML experience  (Faes et al. , 2019). On the one hand this is beneficial for technology democratization, but on the other hand it elevates known risks of treating ML and DL systems as "black boxes"  (PHG Foundation, 2020). Recently NVIDIA has released NVIDIA Clara, a software infrastructure to develop DL models specifically for healthcare applications with large-scale collaboration and federated learning. Each major framework has its own format for representing and storing ML models and associated computation graphs. There are now efforts to standardize formats to improve interoperability, model sharing, and to reduce framework lockin. Examples include the Neural Network Exchange Format (NNEF), developed by the Khronos Group with participation from over 30 industrial partners, Open Neural Network Exchange (ONNX) and Apple's (Cupertino, CA, USA) Core ML for sharing models, and for sharing source code to train and test these models.
g. (Ross et al. , 2018) ) and semisupervised learning (see e. g. (Yu et al. , 2019; Srivastav et al. , 2020) ). Self-supervised methods solve an alternate, pretext or auxiliary task, the result of which is a model or representation that can be used in the solution of the original problem. Semisupervised methods can exploit the unlabelled data in many different ways. In  (Yu et al. , 2019; Srivastav et al. , 2020) , for example, pseudo-annotations are generated on the unlabelled data using a teacher model, and the resulting pseudoannotated dataset is then used to train another (student) model. Recent studies have further shown that exploiting the relationship across different tasks with the concept of multi-task learning  (Twinanda et al. , 2017 ) may be used to address data sparsity as well.
Currently, the overwhelming focus for SDS researchers is in the prescriptive technology area -for example on tools that provide surgical decision support or predict adverse events. Changing the development lens from prescriptive to descriptive SDS applications, however, may open up entirely new avenues. For instance, a low-hanging fruit may lie in a descriptive decision support tool that informs surgeons on how many surgeons performed certain steps within an intervention and the consequences. Such a data-centric SDS product would not require embedded surgical expertise in order to provide value to the surgeon, but only a database of surgical videos and automated recognition of anatomical structures and surgical instruments, which is technically feasible. In essence, instead of the very difficult automation of surgical decisions, value can be found in providing surgeons and surgical teams with moment-to-moment risk stratification data to facilitate their decisions.