Comma-separated Values Research Articles

An historical and geographic data set on the distribution of macroinvertebrates in Italian mountain lakes

Author(s): Boggero, Angela; Garzoli, Laura; Varini, Gianfranco | Abstract: Macroinvertebrates play a key role in freshwater food webs, acting as major links between organic matter resources, primary consumers (such as bacteria), and secondary consumers (e.g.fish, amphibians, birds, and reptiles). In this paper we present a data set encompassing all geographic and historical data available on macroinvertebrates of the Italian mountain lakes from 1902 to 2016. The data set, divided per Italian mountain range (Alps and Apennines) and administrative region, covers more than a century of studies of many foreign and Italian scientists. The data set includes 2372 records and shows macroinvertebrate occurrence data in 176 Alpine and in 13 Apennine lakes, of which 178 of natural origin, 5 reservoirs, and 6 artificially extended. The data set lists 605 taxa, updated on the basis of their current taxonomic position. Only 353 taxa are identified at species level, highlighting the still poorly investigated biodiversity of Italian mountain lake macroinvertebrates. Since they function as key elements to characterize lake ecological status, our data set emphasizes the huge taxonomic effort that still has to be undertaken to fully characterize these ecosystems. The data set is available in csv (comma-separated values) format.

Mining and visualising contradictory data

Big datasets are often stored in flat files and can contain contradictory data. Contradictory data undermines the soundness of the information from a noisy dataset. Traditional tools such as pie chart and bar chart are overwhelmed when used to visually identify contradictory data in multidimensional attribute-values of a big dataset. This work explains the importance of identifying contradictions in a noisy dataset. It also examines how contradictory data in a large and noisy dataset can be mined and visually analysed. The authors developed ‘ConTra’, an open source application which applies mutual exclusion rule in identifying contradictory data, existing in comma separated values (CSV) dataset. ConTra’s capability to enable the identification of contradictory data in different sizes of datasets is examined. The results show that ConTra can process large dataset when hosted in servers with fast processors. It is also shown in this work that ConTra is 100% accurate in identifying contradictory data of objects whose attribute values do not conform to the mutual exclusion rule of a dataset in CSV format. Different approaches through which ConTra can mine and identify contradictory data are also presented.

ViXeN: An open‐source package for managing multimedia data

Abstract There is a dearth of good toolkits to manage the different data formats that are associated with ecological datasets. In this paper, we describe a flexible, general‐purpose, easy‐to‐use, extensible, and open‐source toolkit that was built to manage an ecological study involving thousands of camera trap videos. ViXeN, the software package described here, can be used to view data files, including videos, images, audio files, portable document format (PDF) files, and text, in multiple formats. It can be used to annotate these data files, using metadata in the form of arbitrary user tags. The multimedia data catalogued, archived and extracted, using this software can easily be exported as a comma separated value file that can directly be used for data analysis. The software is capable of handling large datasets with up to a million files. Furthermore, ViXeN provides the option of additional customization by advanced users based on their particular requirements. We provide a case study involving more than 6,000 videos that were collected as part of a camera trap study involving mammalian carnivores to demonstrate some of the functionality of the software.

PyDescriptor: A new PyMOL plugin for calculating thousands of easily understandable molecular descriptors

The field of Quantitative Structure-Activity Relationship (QSAR) relies heavily on molecular descriptors. Among various guidelines suggested by Organisation for Economic Co-operation and Development (OECD), a very important guideline demands the mechanistic interpretation of a QSAR model. For this, a very attractive idea is to build a QSAR model using easily understandable molecular descriptors. To address this important issue, in the present work, we present an innovative chem-informatics tool, PyDescriptor. It can calculate a diverse pool of 11,145 molecular descriptors comprising easily understandable 1D- to 3D- descriptors encoding pharmacophoric patterns, atomic fragments and a variety of fingerprints. It is a new Python based plugin implemented within the commonly used visualization software PyMOL. PyDescriptor has several advantages like easy to install, open source, works on all major platforms (Windows, Linux, MacOS), easy to use through graphical user interface (GUI) and command-line, and output is saved in comma separated values (CSV) file format for further QSAR procedure. The plugin is freely available for academia.

The ACORN-SAT linked climate dataset

In 2012 the Australian Bureau of Meteorology published a dataset, ACORN-SAT, containing the homogenised daily temperature observations of 112 locations throughout Australia for the last 100 years. The dataset employs the latest analysis techniques and takes advantage of newly digitised observational data to monitor climate variability and change in Australia. The observations in ACORN-SAT were initially published only as comma separated values, whereas the metadata was published in a PDF report. In 2013 we converted the metadata and the observation data into RDF and published the result as Linked Open Data, accessible online via a pilot government linked data service built on the Linked Data API. In this article we describe the process of transforming the original tabular data into a Linked Sensor Data Cube (in: Proc. of the 5th International Workshop on Semantic Sensor Networks, SSN12, CEUR-WS.org, 2012, pp. 1-16) based on the W3C Semantic Sensor Network ontology (Web Semantics: Science, Services and Agents on the World Wide Web 17 (2012), 25-32) and the W3C RDF Data Cube vocabulary (The RDF Data Cube Vocabulary, W3C Recommendation, 16 January 2014). We further discuss how the dataset has since been used and interlinked with near-real time weather observations for the 112 sensing locations of the ACORN-SAT that are published by the Bureau of Meteorology. Both the original ACORN-SAT dataset and the weather observation data are accessible online at lab.environment.data.gov.au.

Desktop or remote knowledge base management systems for taxonomic data and identification keys: Xper2 and Xper3

Storing, sharing, managing taxonomic data is crucial for research and systematic outreach. In this context, Xper2 (Ung et al. 2010) and Xper3 (Vignes Lebbe et al. 2015) are two platforms dedicated to taxonomic descriptions and computer-aided identification. These freeware provide a sleek and easy to use interface, and do not require computer skills. Xper2 is available on Windows, MacOS or Linux. Download the software on the website. Xper3 gives users the ability to work simultaneously and remotely on the same knowledge base. Create your own account on the online platform. The numerous knowledge bases (over one thousand active KB) already created and published with the two platforms prove their relevance and efficiency (See for example Mathieu et al. 2012, Kerner 2012, Corvez and Grand 2014, Martin et al. 2015, Engel et al. 2016, Saïd et al. 2017). Scientists can share their data on phenotypes (structured descriptions, documented with images, videos, and text including bibliography and external links), compare phenotypes, and import or export partial or total content in various standard formats such as SDD (Structured Descriptive Data, a TDWG data standard), CSV (Comma Separated Values, used in Spreadsheet), and NEXUS (used in phylogenetic analyses, Maddison et al. 1997) for external analyses. KB can be exchanged between Xper2 and Xper3 via SDD files. We propose demos of the desktop version (Xper2) and online version (Xper3), illustrated with various contents on fossils, botany, marine species and arthropods. The TDWG 2017 participants are invited to come with their own data (list of taxa, identification keys, character-taxon matrices). They will learn and test how to import existing content into Xper KB, and how to build and publish keys (free access keys and single access keys) automatically.

A compact design of multi-feeder data logging system for power quality measurement with a multiplexer and a single PQ transducer

This paper presents a simple and costs effective equipment design multi-feeder data logger for recording and monitoring power quality. The system design uses remote supervising and multi-feeder data logging system (RESMOS). The data collected through resmos portable unit (RMPU) will automatically be saved with the format as binary and comma separated value (CSV). The time setting on the RMPU can be configured with minimum one minute per logging. This data logger uses a single transducer with a multiplexer for recording and monitoring ten channels of power quality at busbar. The system design has been validated with national metrology laboratory scientific and industrial research institute of Malaysia (SIRIM). This tool has the advantage that it can be used to measure harmonic data more than 21st at the same time for ten channels and equipped with software making it easier for analysis data with low operational costs versus another power quality equipment. The experimental results indicate that the proposed technique can accelerate data reading with conversion rate one sample per second for each channel. The device can be used to measure harmonic level and power quality with a confidence level above 95% and percentage error under 2.43% for total harmonics distortion (THD) and 1.72% for voltage harmonics.

Learning constraints in spreadsheets and tabular data

Spreadsheets, comma separated value files and other tabular data representations are in wide use today. However, writing, maintaining and identifying good formulas for tabular data and spreadsheets can be time-consuming and error-prone. We investigate the automatic learning of constraints (formulas and relations) in raw tabular data in an unsupervised way. We represent common spreadsheet formulas and relations through predicates and expressions whose arguments must satisfy the inherent properties of the constraint. The challenge is to automatically infer the set of constraints present in the data, without labeled examples or user feedback. We propose a two-stage generate and test method where the first stage uses constraint solving techniques to efficiently reduce the number of candidates, based on the predicate signatures. Our approach takes inspiration from inductive logic programming, constraint learning and constraint satisfaction. We show that we are able to accurately discover constraints in spreadsheets from various sources.

Synthesizer: Expediting synthesis studies from context-free data with information retrieval techniques.

Scientists have unprecedented access to a wide variety of high-quality datasets. These datasets, which are often independently curated, commonly use unstructured spreadsheets to store their data. Standardized annotations are essential to perform synthesis studies across investigators, but are often not used in practice. Therefore, accurately combining records in spreadsheets from differing studies requires tedious and error-prone human curation. These efforts result in a significant time and cost barrier to synthesis research. We propose an information retrieval inspired algorithm, Synthesize, that merges unstructured data automatically based on both column labels and values. Application of the Synthesize algorithm to cancer and ecological datasets had high accuracy (on the order of 85–100%). We further implement Synthesize in an open source web application, Synthesizer (https://github.com/lisagandy/synthesizer). The software accepts input as spreadsheets in comma separated value (CSV) format, visualizes the merged data, and outputs the results as a new spreadsheet. Synthesizer includes an easy to use graphical user interface, which enables the user to finish combining data and obtain perfect accuracy. Future work will allow detection of units to automatically merge continuous data and application of the algorithm to other data formats, including databases.

IFC File Analyzer Software.

The IFC File Analyzer software generates a spreadsheet or a set of CSV (comma-separated value) files from an IFC file. IFC (Industry Foundation Classes) is the data exchange standard used to facilitate interoperability in the building and construction industry. IFC is developed by buildingSMART and is an ISO standard—ISO 16739. Typical IFC viewers show avisualization of the building represented by the IFC file. The user can drill down to the individual attributes for a single building object. However, there is no way to view all of the entities and their attributes at once. The IFC File Analyzer provides this capability by creating a spreadsheet or CSV files from the IFC file. In the spreadsheet, a worksheet is created for each type of IFC entity in the file. Every row in the worksheet contains the attributes for an instance of an IFC entity. Multiple IFC files can be analyzed at once to compare entity usage. There are options to select which types of IFC entities are processed and to report some of the IFC entity Inverse relationships.

STEP File Analyzer Software.

The STEP File Analyzer is a software tool that generates a spreadsheet or a set of CSV (comma-separated value) files from a STEP (ISO 10303 –STandard for Exchange of Product model data) Part 21 file. STEP files are used to represent product and manufacturing information (PMI) and for data exchange and interoperability between Computer-Aided Design (CAD), Manufacturing (CAM), Analysis (CAE), and Inspection (CMM) software related to the smart manufacturing digital thread. STEP is also used for the long-term archiving and retrieval of product data. A spreadsheet simplifies inspecting information from the STEP file at an entity and attribute level. Typical STEP file viewers show a 3D visualization of the part or model represented by the STEP file. The viewers usually have a high-level hierarchical display of the information in the STEP file where the user can drill down to individual attributes of parts. However, there is no way to view all of the actual STEP entities and their attributes at once. The STEP File Analyzer provides this capability by creating a spreadsheet from the STEP file. The STEP File Analyzer also generates reports for PMI Representation, PMI Presentation, and Validation Properties based on Recommended Practices defined by the CAx Implementor Forum (CAx-IF) [5]. The objective of the CAx-IF is to advance CAx (mainly Computer-Aided Design and Engineering) software system STEP translator development and to ensure that user requirements for interoperability are satisfied.

Alchemy: A Web 2.0 Real-time Quality Assurance Platform for Human Immunodeficiency Virus, Hepatitis C Virus, and BK Virus Quantitation Assays

Background: The molecular diagnostics laboratory faces the challenge of improving test turnaround time (TAT). Low and consistent TATs are of great clinical and regulatory importance, especially for molecular virology tests. Laboratory information systems (LISs) contain all the data elements necessary to do accurate quality assurance (QA) reporting of TAT and other measures, but these reports are in most cases still performed manually: a time-consuming and error-prone task. The aim of this study was to develop a web-based real-time QA platform that would automate QA reporting in the molecular diagnostics laboratory at our institution, and minimize the time expended in preparing these reports. Methods: Using a standard Linux, Nginx, MariaDB, PHP stack virtual machine running atop a Dell Precision 5810, we designed and built a web-based QA platform, code-named Alchemy. Data files pulled periodically from the LIS in comma-separated value format were used to autogenerate QA reports for the human immunodeficiency virus (HIV) quantitation, hepatitis C virus (HCV) quantitation, and BK virus (BKV) quantitation. Alchemy allowed the user to select a specific timeframe to be analyzed and calculated key QA statistics in real-time, including the average TAT in days, tests falling outside the expected TAT ranges, and test result ranges. Results: Before implementing Alchemy, reporting QA for the HIV, HCV, and BKV quantitation assays took 45–60 min of personnel time per test every month. With Alchemy, that time has decreased to 15 min total per month. Alchemy allowed the user to select specific periods of time and analyzed the TAT data in-depth without the need of extensive manual calculations. Conclusions: Alchemy has significantly decreased the time and the human error associated with QA report generation in our molecular diagnostics laboratory. Other tests will be added to this web-based platform in future updates. This effort shows the utility of informatician-supervised resident/fellow programming projects as learning opportunities and workflow improvements in the molecular laboratory.

Twitter Streaming and Analysis through R

Objectives: To retrieve tweets from Twitter through Twitter API. The domain chosen for analysis is Make-In-India Dataset. Methods/Statistical Analysis: This paper consists of two phases of work: 1. Data Streaming from Twitter, 2. Knowledge Mining through R-Studio. Methods used for the two key operations are: 1. Twitter API and 2. Sentiment Analysis through R. Twitter application is created to request for connection with the Twitter database. Once connection establishes, authentication keys are generated. Providing the search key “Make-In-India” and number of keys required, a file with .df (data frame) is generated with the tweets and is converted into .CSV (Comma Separated Values) file which is suitable Analysis. Sentiment Analysis1 is also called Opinion mining talks about retrieving facts from the tweets such as how many people supporting Make-In-India (or) how many are negative with the scheme (or) how many are neutral with it. For this process, a negative words file and a positive words file is taken for comparison with the tweet data to calculate positive score and negative score of the tweet. The difference of these scores gives us with the final score of the tweet. Findings: The number of tweets identified as positive (or) negative (or) neutral so that the status of Make-In-India can be visualised in a graph. Firstly, the extraction of Tweets is from Twitter through R-Studio Environment About “Make-In-India”. Secondly, we parse the extracted raw tweets using R according to the types and store in .CSV format in R database. Scores are calculated for all the tweets and stored in a file. In the third, we perform visual analysis from the stored data using R statistical software to conclude the impact of the program. Application/Improvements: Application of the methodology is to get findings2 from the public opinions which are available from Twitter tweets on a particular government issue, political parties and medical status around the country. Also it is useful to assess the popularity of the political leader and the program. Decision making is possible through sentiment analysis of user tweets.

Merging clinical chemistry biomarker data with a COPD database - building a clinical infrastructure for proteomic studies.

BackgroundData from biological samples and medical evaluations plays an essential part in clinical decision making. This data is equally important in clinical studies and it is critical to have an infrastructure that ensures that its quality is preserved throughout its entire lifetime. We are running a 5-year longitudinal clinical study, KOL-Örestad, with the objective to identify new COPD (Chronic Obstructive Pulmonary Disease) biomarkers in blood. In the study, clinical data and blood samples are collected from both private and public health-care institutions and stored at our research center in databases and biobanks, respectively. The blood is analyzed by Mass Spectrometry and the results from this analysis then linked to the clinical data.MethodWe built an infrastructure that allows us to efficiently collect and analyze the data. We chose to use REDCap as the EDC (Electronic Data Capture) tool for the study due to its short setup-time, ease of use, and flexibility. REDCap allows users to easily design data collection modules based on existing templates. In addition, it provides two functions that allow users to import batches of data; through a web API (Application Programming Interface) as well as by uploading CSV-files (Comma Separated Values).ResultsWe created a software, DART (Data Rapid Translation), that translates our biomarker data into a format that fits REDCap's CSV-templates. In addition, DART is configurable to work with many other data formats as well. We use DART to import our clinical chemistry data to the REDCap database.ConclusionWe have shown that a powerful and internationally adopted EDC tool such as REDCap can be extended so that it can be used efficiently in proteomic studies. In our study, we accomplish this by using DART to translate our clinical chemistry data to a format that fits the templates of REDCap.

Use of the Information System SMS Alert for the management of risk communication in animal epidemic emergencies -African Swine Fever (A.S.F.)

Purpose: In order to counter effectively the animal epidemic emergency, it is necessary that the Veterinary Public Health manages risk communication for breeders interested in the area of crisis, with timely, quick and clear information. The purpose of this paper is to describe the model of communication used and the benefits obtained in the control of African Swine Fever. Methods & Materials: Following the African Swine Fever virus confirmation, the Units of Local Crisis locates the outbreak farm among those present in the database prepared by the Animal Health Service which contains updated information of all pig herds in the territory of ASL Sassari (names, addresses and telephone numbers of the owners, geographic coordinates lat / lon of farms, livestock numbers). All data is saved in Comma Separated Values (CSV) and imported on the QGIS software for the creation of a layer of all pig farms. Through geoprocessing function, two buffer zones around the outbreak of fever were created: the protection zone of 3 km and surveillance zone of 10 km. In this areas the farms were selected and through the software SMS_alert procedure were extrapolated the contact information of the farmers and sent, via a GSM modem, SMS text messages containing the recommendations and the health requirements. Results: In the territory of the ASL of Sassari, in 2015, there were 7 outbreak of African Swine Fever (ASF) in domestic pigs that involved 140 pig farms in the protection zones and 765 pig farms in the Surveillance Zone. The messages are promptly arrived to all farmers concerned. Conclusion: The use of the Information System SMS Alert, has fostered a risk communication to farmers effectively and enabled a rapid and immediate action of warning and their sensitization to take the best biosecurity measures to limit the spread of the ASFV. In fact, in 2015, compared with 7 primary outbreaks, there hasn’t been no secondary outbreak of ASF, health indicator of effective action by the Veterinary Public Service, and a good collaborative response by farmers.

Estimating the Duration of Public Concern After the Fukushima Dai-ichi Nuclear Power Station Accident From the Occurrence of Radiation Exposure-Related Terms on Twitter: A Retrospective Data Analysis.

BackgroundAfter the Fukushima Dai-ichi Nuclear Power Station accident in Japan on March 11, 2011, a large number of comments, both positive and negative, were posted on social media.ObjectiveThe objective of this study was to clarify the characteristics of the trend in the number of tweets posted on Twitter, and to estimate how long public concern regarding the accident continued. We surveyed the attenuation period of the first term occurrence related to radiation exposure as a surrogate endpoint for the duration of concern.MethodsWe retrieved 18,891,284 tweets from Twitter data between March 11, 2011 and March 10, 2012, containing 143 variables in Japanese. We selected radiation, radioactive, Sievert (Sv), Becquerel (Bq), and gray (Gy) as keywords to estimate the attenuation period of public concern regarding radiation exposure. These data, formatted as comma-separated values, were transferred into a Statistical Analysis System (SAS) dataset for analysis, and survival analysis methodology was followed using the SAS LIFETEST procedure. This study was approved by the institutional review board of Hokkaido University and informed consent was waived.ResultsA Kaplan-Meier curve was used to show the rate of Twitter users posting a message after the accident that included one or more of the keywords. The term Sv occurred in tweets up to one year after the first tweet. Among the Twitter users studied, 75.32% (880,108/1,168,542) tweeted the word radioactive and 9.20% (107,522/1,168,542) tweeted the term Sv. The first reduction was observed within the first 7 days after March 11, 2011. The means and standard errors (SEs) of the duration from the first tweet on March 11, 2011 were 31.9 days (SE 0.096) for radioactive and 300.6 days (SE 0.181) for Sv. These keywords were still being used at the end of the study period. The mean attenuation period for radioactive was one month, and approximately one year for radiation and radiation units. The difference in mean duration between the keywords was attributed to the effect of mass media. Regularly posted messages, such as daily radiation dose reports, were relatively easy to detect from their time and formatted contents. The survival estimation indicated that public concern about the nuclear power plant accident remained after one year.ConclusionsAlthough the simple plot of the number of tweets did not show clear results, we estimated the mean attenuation period as approximately one month for the keyword radioactive, and found that the keywords were still being used in posts at the end of the study period. Further research is required to quantify the effect of other phrases in social media data. The results of this exploratory study should advance progress in influencing and quantifying the communication of risk.

In situ air temperature and humidity measurements over diverse land covers in Greenbelt, Maryland, November 2013–November 2015

Abstract. As our climate changes through time there is an ever-increasing need to quantify how and where it is changing so that mitigation strategies can be implemented. Urban areas have a disproportionate amount of warming due, in part, to the conductive properties of concrete and asphalt surfaces, surface albedo, heat capacity, lack of water, etc. that make up an urban environment. The NASA Climate Adaptation Science Investigation working group at Goddard Space Flight Center in Greenbelt, MD, conducted a study to collect temperature and humidity data at 15 min intervals from 12 sites at the center. These sites represent the major surface types at the center: asphalt, building roof, grass field, forest, and rain garden. The data show a strong distinction in the thermal properties of these surfaces at the center and the difference between the average values for the center compared to a local meteorological station. The data have been submitted to Oak Ridge National Laboratory Distributed Active Archive Center (ORNL-DAAC) for archival in comma separated value (csv) file format (Carroll et al., 2016) and can be found by following this link: http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1319.

EP 12. TREMOR12: An open-source mobile app for tremor quantification

Question Evaluating the effect of treatment of tremor is mostly performed with clinical rating scales. Mobile applications facilitate a more rapid, objective and quantitative evaluation of treatment effect. Existing mobile apps do not offer raw data access which limits algorithm development. Our objective is to develop a novel open-source mobile app for tremor quantification. Methods TREMOR12 is an open source mobile app that samples acceleration, rotation, rotation speed and gravity, each in 3 axes and time-stamped in a frequency up to 100Hz. The raw measurement data can be exported as a comma-separated value (CSV) file for further analysis in the TREMOR12P data processing module. The app was tested on five healthy volunteers (2 male, 3 female) aged between 22 and 41 years of age. Results This proof-of-concept study shows that the TREMOR12 app is able to detect and register tremor characteristics such as acceleration, rotation, rotation speed and gravity in a simple and non-burdensome way. The app is compatible with current regulatory oversight by the European Union (MEDDEV regulations) and the Food and Drug Administration (FDA) guidance on mobile medical applications. Conclusions TREMOR12 offers low-cost tremor quantification for research purposes and algorithm development, and may help to improve treatment evaluation.

TREMOR12: An Open-Source Mobile App for Tremor Quantification

Background: Evaluating the effect of treatment of tremor is mostly performed with clinical rating scales. Mobile applications facilitate a more rapid, objective, and quantitative evaluation of treatment effect. Existing mobile apps do not offer raw data access, which limits algorithm development. Objective: To develop a novel open-source mobile app for tremor quantification. Methods: TREMOR12 is an open-source mobile app that samples acceleration, rotation, rotation speed, and gravity, each in 3 axes and time-stamped in a frequency up to 100 Hz. The raw measurement data can be exported as a comma-separated value file for further analysis in the TREMOR12P data processing module. The app was evaluated with 3 patients suffering from essential tremor, who were between 55 and 71 years of age. Results: This proof-of-concept study shows that the TREMOR12 app is able to detect and register tremor characteristics such as acceleration, rotation, rotation speed, and gravity in a simple and nonburdensome way. The app is compatible with current regulatory oversight by the European Union (MEDDEV regulations) and the Food and Drug Administration (FDA) guidance on mobile medical applications. Conclusion: TREMOR12 offers low-cost tremor quantification for research purposes and algorithm development, and may help to improve treatment evaluation.

A New Paradigm to Analyze Data Completeness of Patient Data

There is a need to develop a tool that will measure data completeness of patient records using sophisticated statistical metrics. Patient data integrity is important in providing timely and appropriate care. Completeness is an important step, with an emphasis on understanding the complex relationships between data fields and their relative importance in delivering care. This tool will not only help understand where data problems are but also help uncover the underlying issues behind them. Develop a tool that can be used alongside a variety of health care database software packages to determine the completeness of individual patient records as well as aggregate patient records across health care centers and subpopulations. The methodology of this project is encapsulated within the Data Completeness Analysis Package (DCAP) tool, with the major components including concept mapping, CSV parsing, and statistical analysis. The results from testing DCAP with Healthcare Cost and Utilization Project (HCUP) State Inpatient Database (SID) data show that this tool is successful in identifying relative data completeness at the patient, subpopulation, and database levels. These results also solidify a need for further analysis and call for hypothesis driven research to find underlying causes for data incompleteness. DCAP examines patient records and generates statistics that can be used to determine the completeness of individual patient data as well as the general thoroughness of record keeping in a medical database. DCAP uses a component that is customized to the settings of the software package used for storing patient data as well as a Comma Separated Values (CSV) file parser to determine the appropriate measurements. DCAP itself is assessed through a proof of concept exercise using hypothetical data as well as available HCUP SID patient data.