Workflow Features Research Articles

Fully Automatic Geometric Registration Framework of UAV Imagery Based on Online Map Services and POS

Unmanned aerial vehicle (UAV) remote sensing has found extensive applications in various fields due to its ability to quickly provide remote sensing imagery, and the rapid, even automated, geometric registration of these images is an important component of their time efficiency. While current geometric registration methods based on image matching are well developed, there is still room for improvement in terms of time efficiency due to the presence of the following factors: (1) difficulty in accessing historical reference images and (2) inconsistencies in data sources, scales, and orientations between UAV imagery and reference images, which leads to unreliable matching. To further improve the time efficiency of UAV remote sensing, this study proposes a fully automatic geometric registration framework. The workflow features the following aspects: (1) automatic reference image acquisition by using online map services; (2) automatic ground range and resolution estimation using positional and orientation system (POS) data; (3) automatic orientation alignment using POS data. Experimental validation demonstrates that the proposed framework is able to carry out the fully automatic geometric registration of UAV imagery, thus improving the time efficiency of UAV remote sensing.

A workflow for human-centered machine-assisted hypothesis generation: Commentary on Banker et al. (2024).

Large language models (LLMs) have the potential to revolutionize a key aspect of the scientific process-hypothesis generation. Banker et al. (2024) investigate how GPT-3 and GPT-4 can be used to generate novel hypotheses useful for social psychologists. Although timely, we argue that their approach overlooks the limitations of both humans and LLMs and does not incorporate crucial information on the inquiring researcher's inner world (e.g., values, goals) and outer world (e.g., existing literature) into the hypothesis generation process. Instead, we propose a human-centered workflow (Hope et al., 2023) that recognizes the limitations and capabilities of both the researchers and LLMs. Our workflow features a process of iterative engagement between researchers and GPT-4 that augments-rather than displaces-each researcher's unique role in the hypothesis generation process. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A New Method for Numerical Simulation of Coalbed Methane Pilot Horizontal Wells—Taking the Bowen Basin C Pilot Area in Australia as an Example

Coalbed methane (CBM) pilot wells typically exhibit a short production period, necessitating evaluation of their estimated ultimate recovery (EUR) through numerical simulation. Utilizing limited geological data from the pilot areas to finish history matching and subsequent production forecasting presents substantial challenges. This paper introduces a comprehensive numerical simulation workflow for CBM pilot wells, encompassing the following steps. Initially, geological parameters are categorized into two groups based on their statistical distribution trends: trend parameters (i.e., gas content, permeability, Langmuir volume, and Langmuir pressure) and non-trend parameters (i.e., fracture porosity, gas–water relative permeability, and rock compressibility). The probability method is employed to ascertain the probable high and low limits for trend parameter distributions, while empirical or analogous methods are applied to define the boundaries for non-trend parameters. Subsequently, the parameter sensitivity analysis is conducted to understand the influence of varying parameters on cumulative gas and water production. Conclusively, experimental design algorithms generate over 100 simulation cases using the identified sensitive parameters, from which the top ten optimal cases are chosen for EUR prediction. This workflow features two technological innovations: (1) considering the most comprehensive set of reservoir parameters for uncertainty and sensitivity analyses, and (2) considering the matching accuracy of both cumulative production and dynamic production trends when selecting optimal matching cases. This approach was successfully implemented in the C pilot area of the Bowen Basin, Australia. In addition, it offers valuable insights for numerical simulation of unconventional natural gases, such as shale gas.

Nf-core/nanostring: a pipeline for reproducible NanoString nCounter analysis.

The NanoString™ nCounter® technology platform is a widely used targeted quantification platform for the analysis of gene expression of up to ∼800 genes. Whereas the software tools by the manufacturer can perform the analysis in an interactive and GUI driven approach, there is no portable and user-friendly workflow available that can be used to perform reproducible analysis of multiple samples simultaneously in a scalable fashion on different computing infrastructures. Here, we present the nf-core/nanostring open-source pipeline to perform a comprehensive analysis including quality control and additional features such as expression visualization, annotation with additional metadata and input creation for differential gene expression analysis. The workflow features an easy installation, comprehensive documentation, open-source code with the possibility for further extensions, a strong portability across multiple computing environments and detailed quality metrics reporting covering all parts of the pipeline. nf-core/nanostring has been implemented in the Nextflow workflow language and supports Docker, Singularity, Podman container technologies as well as Conda environments, enabling easy deployment on any Nextflow supported compatible system, including most widely used cloud computing environments such as Google GCP or Amazon AWS. The source code, documentation and installation instructions as well as results for continuous tests are freely available at https://github.com/nf-core/nanostring and https://nf-co.re/nanostring.

Trapped ion mobility mass spectrometry of new psychoactive substances: Isomer-specific identification of ring-substituted cathinones

New psychoactive substances (NPS) are synthetic derivatives of illicit drugs designed to mimic their psychoactive effects. NPS are typically not controlled under drug acts or their legal status depends on their molecular structure. Discriminating isomeric forms of NPS is therefore crucial for forensic laboratories. In this study, a trapped ion mobility spectrometry time-of-flight mass spectrometry (TIMS-TOFMS) approach was developed for the identification of ring-positional isomers of synthetic cathinones, a class of compounds representing two-third of all NPS seized in Europe in 2020. The optimized workflow features narrow ion-trapping regions, mobility calibration by internal reference, and a dedicated data-analysis tool, allowing for accurate relative ion-mobility assessment and high-confidence isomer identification. Ortho-, meta- and para-isomers of methylmethcathinone (MMC) and bicyclic ring isomers of methylone were assigned based on their specific ion mobilities within 5 min, including sample preparation and data analysis. The resolution of two distinct protomers per cathinone isomer added to the confidence in identification. The developed approach was successfully applied to the unambiguous assignment of MMC isomers in confiscated street samples. These findings demonstrate the potential of TIMS-TOFMS for forensic case work requiring fast and highly-confident assignment cathinone-drug isomers in confiscated samples.

Non-regulated LC-MS/MS bioanalysis in support of early drug development: a Novartis perspective.

Scientifically qualified LC-MS/MS methods are essential for the determination of small molecule drug candidates and/or their metabolite(s) in support of various non-regulated safety assessment and in vivo absorption, distribution, metabolism and excretion studies in preclinical development. This articleoutlines an effective method development workflow to fit for this purpose. The workflow features a 'universal' protein precipitation solvent for efficient sample extraction, a mobile phase additive for managing chromatographic resolution and addressing carryoverand an internal standard cocktail to select the best analogue internal standard to track the analyte of interest in LC-MS/MS. In addition, good practices arerecommended to prevent bioanalytical pitfalls due to instability, non-specific bindingand dosing vehicle-induced matrix effect. Proper handling of non-liquid matrix isalso discussed.

Integrated and automated high-throughput purification of libraries on microscale.

We herein report the development of an automation platform for rapid purification and quantification of chemical libraries including reformatting of chemical matter to 10mM DMSO stock solutions. This fully integrated workflow features tailored conditions for preparative reversed-phase (RP) HPLC-MS on microscale based on analytical data, online fraction QC and CAD-based quantification as well as automated reformatting to enable rapid purification of chemical libraries. This automated workflow is entirely solution-based, eliminating the need to weigh or handle solids. This increases process efficiency and creates a link between high-throughput synthesis and profiling of novel chemical matter with respect to biological and physicochemical properties in relevant assays.

Machine Learning Based Prediction of Squamous Cell Carcinoma in Ex Vivo Confocal Laser Scanning Microscopy.

Simple SummarySquamous cell carcinoma is the second most common type of skin cancer, with incidence rates rising each year. Micrographic urgery is the treatment of choice for large, aggressive, or recurrent lesions. To ensure complete removal, excised tissue is frozen or embedded in paraffin, cut by a microtome, and stained for examination by an expert Mohs surgeon or a dermatopathologist. Thus, resection of tumor is performed in multiple steps, resulting in delayed wound closure, patient discomfort, longer hospital stay, and in turn, higher healthcare costs. In the last few years, ex vivo confocal laser scanning microscopy (CLSM) has shown promising results in intraoperative almost-real-time detection of skin cancers. This technology is not yet widespread in part due to necessity of training for laboratory technicians, surgeon and dermatopathologists. To increase efficiency and objectivity in the image interpretation process, we have built a machine learning model to detect squamous cell carcinoma lesions in excised tissues.Image classification with convolutional neural networks (CNN) offers an unprecedented opportunity to medical imaging. Regulatory agencies in the USA and Europe have already cleared numerous deep learning/machine learning based medical devices and algorithms. While the field of radiology is on the forefront of artificial intelligence (AI) revolution, conventional pathology, which commonly relies on examination of tissue samples on a glass slide, is falling behind in leveraging this technology. On the other hand, ex vivo confocal laser scanning microscopy (ex vivo CLSM), owing to its digital workflow features, has a high potential to benefit from integrating AI tools into the assessment and decision-making process. Aim of this work was to explore a preliminary application of CNN in digitally stained ex vivo CLSM images of cutaneous squamous cell carcinoma (cSCC) for automated detection of tumor tissue. Thirty-four freshly excised tissue samples were prospectively collected and examined immediately after resection. After the histologically confirmed ex vivo CLSM diagnosis, the tumor tissue was annotated for segmentation by experts, in order to train the MobileNet CNN. The model was then trained and evaluated using cross validation. The overall sensitivity and specificity of the deep neural network for detecting cSCC and tumor free areas on ex vivo CLSM slides compared to expert evaluation were 0.76 and 0.91, respectively. The area under the ROC curve was equal to 0.90 and the area under the precision-recall curve was 0.85. The results demonstrate a high potential of deep learning models to detect cSCC regions on digitally stained ex vivo CLSM slides and to distinguish them from tumor-free skin.

A hyper-heuristic selector algorithm for cloud computing scheduling based on workflow features

This study focuses on the presentation of a new algorithm for scheduling workflows on heterogeneous distributed systems such as cloud computing. Since heterogeneous distributed systems deal with different types of resources, scheduling of applications on cloud resources plays an important role in the computing environment. Due to being heterogeneous and dynamic properties of resources as well as large numbers of tasks with different characteristics and dependencies among tasks, scheduling tasks on cloud computing is referred to as an NP-hard problem. Heuristic methods are one of the common approaches to solve this problem. Heuristic algorithms according to the specifications of resources and workflow structure could be superior to the rule-based methods. However, it is difficult to define which heuristic algorithm is performed better than the rest. Therefore, the choice of appropriate heuristic algorithms based on the circumstances can be effective. Moreover, the hyper-heuristic algorithm obtains higher performance. In this study, a new method is presented to improve the Hyper-Heuristic Scheduling Algorithm for the cloud using the decision tree method to select a convenient heuristic algorithm based on the characteristics of resources and workflows by considering evaluation criteria such as cost and Makespan. Finally, the presented algorithm is evaluated by Workflowsim and using RapidMiner. The simulation results demonstrate that our proposed algorithm outperforms existing approaches in terms of Makespan and Accuracy.

Method Optimisation in Hydrophilic-Interaction Liquid Chromatography by Design of Experiments Combined with Quantitative Structure–Retention Relationships

Accurate prediction of the separation conditions for a set of target analytes with no retention data available is fundamental for routine analytical assays but remains a very challenging task. In this paper, a quality by design (QbD) optimisation workflow capable of discovering the optimal chromatographic conditions for separation of new compounds in hydrophilic-interaction liquid chromatography (HILIC) is introduced. This workflow features the application of quantitative structure−retention relationship (QSRR) methodology in conjunction with design of experiments (DoE) principles and was used to carry out a two-level full factorial DoE optimisation for a mixture of pharmaceutical analytes on zwitterionic, amide, amine, and bare silica HILIC stationary phases, with mobile phases containing varying acetonitrile content, mobile phase pH, and salt concentration. A dual-filtering approach that considers both retention time (tR) and structural similarity was used to identify the optimal set of analytes to train the QSRR in order to maximise prediction accuracy. Highly predictive retention models (average R2 of 0.98) were obtained and statistical analysis of the prediction performance of the QSRR models demonstrated their ability to predict the retention times of new compounds based solely on their molecular structures, with root-mean-square errors of prediction in the range 7.6–11.0 %. Further, the obtained retention data for pharmaceutical test compounds were used to compute their separation selectivity, which was used as input into a DoE optimiser in order to select the optimal separation conditions. Experimental separations performed under the chosen optimal working conditions showed good agreement with the theoretical predictions. To the best of our knowledge, this is the first study of a QbD optimisation workflow assisted with dual-filtering-based retention modelling to facilitate the method development process in HILIC.

0715 Long-term CPAP Adherence In A Public Sleep Clinic

Abstract Introduction CPAP adherence may drop substantially over a long time frame. Since minorities including Hispanics and low socioeconomic groups have lower short-term acceptance and adherence, long-term adherence may also be reduced. We adapted a brief motivational enhancement education program (BMEEP) (Lai, CHEST 2014) in a Los Angeles County safety-net sleep clinic and found improved CPAP adherence at 3 months. We now report longitudinal long-term adherence over >=2 years. Our hypothesis: Many patients would meet CMS adherence criteria over >= 2 years. Methods During 3/1/2016 - 4/1/17, 118 patients completed BMEEP during CPAP initiation and were scheduled for a reinforcement session and routine clinic visits at 1 and 3 months and every 6 months thereafter. Electronic adherence and efficacy data were accessed each visit. Multivariate regression analyses explored association of adherence variables with demographic, clinical and workflow features. Results Baseline characteristics (mean (SD) or percent): Age 57.2 (17.8); Women 44.9%; Hispanic 69.5%, Non-Hispanic-White 22.9%, Other 5%, Black 3%; BMI 37; Epworth score 10.4 (6.05). Home sleep testing (69 patients) respiratory event indices were >=15/hr in 67% and >=30/hr in 57%. Polysomnography (81 patients) apnea hypopnea indices were (AHI) >= 15 in 78% and >=30/hr in 43%. By 7/1/2019, 23 (19%) patients were lost with unknown CPAP use status, while 76 (70.5%) of remaining patients continued to use CPAP. At 2 years, CPAP adherence parameters included: Average nightly use, 75% (27.7); Average hours/night, 4.74 (2.5); Average nightly use >=4 hours, 65.2% (31.6); >=70% nightly use >= 4 hours, 35 (51.5%). Average residual AHI was 2.05 (1.69). Adherence parameters in individual patients remained similar throughout 2 years. Long-term adherence was not associated with the demographic, clinical or workflow variables tested. Conclusion Programs that educate, motivate and provide regular follow-up for predominantly Hispanic low income populations can achieve acceptable long-term CPAP adherence rates. Support None

Assessment of Workflow Feature Selection on Forest LAI Prediction with Sentinel-2A MSI, Landsat 7 ETM+ and Landsat 8 OLI.

The European Space Agency (ESA)’s Sentinel-2A (S2A) mission is providing time series that allow the characterisation of dynamic vegetation, especially when combined with the National Aeronautics and Space Administration (NASA)/United States Geological Survey (USGS) Landsat 7 (L7) and Landsat 8 (L8) missions. Hybrid retrieval workflows combining non-parametric Machine Learning Regression Algorithms (MLRAs) and vegetation Radiative Transfer Models (RTMs) were proposed as fast and accurate methods to infer biophysical parameters such as Leaf Area Index (LAI) from these data streams. However, the exact design of optimal retrieval workflows is rarely discussed. In this study, the impact of five retrieval workflow features on LAI prediction performance of MultiSpectral Instrument (MSI), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) observations was analysed over a Dutch beech forest site for a one-year period. The retrieval workflow features were the (1) addition of prior knowledge of leaf chemistry (two alternatives), (2) the choice of RTM (two alternatives), (3) the addition of Gaussian noise to RTM produced training data (four and five alternatives), (4) possibility of using Sun Zenith Angle (SZA) as an additional MLRA training feature (two alternatives), and (5) the choice of MLRA (six alternatives). The features were varied in a full grid resulting in 960 inversion models in order to find the overall impact on performance as well as possible interactions among the features. A combination of a Terrestrial Laser Scanning (TLS) time series with litter-trap derived LAI served as independent validation. The addition of absolute noise had the most significant impact on prediction performance. It improved the median prediction Root Mean Square Error (RMSE) by 1.08 m2 m−2 when 5 % noise was added compared to inversions with 0 % absolute noise. The choice of the MLRA was second most important in terms of median prediction performance, which differed by 0.52 m2 m−2 between the best and worst model. The best inversion model achieved an RMSE of 0.91 m2 m−2 and explained 84.9% of the variance of the reference time series. The results underline the need to explicitly describe the used noise model in future studies. Similar studies should be conducted in other study areas, both forest and crop systems, in order to test the noise model as an integral part of hybrid retrieval workflows.

Developing a fabrication workflow for irregular sawlogs

In this article, we suggest using contemporary manufacturing technologies to integrate material properties with architectural design tools, revealing new possibilities for the use of wood in architecture. Through an investigative approach, material capacities and fabrication methods are explored and combined towards establishing new workflows and architectural expressions, where material, fabrication and result are closely interlinked. The experimentation revolves around discarded, crooked oak logs, doomed to be used as firewood due to their irregularity. This project treats their diverging shapes differently by offering unique processing to each log informed by its particularities. We suggest here a way to use the natural forms and properties of sawlogs to generate new structures and spatial conditions. In this article, we discuss the scope of this approach and provide an example of a workflow for handling the discrete shapes of natural sawlogs in a system that involve the collection of material, scanning/digitisation, handling of a stockpile, computer analysis, design and robotic manufacturing. The creation of this specific method comes from a combination of investigation of wood as a material, review of existing research in the field, studies of the production lines in the current wood industry and experimentation through our in-house laboratory facilities. As such, the workflow features several solutions for handling the complex and different shapes and data of natural wood logs in a highly digitised machining and fabrication environment. This up-cycling of discarded wood supply establishes a non-standard workflow that utilises non-standard material stock and leads to a critical articulation of today’s linear material economy. The project becomes part of an ambition to reach sustainable development goals and technological innovation in global and resource-intensive architecture and building industry.

Development of a drug-drug interaction screening tool for oncology clinical trial enrollment.

315 Background: Screening drug-drug interactions (DDI) for subjects enrolling in oncology clinical trials is critical to ensuring patient safety and the validity of clinical trial data. We previously reported that DDI screening is not uniformly conducted when screening patients for enrollment into SWOG clinical trials and found that at the University of Michigan Rogel Cancer Center up to 24.2% of subjects enrolled in National Clinical Trial Network (NCTN) trials had a DDI. Screening tools aid in DDI reduction in clinical practice, but none have been created for clinical trial enrollment. Our objective was to develop a clinical trial specific DDI screening tool based on features requested by the end-users of the tool at U-M. Methods: Semi-structured and informal interviews were conducted with all data managers who enroll patients into NCTN clinical trials at the U-M cancer center. Data managers were asked about their current workflow and desired features of a DDI screening tool. Responses were combined and reviewed for feasibility. Desired features were conveyed to PEPID, LLC (Phoenix, AZ) for tool development. Results: Four data managers were interviewed. Protocol-guided screening was a key workflow feature, which was completed by gathering DDI information primarily from the exclusion criteria and drug information sections of each respective protocol, Google, CredibleMeds, and the Indiana University P450 Drug Interaction Table. Consequently, a critical feature was the display of drug characteristics with wording that aligned with that in the protocol including transporter and CYP450 substrates, inhibitors, or inducers and QT prolongation potential. Additional desirable features included separate entry of study and concomitant drugs, filtering to display only DDI with study drugs, and PDF export of results. PEPID developed a prototype tool including these desired attributes for a prospective implementation pilot study. Conclusions: A first generation clinical trial specific DDI screening tool was developed based on end-user feedback. We are designing a prospective study to determine whether implementation of this tool can reduce DDI, enhance patient safety, and ensure validity of clinical trial data.

Validation of an automated ultraperformance liquid chromatography IgG N-glycan analytical method applicable to classical galactosaemia.

Background Classical galactosaemia (OMIM #230400) is a rare disorder of carbohydrate metabolism caused by deficiency of the galactose-1-phosphate uridyltransferase enzyme. The pathophysiology of the long-term complications, mainly cognitive, neurological and female fertility problems, remains poorly understood. Current clinical methods of biochemical monitoring lack precision and individualization with an identified need for improved biomarkers for this condition. Methods We report the development and detailed validation of an automated ultraperformance liquid chromatography N-glycan analytical method of high peak resolution applied to galactose incorporation into human serum IgG. Samples are prepared on 96-well plates and the workflow features rapid glycoprotein denaturation, enzymatic glycan release, glycan purification on solid-supported hydrazide, fluorescent labelling and post-labelling clean-up with solid-phase extraction. Results This method is shown to be accurate and precise with repeatability (cumulative coefficients of variation) of 2.0 and 8.5%, respectively, for G0/G1 and G0/G2 ratios. Both serum and processed N-glycan samples were found to be stable at room temperature and in freeze-thaw experiments. Conclusions This high-throughput method of IgG galactose incorporation is robust, affordable and simple. This method is validated with the potential to apply as a biomarker for treatment outcomes for galactosaemia.

Learning Environment to Support the Product Development Process

The competitive global market creates challenges like shortened product life cycles and high level of customization with lower costs. Therefore, it is necessary to make investments to achieve competitive gains. In this scenario, qualification of human resources is a factor of success in the global competition. To fulfill this requirement, a new way of learning that integrates education in a real factory environment was developed. This concept is called Learning Factories. It brings an environment that encourages the experimental learning, simulating the key features of a real production environment in an academic setting. This teaching strategy reduces the gap between the knowledge (University) and the action (industry). This paper describes a learning factory testbed environment supporting the product development process, which was implemented at the Methodist University of Piracicaba, Brazil. This new learning environment simulates a real product development process for undergraduate and postgraduate programs. The application of Learning Factory shows in practice the tools applied in the product development process and involves design teams, product management, engineering changes request and, during the entire process, the communication and workflow features of the product data management tools used in the implementation of the environment.

Shotgun and Targeted Plasma Proteomics to Predict Prognosis of Non-Small Cell Lung Cancer.

Lung cancer is the leading cause of cancer deaths worldwide. Clinically, the treatment of non-small cell lung cancer (NSCLC) can be improved by the early detection and risk screening among population. To meet this need, here we describe in detail a shotgun following the targeted proteomics workflow that we previously applied for human plasma analysis, which involves (1) the application of extensive peptide-level fractionation coupled with label-free quantitative proteomics for the discovery of plasma biomarker candidates for lung cancer and (2) the usage of the multiple reaction monitoring (MRM) assays for the follow-up validations in the verification phase. The workflow features simplicity, low cost, high transferability, high robustness, and flexibility with specific instrumental settings.

High-Throughput Analysis of the Plasma N-Glycome by UHPLC.

The understanding of glycosylation alterations in health and disease has evolved significantly and glycans are considered to be relevant biomarker candidates. High-throughput analytical technologies capable of generating high-quality, large-scale glycoprofiling data are in high demand. Here, we describe an automated sample preparation workflow and analysis of N-linked glycans from plasma samples using hydrophilic interaction liquid chromatography with fluorescence detection on an ultrahigh-performance liquid chromatography (UHPLC) instrument. Samples are prepared in 96-well plates and the workflow features rapid glycoprotein denaturation, enzymatic glycan release, glycan purification on solid-supported hydrazide, fluorescent labeling, and post-labeling cleanup with solid-phase extraction. The development of a novel approach for plasma N-glycan analysis and its implementation on a robotic platform significantly reduces the time required for sample preparation and minimizes technical variation. It is anticipated that the developed method will contribute to expanding high-throughput capabilities to analyze protein glycosylation.

Comprehensive Profiling of Glycosphingolipid Glycans Using a Novel Broad Specificity Endoglycoceramidase in a High-Throughput Workflow.

The biological function of glycosphingolipids (GSLs) is largely determined by their glycan headgroup moiety. This has placed a renewed emphasis on detailed GSL headgroup structural analysis. Comprehensive profiling of GSL headgroups in biological samples requires the use of endoglycoceramidases with broad substrate specificity and a robust workflow that enables their high-throughput analysis. We present here the first high-throughput glyco-analytical platform for GSL headgroup profiling. The workflow features enzymatic release of GSL glycans with a novel broad-specificity endoglycoceramidase I (EGCase I) from Rhodococcus triatomea, selective glycan capture on hydrazide beads on a robotics platform, 2AB-fluorescent glycan labeling, and analysis by UPLC-HILIC-FLD. R. triatomea EGCase I displayed a wider specificity than known EGCases and was able to efficiently hydrolyze gangliosides, globosides, (n)Lc-type GSLs, and cerebrosides. Our workflow was validated on purified GSL standard lipids and was applied to the characterization of GSLs extracted from several mammalian cell lines and human serum. This study should facilitate the analytical workflow in functional glycomics studies and biomarker discovery.

MoocRP: Enabling Open Learning Analytics with an Open Source Platform for Data Distribution, Analysis, and Visualization

In this paper, we address issues of transparency, modularity, and privacy with the introduction of an open source, web-based data repository and analysis tool tailored to the Massive Open Online Course community. The tool integrates data request/authorization and distribution workflow features as well as provides a simple analytics module upload format to enable reuse and replication of analytics results among instructors and researchers. We survey the evolving landscape of competing established and emerging data models, all of which are accommodated in the platform. Data model descriptions are provided to analytics authors who choose, much like with smartphone app stores, to write for any number of data models depending on their needs and the proliferation of the particular data model. Two case study examples of analytics and responsive visualizations based on different data models are described in the paper. The result is a simple but effective approach to learning analytics immediately applicable to X consortium MOOCs and beyond.