Data Dictionary Research Articles

Physicians’ Response on Haemophilia Care in South Western Nigeria

Introduction: Haemophilia is a rare inherited bleeding disorder that affects predominantly male because it is an X chromosome linked disorder. The severity of the disease is determined by the percentage of Factors VIII and IX activity in the individuals with Haemophilia.
 Objective: The objective of this study was to determine the extent of haemophilia care in the southwestern part of Nigeria. This forms part of a nationwide survey in 2016, focusing especially on whether or not physicians in specific hospitals in the states in south west had ever managed a haemophiliac.
 Methods: A self-administered questionnaire titled “Survey on Haemophilia Care in Nigeria” was developed for physicians in order to assess their experience on Haemophilia care in Nigeria. The responses from the Doctors were entered into SPSS (version 23) using the Data Dictionary (on survey of haemophilia) coded table prepared along with the questionnaire.
 Results: The self-administered questionnaires were retrieved from 215 physicians in south west, Nigeria. Most of the respondents are from Oyo state, no respondent from both Osun and Ondo states. Also, about two-thirds of the respondents work in a teaching hospital, with majority having Bachelor of Medicine, Bachelor of Surgery (MBBS) as the highest qualification.
 Conclusion: The care of haemophiliacs in the south western part of Nigeria needs to be standardized as the study showed that most physicians have no contact with Haemophiliacs.

Novel reduced kernel independent component analysis for process monitoring

Kernel independent component analysis (KICA), as a nonlinear extension monitoring method of independent component analysis (ICA), has attracted significant attention. To accomplish different monitoring tasks for nonlinear systems with non-Gaussian data distribution, many modified algorithms based on KICA have also been designed. However, most of the existing methods suffer from defects; for example, the computation time increases with the number of training samples and the models are insensitive to minor faults. Nevertheless, there is currently limited research on addressing these defects, which greatly limits their application in industrial processes. To fill these gaps, a novel reduced kernel independent component analysis (NRKICA) method is proposed to reduce the computation complexity and improve the ability of minor fault detection at the same time. In this approach, an important factor is defined to measure the ability of the samples to represent the properties of the system. In addition, then the top-n important observations are selected to build a data dictionary. To improve the sensitivity to minor faults, the [Formula: see text] and [Formula: see text] statistics are redesigned by introducing information from past observations. Besides, the kernel parameter is optimized by the tabu search algorithm. The proposed method is applied to fault detection with a numerical example and the Tennessee Eastman process (TEP), and the experimental results verify the effectiveness and sensitivity of the proposed method.

PO2/TransformON, an ontology for data integration on food, feed, bioproducts and biowaste engineering

We are witnessing an acceleration of the global drive to converge consumption and production patterns towards a more circular and sustainable approach to the food system. To address the challenge of reconnecting agriculture, environment, food and health, collections of large datasets must be exploited. However, building high-capacity data-sharing networks means unlocking the information silos that are caused by a multiplicity of local data dictionaries. To solve the data harmonization problem, we proposed an ontology on food, feed, bioproducts, and biowastes engineering for data integration in a circular bioeconomy and nexus-oriented approach. This ontology is based on a core model representing a generic process, the Process and Observation Ontology (PO2), which has been specialized to provide the vocabulary necessary to describe any biomass transformation process and to characterize the food, bioproducts, and wastes derived from these processes. Much of this vocabulary comes from transforming authoritative references such as the European food classification system (FoodEx2), the European Waste Catalogue, and other international nomenclatures into a semantic, world wide web consortium (W3C) format that provides system interoperability and software-driven intelligence. We showed the relevance of this new domain ontology PO2/TransformON through several concrete use cases in the fields of process engineering, bio-based composite making, food ecodesign, and relations with consumer’s perception and preferences. Further works will aim to align with other ontologies to create an ontology network for bridging the gap between upstream and downstream processes in the food system.

Outcomes Associated With Intracranial Aneurysm Treatments Reported as Safe, Effective, or Durable

Testing new medical devices or procedures in terms of safety, effectiveness, and durability should follow the strictest methodological rigor before implementation. To review and analyze studies investigating devices and procedures used in intracranial aneurysm (IA) treatment for methods and completeness of reporting and to compare the results of studies with positive, uncertain, and negative conclusions. Embase, MEDLINE, Web of Science, and The Cochrane Central Register of Clinical Trials were searched for studies on IA treatment published between January 1, 1995, and the October 1, 2022. Grey literature was retrieved from Google Scholar. All studies making any kind of claims of safety, effectiveness, or durability in the field of IA treatment were included. Using a predefined data dictionary and analysis plan, variables ranging from patient and aneurysm characteristics to the results of treatment were extracted, as were details pertaining to study methods and completeness of reporting. Extraction was performed by 10 independent reviewers. A blinded academic neuro-linguist without involvement in IA research evaluated the conclusion of each study as either positive, uncertain, or negative. The study followed Preferring Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The incidence of domain-specific outcomes between studies with positive, uncertain, or negative conclusions regarding safety, effectiveness, or durability were compared. The number of studies that provided a definition of safety, effectiveness, or durability and the incidence of incomplete reporting of domain-specific outcomes were evaluated. Overall, 12 954 studies were screened, and 1356 studies were included, comprising a total of 410 993 treated patients. There was no difference in the proportion of patients with poor outcome or in-hospital mortality between studies claiming a technique was safe, uncertain, or not safe. Similarly, there was no difference in the proportion of IAs completely occluded at last follow-up between studies claiming a technique was effective, uncertain, or noneffective. Less than 2% of studies provided any definition of safety, effectiveness, or durability, and only 1 of the 1356 studies provided a threshold under which the technique would be considered unsafe. Incomplete reporting was found in 546 reports (40%). In this systematic review and meta-analysis of IA treatment literature, studies claiming safety, effectiveness, or durability of IA treatment had methodological flaws and incomplete reporting of relevant outcomes supporting these claims.

355. DEVELOPMENT OF A CUSTOM RELATIONAL DATABASE FOR INTEGRATING CLINICAL AND RESEARCH DATA FOR THE STUDY FOR ESOPHAGEAL ADENOCARCINOMA

Abstract Background With the advent of next-generation sequencing, digital pathology, and other high-dimensional data sources used in modern research, improved data management is required to maintain organization and relationship to the corresponding patient and their outcomes. The complexity of the data often limits interpretation by clinicians not highly versed in its analysis. Conversely, data scientists may not be able to interpret the data in the correct clinical context. We describe here a relational database and custom tools which coordinates clinical, organoid, and genomic data in the study of esophageal adenocarcinoma. Methods Database architecture was designed with pillars of clinical data and outcome, organoid culture data, and next-generation sequencing data. Data dictionaries were composed for all tables in the database architecture. Oracle Database was utilized and populated with datasets formatted to adhere to data dictionaries. Application Express was used to develop web applications for users to enter, query, and analyze data. Automatic nightly backups, robust login security, and tiers of access protect confidential data against system failures, human error, and data theft. Custom tools with graphical user interfaces were developed for survival curve generation and organoid drug-screening results, to facilitate clinician use. Results This database now encompasses over 21 data tables comprising clinical, surgical, recurrence, treatment response, tissue sample storage, organoid research, and genomic data. Data scientists can link omic data directly to patient clinical data and clinician researchers can rapidly obtain patient and organoid survival data for covariates of choice using a graphical user interface. Conclusion Proper data management requires a substantial initial investment in designing of database architecture, deployment of software and servers, formatting of data, and the creation of front-end user applications. The investment is well worth the cost, however, as a centralized database allows for rapid querying of data that would otherwise be scattered or require individualized analysis for each project. This also facilitates the interactions between data scientists and clinician researchers.

PERANCANGAN APLIKASI BIMBINGAN BELAJAR ONLINE

PERANCANGAN APLIKASI BIMBINGAN BELAJAR ONLINE

Rancang Bangun Sistem Informasi Peminjaman dan Pengembalian Buku di Perpustakaan SMK Lugina Rancaekek

The system of borrowing and returning books at the Library of SMK Lugina Rancaekek includes making loan reports, return reports, and member data is still done manually in the ledger, so errors often occur so that making reports takes a long time. The method used in this is the SLDC (System Development Life Cycle) method using Waterfall. Analysis and design of the system uses other tools such as Data Flow Diagram, Flowmap, Structure Chart, E-R Diagram, Data Dictionary, while the implementation uses Microsoft Visual Foxpro 9.0 and Microsoft Database Foxpro 9.0 for Database. The design of this system can facilitate the search for book data, speed up report generation, make it easier to control book stock, facilitate the process of borrowing and returning books so that they can produce information more quickly, precisely and accurately.

Sistem Pencatatan Dan Pelaporan Surveilans Infeksi Nosokomial Di RS Premier Surabaya

Nosocomial infection is infection that occur in patients who are hospitalized. One of infection control programs is the surveillance activities. Refer to the Instructions Practical Hospital nosocomial infections Surveillance by the Ministry of health (2011) that computer usage in surveillance activities will help increase the efficiency of data collection and analysis. The propuse of this study is to develop recording and reporting nosocomial infection surveillance system. Methods: this study type is action research with system development life cycle (SDLC) methods , the stages of SDLC is planning, analysis, design, implementation and usage. The instruments used in this study by creating an DFD,ERD, data dictionary and continued with the development of applications using PHP and mySQL. Data collection through interviews and observations. The study results in the form of web-based applications further tested using the method of Technology Accepatnce Mode (TAM). Keyword: Nosocomial infection surveillance, recording and reporting, system development, rs premier surabaya

Improving Graduate Medical Education by Aggregating Data Across the Medical Education Continuum.

Meaningful improvements to graduate medical education (GME) have been achieved in recent decades, yet many GME improvement pilots have been small trials without rigorous outcome measures and with limited generalizability. Thus, lack of access to large-scale data is a key barrier to generating empiric evidence to improve GME. In this article, the authors examine the potential of a national GME data infrastructure to improve GME, review the output of 2 national workshops on this topic, and propose a path toward achieving this goal.The authors envision a future where medical education is shaped by evidence from rigorous research powered by comprehensive, multi-institutional data. To achieve this goal, premedical education, undergraduate medical education, GME, and practicing physician data must be collected using a common data dictionary and standards and longitudinally linked using unique individual identifiers. The envisioned data infrastructure could provide a foundation for evidence-based decisions across all aspects of GME and help optimize the education of individual residents.Two workshops hosted by the National Academies of Sciences, Engineering, and Medicine Board on Health Care Services explored the prospect of better using GME data to improve education and its outcomes. There was broad consensus about the potential value of a longitudinal data infrastructure to improve GME. Significant obstacles were also noted.Suggested next steps outlined by the authors include producing a more complete inventory of data already being collected and managed by key medical education leadership organizations, pursuing a grass-roots data sharing pilot among GME-sponsoring institutions, and formulating the technical and governance frameworks needed to aggregate data across organizations.The power and potential of big data is evident across many disciplines, and the authors believe that harnessing the power of big data in GME is the best next step toward advancing evidence-based physician education.

American Burn Association (ABA) Burn Care Quality Platform (BCQP) and Large Data Set Analysis Considerations: A Practical Guide to Investigating Clinical Questions in Burns via Large Data Sets.

The Burn Care Quality Platform (BCQP) consolidates data previously collected from the National Burn Repository and the Burn Quality Improvement Program into a single registry. Its data elements and their associated definitions are tailored to create consistency across other national trauma registries, namely the National Trauma Data Bank implemented by the American College of Surgeons Trauma Quality Improvement Program (ACS TQIP). The BCQP now includes 103 participating burn centers and has captured data from 375,000 total patients as of 2021. With 12,000 patients entered under the current data dictionary, the BCQP represents the largest registry of its kind. On behalf of the American Burn Association Research Committee, the aim of this whitepaper is to provide a succinct overview of the BCQP, showcasing its unique features, strengths, limitations, and relevant statistical considerations. This whitepaper will highlight the resources available to the burn research community and offer insight on proper study design when preparing to conduct a large data set investigation for burn care. All recommendations herein were formulated through the consensus of a multidisciplinary committee and based on the available scientific evidence.

Data democratisation requires literacy and fluency for proficiency

This paper discusses the challenges of data democratisation for non-digital-native organisations and the importance of data literacy in the process. Data democratisation refers to providing access to data to everyone to enable easy and informed decision-making without gatekeepers. Without a firm understanding of the basics and an educated grasp of the nuances, exploring data can be intriguing but not constructive. To be successful, organisations must get everybody, including those who are less knowledgeable, to have the same understanding of data and speak the same language in a collaborative manner. A common understanding of data is supported by data dictionaries, catalogues and legacy repositories. A common language allows teams to better choose which projects have the highest likelihood of success. The paper emphasises the need for data fluency across an organisation.

Is there a harmonised standard to oncology trial data provision in data sharing initiatives?

e13645 Background: Over the past decade the pharmaceutical industry has invested considerably in data sharing infrastructures to support independent researcher access to clinical trial individual-participant data (IPD), facilitating novel secondary analyses and the development of predictive models/simulation tools. Data sharing also helps in informing trial design and can accelerate the development of new therapies. To facilitate a data sharing ecosystem achieving these visions, researchers need to be able to access data, and when they do so, optimally, the data needs to be provided in a harmonised format that supports the research. Methods: In 2021 an audit confirmed 91 (of 203) clinical trials supporting medicines approved by the FDA in the last 10 years for the treatment of solid tumours as eligible (‘in scope’) for independent IPD request. This study reports on data provision for a planned clinical trial IPD meta-analysis summarising adverse events and therapeutic outcomes for new anticancer medicines according to race and sex. For the trials shared upon request, we report the heterogeneity in provisions of IPD and supporting documents. Results: Following SAP submission, the team obtained IPD packages from 76% (69 of 91) of requested trials – this includes data from 13 companies in a pooled cohort of over 40000 patients treated with new medicines against solid tumours. Time to data provision ranged from 117 to 312 days. In the IPD packages of 7 trials, overall survival or progression-free survival data were redacted, while for 9 trials there were partial redactions to adverse event data. For 3 trials, race data was removed, and for 1, sex was removed. Age was provided as a categorised variable in 17 trials. Weight was removed from 10 trials, and performance status was removed from 2. For 19 trials no clinical study report was provided, for 4 trials the data dictionary was missing, for 18 trials there were no data derivation specifications, while 5 IPD packages did not contain files orientating to anonymisation. Conclusions: While there is room to improve and standardize data sharing principles and procedures, overall, the acquired IPD was observed as a significant and high-quality resource for future scientific discovery. Future research could consider evaluating the ability to access data from non-industry trial sponsors, and strategies to harmonise data sharing practices between companies.

Cancer ontology understanding among community oncologists: Insights from a survey linked to implementation of mCODE-informed electronic health record (EHR).

e18860 Background: Real-world data (RWD) will help recognize the gaps between evidence and practice, re-shaping clinical assessments. Data collected at the source in oncology-tailored EHRs are expected to facilitate generation of high-quality RWD. In the process of implementing a new EHR informed by mCODE standards in our network of oncology clinics, we defined mandatory variables amenable to structured data capture. However, medical knowledge about cancer ontology has not been methodically investigated. Methods: Online survey with 4 multiple-choice questions that illustrate in clinical cases the most important concepts and terminologies related to cancer diagnosis, treatment, and patient outcome. The questionnaire was shared with ~ 300 medical oncologists from our institution through mobile messaging app in December 2022. Physicians signed electronic informed consent for anonymized data analysis. Results: In total, 203 oncologists agreed to participate and 152 completed the survey. Most oncologists (75%) correctly define “therapy intent” (palliative versus curative) and corresponding “treatment line”. Many (51%) do not understand that “disease stage” is static (defined at the time of diagnosis) and does not change to stage IV if patient presents with relapsed/recurrent disease. A significant proportion of oncologists (66%) wrongly define “response status” to a given therapy as stable/progressive disease in a patient that had major clinical/radiological response but presents with unconfirmed tumor marker increase and no clear change in bone metastases by imaging. However, most oncologists (88%) can correctly define “reason for therapy discontinuation” as “planned interruption” in the end of adjuvant chemo-immunotherapy. When stratifying physicians according to self-defined knowledge of disease ontology, we found that 27% of those with moderate/deep understanding of mCODE scored 4 correct answers in the questionnaire as compared to 8% with 4 correct answers if mCODE knowledge was null or vague (P value < 0.001, odds ratio 5.1, CI95% 1.8-15.8). Conclusions: Overall understanding of cancer-specific data dictionaries is low among community oncologists. With the new EHR system we are nudging physicians not to update the “stage at diagnosis” status at the time of disease relapse/recurrence by creating a “current cancer status” element with mandatory completion (linked to prescriptions/ pharmacy claims). Also, “response status” as assessed by physicians must be cross-checked with other data sources (tumor markers, imaging results, physician notes and reason for therapy discontinuation) given inconsistent definition of “clinical benefit versus progressive disease” among physicians. In addition, we will constantly educate oncologists and monitor their EHR data entry behavior for successful execution of RWD projects.

Judgemental Analysis of Data and Prediction Using ANN

Everyday lot of posts are created in social media and several users make comments on the post. The comments can be positive or negative or moderate. Just by watching the post one cannot estimate whether the post is positive or not. Manual analysis of each comment of the post related document using Vader analysis takes a lot of time (approximately 5 to 6 hours- depending on number of comments). Also, every post comment contains lots of redundant data which should be removed to get the exact sentiment of the post. We need to analyze the sentiment of the post within fraction of seconds by eliminating the redundant data. By using Machine Learning ANN Algorithms and applying them to social media post related datasets which contains a large number of comments. Initially dataset to be imported and at first stage data pre-processing is to be performed so as to remove the useless redundant information or data. The Algorithm uses ANN Data Dictionary which contains list of positive words and negative words to classify each comment in the dataset into positive or negative or moderate category. Along with this it will also give the information about the sentiment of the post by visualizing the total positive, negative and moderate comments. Apart from this we are calculating the positive words and negative words number in each comment and also analyzing the sentim

Establishing the Australian National Endometriosis Clinical and Scientific Trials (NECST) Registry: A protocol paper.

Endometriosis is a common yet under-recognised chronic inflammatory disease, affecting 176 million women, trans and gender diverse people globally. The National Endometriosis Clinical and Scientific Trials (NECST) Registry is a new clinical registry, collecting and tracking diagnostic and treatment data, and patient-reported outcomes on people with endometriosis. The registry is a research priority action item from the 2018 National Action Plan for Endometriosis and aims to provide, large-scale, national and longitudinal population-based data on endometriosis. Working groups (consisting of patients with endometriosis, clinicians and researchers) developing the NECST Registry data dictionary and data collection platform started in 2019. Our data dictionary was developed based on existing and validated questionnaires, tools, meta-data and data cubes - World Endometriosis Research Foundation (WERF) Endometriosis Phenome and Biobanking Harmonisation Project (EPHect), endometriosis CORE outcomes set, patient-reported outcome measures, the International Statistical Classification of Diseases-10th Revision Australian Modification diagnosis codes, and Australian Government datasets: Australian Institute for Health and Welfare (for sociodemographic data), Medicare Benefits Schedule (MBS; for medical procedures) and the Pharmaceutical Benefits Scheme (PBS; for medical therapies). The resulting NECST Registry is an online, secure cloud-based database; prospectively collecting minimum core clinical and health data across eight patient and clinician modules and longitudinal data tracking disease life course. The NECST Registry has ethics approval (HREC/62508/MonH-2020) and is registered on the Australian New Zealand Clinical Trials Registry (ACTRN12622000987763).

Epidemiology of Gram-Negative Bloodstream Infections in the United States: Results From a Cohort of 24 Hospitals.

To address knowledge gaps in management of Gram-negative bloodstream infection, the Antibiotic Stewardship Implementation Collaborative was established consisting of programs from 24 academic and community hospitals across the United States. A retrospective cohort study was conducted of unique adult patients with Gram-negative bloodstream infection hospitalized at participating hospitals from January to December 2019. Patient level and microbiologic data were collected via electronic medical record review with a standardized data collection form and data dictionary. Data analysis was largely descriptive. The Pearson χ2 test to compare categorical variables and the Wilcoxon rank sum test for continuous variables were used. In total, 4851 bacterial isolates from 3710 eligible unique patients were included in the cohort. Most common source of infection was the urinary tract (47.9%). Source control was achieved in 84% of cases. Escherichia coli (2471, 51.0%) was the most common Gram-negative organism recovered. Antibiogram combining isolates from all participating centers with species-level susceptibilities and source specific antibiograms for isolates from urinary, respiratory, and intraabdominal source were created. Northeast sites contributed the most extended spectrum beta-lactamase (ESBL) producing organisms (73%), but West sites had the highest percentage of ESBL producers of total isolates (16%). A statistically significant difference in percentage of ESBL-producing organisms in Whites vs. non-Whites (14.6 % and 9.5 %, respectively, P<0.01) was observed. While the present study was conducted pre-pandemic, it highlights the need for stewardship data collaboratives to enhance our understanding of the antimicrobial resistance patterns.

287 Assessment of the National Burn Repository’s Ability to Examine Obesity and Determine Burn Related Outcomes

Abstract Introduction Despite obesity being a national epidemic there is a paucity of literature examining the effect of obesity in burn-injured patients. Controversy exists on whether obesity provides a protective benefit dubbed the “Obesity Paradox”. Previous literature examining the role of the National Burn Repository (NBR) in obesity-related research identified a lack of nutrition-related fields as a limitation of the dataset. The aim of this work was to provide an interval assessment of the NBR dataset and determine if an effect of obesity on burn-related outcomes and care can be elucidated. Methods A retrospective review of patient data contained within the NBR dataset from 2016-2018 was conducted. The data dictionary was queried for patients with “obesity” listed as a comorbidity. Information on patient weight was available, but no data on height, body mass index, or other nutrition-related fields were found. The primary outcome measure was mortality. Secondary outcomes included hospital length of stay (LOS), ICU LOS and total hospital costs. Patients were stratified by total body surface area burn (TBSA) to examine the effect of obesity on mortality under different injury severities. Mann-Whitney Tests were used to compare obese and non-obese patients. Multiple logistic regression (MLG) was used to compare effects of several common independent variables (age, gender, inhalation injury (IHI), percent full thickness burn (%FT), TBSA and obesity) on mortality, ICU LOS &amp;gt;7days, hospital LOS &amp;gt;10 days and total hospital costs &amp;gt;$200,000. Results Of 41,031 patients included in the analysis, 3,845 (9.37%) were identified as obese. Overall, obese patients had a higher mean TBSA (8.68 ± 13.19% vs. 7.83 ± 12.39%, p=0.01), but not a higher %FT (7.86 ± 14.09% vs. 7.60 ± 14.25%, p=0.85) or occurrence of inhalation injury (8.92 vs. 8.86%, p=0.90). Obese patients had significantly longer hospital LOS (11.93 ± 18.92 days vs. 10.13 ± 18.73 days, p&amp;lt; 0.001), ICU LOS (10.03 ± 18.96 days vs. 9.44 ± 17.68 days, p&amp;lt; 0.001) and total hospital costs ($364,385 vs $211,513, p&amp;lt; 0.001). MLG found obesity to an independent predictor of ICU LOS &amp;gt;7 days, hospital LOS &amp;gt;10 days and total hospital costs &amp;gt;$200,000 (p=0.036, p=0.039 and p=0.046, respectively). MLG also identified age, TBSA and %FT as independent predictors of mortality in the &amp;lt; 20% TBSA group. Age, TBSA, %FT and IHI were independent predictors of mortality in the 20-40% and &amp;gt;40% TBSA groups. Obesity was not an independent predictor of mortality in any group. Conclusions This study showed obesity was not found to be a predictor of mortality for any burn size but did influence extended ICU LOS, hospital LOS and total hospital costs. Standardizing the data dictionary and adding obesity-related fields may greatly strengthen the role of the NBR in obesity-related burn research. Applicability of Research to Practice Utilizing the NBR and understanding its limitations can help to improve this database and better understand the obese burn patient population.

PERANCANGAN SISTEM INFORMASI EVALUASI PASCA PELATIHAN KESEHATAN DI KEMENTERIAN KESEHATAN

The industrial revolution and the current era of information technology have touched various fields, including health. Competency development of health human resources is something that must be done to be able to adapt in the era of the industrial revolution 4.0. In an effort to develop the competence of Health Human Resources, training is a form that is commonly carried out today. Post-training evaluation is a form of training evaluation to determine the effectiveness of a training so that training can be continuously modified to achieve the best effectiveness. Evaluation of post-health training is currently still done manually, which causes not all health training to be evaluated because it requires a lot of resources and budget. The use of information technology in conducting post-training evaluations can be a solution to existing problems. The post-health training evaluation is carried out by developing an information system for the post-health training evaluation that can be carried out quickly, precisely and massively. The purpose of this research is to design an evaluation information system after health training to the system design stage. The development method uses the System Development Life Cyle (SDLC) with the waterfall model until the system design stage. System design is done by making context diagrams, ERD, TRD, Data Dictionary, Flow Chart and DFD. The design of this information system is expected to be the basis for the development of a more comprehensive health EPP information system in the future.

Rancang Bangun Sistem Informasi Penerimaan Keuangan Sekolah Berbasis Client Server di SMK Pasundan Jatinangor

This Client Server-Based School Financial Receipt Information System Design was developed to make it easier for teachers' school financial receipt transactions at SMK Pasundan Jatinangor so as to minimize duplication. This Client Server-Based School Financial Receipt Information System Design uses the SDLC (system Development Life cycle) system development method starting from the system development process until the system is implemented, which consists of several basic work stages, including analysis (Analysis), Design (Design). ), Code (Coding), Testing, and maintenance, with the waterfall model. While the research methodology used is descriptive method, to obtain data using observation, interviews, literature studies, and documentation studies. In optimizing the design of the school financial acceptance information system, using tools such as Data Flow Diagrams (DFD), Entity Relationship Diagrams (ERD), Flowmaps, Structured Charts and Data Dictionary. Hopefully this program can help to overcome the problems and risks encountered in the system that was running previously and will make it easier for users to make decisions

Unified access to up-to-date residue-level annotations from UniProtKB and other biological databases for PDB data

More than 61,000 proteins have up-to-date correspondence between their amino acid sequence (UniProtKB) and their 3D structures (PDB), enabled by the Structure Integration with Function, Taxonomy and Sequences (SIFTS) resource. SIFTS incorporates residue-level annotations from many other biological resources. SIFTS data is available in various formats like XML, CSV and TSV format or also accessible via the PDBe REST API but always maintained separately from the structure data (PDBx/mmCIF file) in the PDB archive. Here, we extended the wwPDB PDBx/mmCIF data dictionary with additional categories to accommodate SIFTS data and added the UniProtKB, Pfam, SCOP2, and CATH residue-level annotations directly into the PDBx/mmCIF files from the PDB archive. With the integrated UniProtKB annotations, these files now provide consistent numbering of residues in different PDB entries allowing easy comparison of structure models. The extended dictionary yields a more consistent, standardised metadata description without altering the core PDB information. This development enables up-to-date cross-reference information at the residue level resulting in better data interoperability, supporting improved data analysis and visualisation.