Directory Data Research Articles

Characterizing physician directory data quality: variation by specialty, state, and insurer

BackgroundAs U.S. legislators are urged to combat ghost networks in behavioral health and address the provider data quality issue, it becomes important to better characterize the variation in data quality of provider directories to understand root causes and devise solutions. Therefore, this manuscript examines consistency of address, phone number, and specialty information for physician entries from 5 national health plan provider directories by insurer, physician specialty, and state.MethodsWe included all physicians in the Medicare Provider Enrollment, Chain, and Ownership System (PECOS) found in ≥ 2 health insurer physician directories across 5 large national U.S. health insurers. We examined variation in consistency of address, phone number, and specialty information among physicians by insurer, physician specialty, and state.ResultsOf 634,914 unique physicians in the PECOS database, 449,282 were found in ≥ 2 directories and included in our sample. Across insurers, consistency of address information varied from 16.5 to 27.9%, consistency of phone number information varied from 16.0 to 27.4%, and consistency of specialty information varied from 64.2 to 68.0%. General practice, family medicine, plastic surgery, and dermatology physicians had the highest consistency of addresses (37-42%) and phone numbers (37-43%), whereas anesthesiology, nuclear medicine, radiology, and emergency medicine had the lowest consistency of addresses (11-21%) and phone numbers (9-14%) across health insurer directories. There was marked variation in consistency of address, phone number, and specialty information by state.ConclusionsIn evaluating a large national sample of U.S. physicians, we found minimal variation in provider directory consistency by insurer, suggesting that this is a systemic problem that insurers have not solved, and considerable variation by physician specialty with higher quality data among more patient-facing specialties, suggesting that physicians may respond to incentives to improve data quality. These data highlight the importance of novel policy solutions that leverage technology targeting data quality to centralize provider directories so as not to not reinforce existing data quality issues or policy solutions to create national and state-level standards that target both insurers and physician groups to maximize quality of provider information.

Evaluation of Severity Index of Road Casualties on Mangawan to Chakghat Section of NH-30 Using Machine Learning Approach

Abstract: Each year, over 1.35 million people die in road accidents, and many more are seriously injured, according to the WHO. Predicting the severity of these accidents is vital for improving prevention and response plans. This paper proposes a new method to predict the severity of road accidents using various factors. This research focuses on road accidents at Sohagi Ghat on NH-30. The study looks at accident types and locations along a 3.67 Kilometer section of steep, challenging terrain. A safety audit was conducted on black spots and it revealed that 324 accidents occurred over five years, making this section particularly dangerous. Special safety measures are needed to reduce the risk in this area. The paper aims at forecasting of upcoming highway accidents through moving average approach, exponential smoothing approach and linear forecasting approach using the previous year’s data sets. With the help of Kaggle Data Directory, precise accident data for visualization is formed and then visualization of road accidents is performed on Python programming language-based Anaconda Navigator’s Jupyter Notebook. While accident severity index of Sohagi Ghat NH-30 is also carried out and some results of machine learning based visualization and some forecasting-based results are driven out and compared with each other. The study calculated accident severity and used Logistic Regression to train a model with 0.85 accuracy value. The precision, recall, and F-1 score of the model were perfect. Injury severity percentages were 10.8% fatal, 48.2% serious, and 41% minor. The Accident Severity Index for the NH-30 Sohagi Ghat location was found to be 0.211.

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Penelitian ini bertujuan untuk mengetahui kualitas batu bata yang memiliki campuran serbuk kayu dengan variasi yang berbeda–beda dan telah dilakukan tiga jenis pengujian dan dua karakterisasi. Proses pembuatan batu bata dengan tambahan serbuk kayu dilakukan dengan mencampurkan bentonit dan serbuk kayu hingga homogen, dengan persentase campuran antara serbuk kayu dan bentonit sebesar 0% (sebagai kontrol), 10%, 20%, 30% dan 50%, lalu dikeringkan dan dibakar. Kemudian batu bata diuji tiga parameter yaitu uji kuat tekan, uji akustik dan uji daya serap air. Setelah itu dikarakterisasi SEM dan FTIR. Berdasarkan hasil pengujian, diperoleh masing-masing uji parameternya yaitu nilai kuat tekan secara minimum 43,75 kg/cm2 dan maksimumnya 82,57 kg/cm2, dimana variasi 1 dan 2 memenuhi syarat kualitas (sesuai kategori kelas III menurut SNI 15-2094-2000). Hasil uji akustik diperoleh hasil terbaik pada semua variasi pada pengujian kedua dan ketiga saat Ls (Level malam) dan nilai daya serap air diperoleh secara minimum 11,16 % dan maksimum 60,98 %, dimana variasi 1 memenuhi syarat kualitas (sesuai standar SNI 15–2094–2000).

The Building Data Genome Directory – An open, comprehensive data sharing platform for building performance research

The building sector plays a crucial role in the worldwide decarbonization effort, accounting for significant portions of energy consumption and environmental effects. However, the scarcity of open data sources is a continuous challenge for built environment researchers and practitioners. Although several efforts have been made to consolidate existing open datasets, no database currently offers a comprehensive collection of building data types with all subcategories and time granularities (e.g., year, month, and sub-hour). This paper presents the Building Data Genome Directory, an open data-sharing platform serving as a one-stop shop for the data necessary for vital categories of building energy research. The data directory is an online portal (buildingdatadirectory.org/) that allows filtering and discovering valuable datasets. The directory covers meter, building-level, and aggregated community-level data at the spatial scale and year-to-minute level at the temporal scale. The datasets were consolidated from a comprehensive exploration of sources, including governments, research institutes, and online energy dashboards. The results of this effort include the aggregation of 60 datasets pertaining to building energy ontologies, building energy models, building energy and water data, electric vehicle data, weather data, building information data, text-mining-based research data, image data of buildings, fault detection diagnosis data and occupant data. A crowdsourcing mechanism in the platform allows users to submit datasets they suggest for inclusion by filling out an online form. This directory can fuel research and applications on building energy efficiency, which is an essential step toward addressing the world’s energy and environmental challenges.

Post-mortem digital forensic analysis of the Garmin Connect application for Android

The Garmin Vivosmart 4 smartband can monitor various health metrics, including heart rate, oxygen saturation, body composition, and stress levels. It is a quite popular fitness tracking device, as its Android companion application – Garmin Connect – has been downloaded more than 10 million times and can provide critical forensic artifacts such as timestamped GPS-based locations. In this work, we analyze the Garmin Connect application to identify i) relevant digital forensic artifacts, and ii) assess methods to retrieve cloud-based data relevant to a digital forensic examination. For this purpose, we first establish a test scenario where the paired device/application collects data in regular real-world situations using a rooted smartphone running Android 11. The smartphone is then examined to gain insights into the data stored by the application and identify meaningful digital artifacts.To ease and automate the task of digital forensic practitioners, we have developed the Garmin Connect for Android Analyzer (GC4AA) set of Python 3 modules tailored for the digital forensic framework Android Logs Events And Protobuf Parser (ALEAPP). These open-source modules parse dumps of a Vivosmart 4 data directory and create reports displaying several digital artifacts, such as health metrics, GPS data and routes, and phone notifications. They automate the information-gathering process and produce a report specially tailored for Garmin Connect data, highlighting the most relevant artifacts. Our results show that the analysis of paired Garmin Collect/Vivosmart 4 with GC4AA can yield more digital forensic artifacts than existing open-source tools, including the following new artifacts: i) Daily Summary data; ii) GPS data; iii) Response Cache data; iv) Network Logs; v) Facebook API tokens; vi) Device Synchronization cache; vii) SpO2 reading charts. Our contributions include a graphical presentation of the collected data, greatly improving its readability and analysis.

Comparative analysis of certainty factor and analytic hierarchy process for landslide susceptibility zonation in parts of Solan, Himachal Pradesh, India

The state of Himachal Pradesh in India is one of the most important hotspots when it comes to landslides; and Kandaghat, a tehsil in the Solan district of Himachal Pradesh having religious and tourism importance, is substantially affected by frequent landslides causing road blocking. In the present study, the analytic hierarchy process (AHP) and certainty factor (CF) techniques, which form part of the geographic information system (GIS)-based landslide susceptibility models, were used to prepare a landslide susceptibility map for the Kandaghat region, for which, as a preliminary step, an inventory of 214 live landslides was prepared from the Bhukosh data directory. The landslide inventory was cross-verified on the Google Earth platform. About nine landslide causative factors (slope, curvature, aspect, soil, rainfall, land use–land cover, lithology, drainage density and lineament density) were considered for the study area, and against the backdrop of these, the corresponding thematic maps were prepared and used in turn for the preparation of the final landslide susceptibility map. Based on the two mentioned techniques, the thematic maps were assigned weights according to their prominence and dynamic processes in the study area. The model performance for each method was evaluated using the area under the curve (AUC), and the accuracies for the AHP and CF were ascertained as, respectively, 81% and 85.6%. The Himalayan terrains are significantly prone to landslides, and this study outlines the characteristics of one of the important Himalayan towns in terms of vulnerability for landslides, together with providing its classification in terms of slope deformation susceptibility; this procedure can help direct attention towards areas needing to be classified under high to very high landslide susceptibility zones.

The Spatiotemporal Evolution and Influencing Factors of the Ceramics Industry in Jingdezhen in the Last 40 Years

The ceramic industry has been vital to the city’s development and prosperity in Jingdezhen, but the development of the ceramics industry in Jingdezhen has been unclear since China’s economic reforms, which will become a bottleneck limiting the sustainable development of the city. This study explored the spatial agglomeration and spatiotemporal evolution of the ceramics industry in Jingdezhen from 1980 to 2020 using enterprise directory data. The study opted for a microscopic perspective and employed kernel density estimation and exploratory spatial data analysis to obtain the necessary results. It also analyzed the influencing factors using a Geodetector. The results show that the temporal evolution of the ceramics industry in Jingdezhen went through two stages from 1980 to 2020. The number of enterprises experienced exponential growth, with fluctuations. The spatial evolution of the ceramics industry transitioned from a “single-center” to a “double-center” model and further evolved into a “multi-center” model. Moreover, the spatial agglomeration of the ceramics industry underwent the process of “agglomeration-diffusion-polarization”, ultimately developing into four ceramic industrial agglomeration patterns in six hotspots. Agglomeration, historical, technological, policy, and transportation factors had positive effects on the evolution of the ceramics industry in Jingdezhen, with agglomeration being the top contributor. Likewise, there were obvious interactions between the factors. This study can provide a basis for formulating policies to support urban spatial planning for urban revitalization, and provide foundation for the development of the national ceramic culture inheritance and innovation pilot zone in Jingdezhen.

CLASSYFYING STUDENTS DECISION MAKING ABILITY USING K-NEAREST NEIGHBOR FOR DETERMINING STUDENTS SUPPLEMENTARY LEARNING

Mathematical decision-making ability is a complex cognitive process of finding problems solutions which is continuously explored and optimized for undergraduate students. The current research only focus on categorization on class score average into high, medium and low abilities. As a result, the lecturers do not have any standard categories to classify students’ abilities as a reference in planning supplementary learning that could optimize undergraduate students’ abilities. Therefore, the purpose of this study is to determine a classification model for undergraduate students' mathematical decision-making abilities that require supplementary learning based on the identification of the shortest distance of a new data from an existing data directory. The research method involved data mining techniques with the KNN classification model through the Knowledge Discovery in Database (KDD) process starting from data selection, pre-processing, transformation, data mining and interpretation/evaluation [1]. A total of 100 data were used as research samples which were divided into training data and testing data. Based on the test results, it is obtained that the accuracy of the classification model is 95% for the parameter value k = 15, meaning that each predicted testing data for the classification class is close to the actual condition with the number of neighbors 15 data from the training data.

PyTANSPEC: A data reduction package for TANSPEC

TIFR-ARIES Near Infrared Spectrometer (TANSPEC) instrument provides simultaneous wavelength coverage from 0.55 to 2.5 $$\mu $$ m, mounted on India’s largest ground-based telescope, 3.6-m Devasthal Optical Telescope at Nainital, India. The TANSPEC offers three modes of observations, imaging with various filters, spectroscopy in the low-resolution prism mode with derived $$R\sim 100$$ –400 and the high-resolution cross-dispersed mode (XD-mode) with derived median $$R\sim 2750$$ for a slit of width 0.5 arcsec. In the XD-mode, 10 cross-dispersed orders are packed in the $$2048 \times 2048$$ pixels detector to cover the full wavelength regime. As the XD-mode is most utilized as well as for consistent data reduction for all orders and to reduce data reduction time, a dedicated pipeline is essential. In this paper, we present the code for the TANSPEC XD-mode data reduction, its workflow, input/output files and a showcase of its implementation on a particular dataset. This publicly available pipeline pyTANSPEC is fully developed in Python and includes nominal human intervention only for the quality assurance of the reduced data. Two customized configuration files are used to guide the data reduction. The pipeline creates a log file for all the fit files in a given data directory from its header, identifies correct frames (science, continuum and calibration lamps) based upon the user input, offers an option to the user for eyeballing and accepting/removing of the frames, does the cleaning of raw science frames and yields final wavelength calibrated spectra of all orders simultaneously.

A data directory to facilitate investigations on worldwide wildlife trafficking

ABSTRACT Wildlife trafficking is a global phenomenon posing many negative impacts on socio-environmental systems. Scientific exploration of wildlife trafficking trends and the impact of interventions is significantly encumbered by a suite of data reuse challenges. We describe a novel, open-access data directory on wildlife trafficking and a corresponding visualization tool that can be used to identify data for multiple purposes, such as exploring wildlife trafficking hotspots and convergence points with other crime, discovering key drivers or deterrents of wildlife trafficking, and uncovering structural patterns. Keyword searches, expert elicitation, and peer-reviewed publications were used to search for extant sources used by industry and non-profit organizations, as well as those leveraged to publish academic research articles. The open-access data directory is designed to be a living document and searchable according to multiple measures. The directory can be instrumental in the data-driven analysis of unsustainable illegal wildlife trade, supply chain structure via link prediction models, the value of demand and supply reduction initiatives via multi-item knapsack problems, or trafficking behavior and transportation choices via network interdiction problems.

Information Retrieval Method of Natural Resources Data based on Hash Algorithm

In order to improve the ability of searching and identifying information in natural resources data, this paper puts forward a method of searching information in natural resources data based on hash algorithm. Through the data center technology, the problems of information source positioning, data directory organization, data semantic definition and expression, and data entity relationship construction in natural resource data center are solved. Combined with the distribution of resource data stream, the information structure reorganization and data encryption in natural resource data center are realized by using hash algorithm, and the parameters of information quality control model in natural resource data center are established. Through natural resource data governance and semantic reconstruction, the characteristics detection and redundancy arrangement of information data in natural resource data center are realized by standardizing data collection rules, and they are stored in the intermediate database. Through data governance rules, the information in the natural resources data is structured and managed, and stored in the publishing library. Through all kinds of data processing tools, all kinds of data are processed, cleaned and reconstructed, and through Hash algorithm and data aggregation processing, information detection in natural resources data is realized. The simulation results show that the precision rate of natural resource data retrieval by this method is high.

Assurances for Higher Education, and Other Industries, as Well as Various Supports and Services at Global Standard, Are Made Possible by Unrestricted Open Data Directories: Vision 2040

Assurances for Higher Education, and Other Industries, as Well as Various Supports and Services at Global Standard, Are Made Possible by Unrestricted Open Data Directories: Vision 2040

Fluctuations in the Number of Stores by Industry During the COVID-19 Pandemic Based on Japanese Phone Book Entries

Currently in Japan, summaries of the number of bankruptcies due to the spread of COVID-19 can only be obtained from surveys conducted by a few research firms targeting particular companies. In this study, we used Japanese telephone directory data containing detailed information on the location and industrial category of stores/facilities nationwide in an effort to infer the influence of COVID-19 on businesses in Japan. We analyzed the temporal change in the number of stores before and after the COVID-19 outbreak. Among other findings, the analysis revealed that the number of travel agencies and facilities offering karaoke and other forms of entertainment declined significantly after the outbreak in some prefectures, with the largest declines in Ibaraki, Osaka, and Hyogo prefectures, and a relatively small decline in Tochigi prefecture. Among the stores and facilities categorized as restaurants and travel-related services, the decline was particularly significant in urban areas such as Tokyo and Osaka prefectures.

RWD51 Comparing the Food and Drug Administration National Drug Code Directory and Redbook to Identify Prescription Records

The National Drug Code (NDC) Directory by the Food and Drug Administration (FDA) is a publicly available, and frequently used in pharmacoepidemiologic to identify prescription drug usage. The current study compared the FDA NDC Directory data and Micromedex Redbook® in identifying frequently prescribed medication records in nationally representative utilization data using the NDC generic name mapping tables.

Dataset of Annotated Virtual Detection Line for Road Traffic Monitoring

Monitoring, detection, and control of traffic is a serious problem in many cities and on roads around the world and poses a problem for effective and safe control and management of pedestrians with edge devices. Systems using the computer vision approach must ensure the safety of citizens and minimize the risk of traffic collisions. This approach is well suited for multiple object detection by automatic video surveillance cameras on roads, highways, and pedestrian walkways. A new Annotated Virtual Detection Line (AVDL) dataset is presented for multiple object detection, consisting of 74,108 data files and 74,108 manually annotated files divided into six classes: Vehicles, Trucks, Pedestrians, Bicycles, Motorcycles, and Scooters from the video. The data were captured from real road scenes using 50 video cameras from the leading video camera manufacturers at different road locations and under different meteorological conditions. The AVDL dataset consists of two directories, the Data directory and the Labels directory. Both directories provide the data as NumPy arrays. The dataset can be used to train and test deep neural network models for traffic and pedestrian detection, recognition, and counting.

DIREKTORI DATA PESANTREN DI KABUPATEN MALANG (Profiling dan Pengembangan Direktori Pesantren Center)

Pesantren is a non formal educational institution that is one of the important means in the spread of Islam in Indonesia. The existence of pesantren for the lives of Indonesian people has a very significant role. Therefore, the study of pesantren, especially those related to the collection of pesantren, becomes important to do. Meanwhile, in the statistical report of Malang regency, the number of pesantren in Malang regency in 2019 amounted to 724 pesantren in 33 sub-districts. The data according to the study while some RMI PC managers are data that also cannot be valid values, because it is likely that many enter institutions other than pesantren, such as TPQ, Majlis Ta'lim or Madrasah Diniyah. Thus, this research project becomes very important to provide serious assistance to the creation, development and development of pesantren directories on the pesantren center website, the implementation of pesantren data collection through pesantren profiling programs, and ensuring the entry of pesantren profiling data into the directory system. The creation and development of the pesantren directory system on the Pesantren Center website can be carried out properly and in accordance with the planned targets. Even this system product exceeds the expectations of some members of pesantren center itself who can produce web-based application products with a coding system that is quite user friendly very easy to use by teams, officers and program agent profiling. Keywords: Data Directory, Profiling, Pesantren

ДОСЛІДЖЕННЯ ВПЛИВУ ПІДВИЩЕНОЇ ТЕМПЕРАТУРИ НА ДИСИПАТИВНІ ВЛАСТИВОСТІ ОДНОСПРЯМОВАНОГО ВУГЛЕПЛАСТИКУ

An experimental method for determination of composite materials dissipative properties, the main characteristic of which is the minimization of external energy losses and ensuring uniform distribution of deformations along the specimen working part under the pure bending condition has been developed. The scheme of the modernized experimental setup is presented. Its mechanical system represents a closed circuit in which the energy in material specimen under flexural vibrations is dissipated. Automated testing and registration of vibration characteristics were carried out by the method of attenuating vibrations at different temperatures. Based on the results for a set of experimental studies on the determination of the dissipative properties of unidirectional CFRP specimens under the pure bending conditions, the dependencies of the logarithmic decrement of vibrations on the relativedeformation in the range (2...12)∙10–4for temperatures from 300 K to 423 K was established.To analyze the effect of temperature on the dissipative properties of the composite, a parameter is proposed that is determined by the increment of the logarithmic decrement of vibrations (LDV) before deformation. It is shown that it is a more informative indicator of the structural integrity of carbon fiber samples, even with a relatively small violation of it due to heating and simultaneous cyclic deformation, compared to the relative growth of the LDV and the drop in the vibration frequency.The obtained experimental data and the presented method of investigation the dissipative properties of materials can be used as a directory data in the design of new models of equipment and in the development of mathematical models of composite structural elements operating under dynamic loads and elevated temperatures.

Data Lake Organization

We consider the problem of building an organizational directory of data lakes to support effective user navigation. The organization directory is defined as an acyclic graph that contains nodes representing sets of attributes and edges indicating subset relationships between nodes. A probabilistic model is constructed to model user navigational behaviour. The model also predicts the likelihood of users finding relevant tables in a data lake given an organization. We formulate the data lake organization problem as an optimization over the organizational structure in order to maximize the expected likelihood of discovering tables by navigating. An approximation algorithm is proposed with an analysis of its error bound. The effectiveness and efficiency of the algorithm are evaluated on both synthetic and real data lakes. Our experiments show that our algorithm constructs organizations that outperform many existing organizations including an existing hand-curated taxonomy, a linkage graph, and a common baseline organization. We have also conducted a formal user study which shows that navigation can help users discover relevant tables that are not easily accessible by keyword search queries. This suggests that keyword search and navigation using an organization are complementary modalities for data discovery in data lakes.

The XPS library website: A resource for the XPS community including - The XPS library of information, XPS spectra-base having >70,000 monochromatic XPS spectra, and spectral data processor (SDP) v8.0 software

Surface chemical analysis by XPS is more than 60 years old. During the last 15 years ther-e has been an explosive growth in the publication of XPS peak-fits and interpretations. Unfortunately, these publications suffer from a variety of problems in reliability and usefulness because the new authors are not familiar enough with the science and application of XPS, chemical state assignments, and peak-fitting. Efforts to help new users include journal publications such as self-training guides for peak-fitting, international standards, collections of BEs, examples of peak-fitting, and more. These user guides are useful and are published in journals that are known to publish XPS information. The basics of XPS are taught at a few graduate level courses in universities and there are 3–5 day training courses at conferences, but the need is much greater. To help the international XPS community and the infrequent users of XPS to overcome these limitations, problems and needs, The XPS Library™ was formed as a non-profit institute to provide an on-line resource of XPS information, self-training material, free >70,000 monochromatic XPS reference spectra, free spectral data processing software, reference tables, links, examples of good and bad peak-fitting, and more. In 1986, this author began to build The XPS Spectra-Base™ by collecting and organizing detailed sets of “self-consistent” monochromatic XPS spectra from reference materials, natural crystals, man-made single crystals, and commercial products. The current number of individual spectra in digital form is >70,000 and the number of materials is >9,000. Each set of spectra (4–10) is stored inside individual data directories having the name of the material analyzed. Multiple data directories belonging to the same category are placed in a directory with the name of that category. Each category name is distinct and is based on the chemistry of the materials. Currently, “XPS Library” members must download individual sets of spectra or full directories that are stored in ZIP files. To view, peak-fit, export, analyze, overlay, process, or print the spectra provided by The XPS Spectra-Base the library member needs a software that can import or directly open spectra having: VAMAS-ISO 14,976 ASCII file format, other ASCII file formats (XY, XYZ), or the SDP binary file format. Most software from the instrument makers can import these types of spectra. The “Spectral Data Processor” (SDP™) v8.0 is a 32-bit Windows™ (v7−10 compatible) software is provided free of charge to library members. Using SDP v8 the user can: (a) access and open the raw or processed spectra downloaded from The XPS Spectra-Base, (b) save all processing results and labels to permanent memory, (c) completely reprocess, peak-fit, analyze, overlay, process, or print the spectra, (d) export or save the raw or processed spectra in VAMAS-ISO ASCII and other ASCII file formats, and (e) import spectra having VAMAS-ISO 14,976 ASCII file format and other ASCII file formats (XY, XYZ). Several thousand High Energy Resolution Auger Electron Spectra (HER-AES) from insulators, conductors, and native oxides will be added. All spectra are owned, copyrighted, and are the property of The XPS Library and are protected by a registered international copyright (TX 4−560-881).

Impacting Community Well-Being in Lafayette, Indiana, in the Midst of a Pandemic

For the past five years, Dr. Jason Ware has centered community-based research and service-learning courses around local community partners’ needs as they focused collectively on community well-being issues. The nature of their work has prioritized qualitative research methods such as narrative inquiry via in-depth interviews and ethnography via immersive observations within varying service-providing institutions such as the Hartford Hub and the Hanna Community Center. COVID-19 and the constant threat of its transmission meant that Dr. Ware, his students, and their community partners had to approach their work differently. They responded with a pivot. They turned to mining large publicly accessible and proprietary data sets, such as United States Census data, Home Mortgage Disclosure Act (HMDA) data, the Homeless Management Information System (HMIS) data, and the Polk Directory data. The pivot served as a direct response to the City of Lafayette’s need for useful data that could inform decision-making related to neighborhood revitalization, affordable housing, and homelessness intervention. This different approach impacted the co-authors’ learning and scholarly development and provided the community partners with useful data. The co-authors experienced increased autonomy in pursuing data-specific questions, extracting data, analyzing, and visualizing it. One of the co-authors taught himself Python to import, statistically analyze, and visualize the data, and then presented the findings to the City of Lafayette. The co-author’s initial work — a pilot study — led to a scaled-up project that resulted in five significant outputs for three different community partners with a direct impact on six neighborhoods in the north end of Lafayette. Another co-author, who focused on scholarship during the pandemic, led an effort to develop a comprehensive literature review focused on the effect of community-based robotics programs on minority youth. The co-author also had presentations accepted at the local, national, and international levels while working on multiple publications. The third co-author is partnering with the other authors to create an automated system that will support the collection, extraction, and analysis of secondary data that will facilitate sustainable data analysis into the future.