Application Programming Research Articles

Pengembangan Keuangan Mikro Islam untuk Mengatasi Kemiskinan dan Mencapai Tujuan SDGs: Pendekatan Bibliometrik

This study tries to review research on sustainable microfinance development in Scopus indexed journals. This analysis uses descriptive statistical analysis based on 319 selected papers related to microfinance sustainable development from both national and international journals. All samples of published journals have been published for 27 years from 1996 to 2022. The data is then processed and analyzed using the VOSviewer application program to find out the bibliometric visualization map for sustainable development of microfinance research. The results of this bibliometric mapping study show a map of the development of research in the field of microfinance sustainable development. This study also found a close relationship between microfinance sustainable development and the main goal of the SDGs, namely eradicating poverty to promote prosperity for all people of all ages around the world. This research finds new and interesting keywords for further research.

Bunyi Forman Vokal dalam Bahasa Jawa Ngoko: Kajian Fonetik Akustik

The Javanese language (BJ) is divided into three, namely Ngoko, Krama Madya, and Krama Inggil. BJ consists of 10 vowel sounds: [i, I, e, ɛ, a, ǝ, o, ɔ, U, u]. This study aims to determine the values of BJ vowel sounds based on FORMAN data analysis, comparing the voices of male speakers (PL) and female speakers (PP). Additionally, the study aims to identify the more dominant vowel sounds based on gender. The qualitative method was used to collect data on the vowel sounds of Ngoko Javanese (BJN) by analyzing voice recordings using the Praat application program. The quantitative method was employed to calculate the FORMAN F1, F2, and F3 values and determine the average difference in BJN vowel sounds. Data from the BJ dictionary, consisting of 10 words with vowel sounds at the beginning, were used for analysis. The results showed that in PL, the highest vowel values were: [u] at 3089Hz, [i] at 3918Hz, [ɔ] at 3535Hz, and [ɛ] at 3228Hz. The lowest vowel values were: [ɛ] at 277Hz, [U] at 431Hz, and [i] at 336Hz. In PP, the highest vowel values were: [ɛ] at 3021Hz, [i] at 3200Hz, [I] at 3083Hz, and [o] at 3367Hz. The lowest vowel values were: [e] at 391Hz, [I] at 525Hz, [ɛ] at 385Hz, and [i] at 455Hz. In conclusion, BJN consists of 10 vowel sounds, with the highest vowel sound being [ɔ] and the lowest vowel sound being [i] in PL. The comparison of vowel sounds at the beginning of words in PL and PP in BJN, based on the high and low sounds of the F1, F2, and F3 vowel sounds, indicates that PL is more dominant.

ANALYSIS AND DESIGN OF DIGITAL APPLICATION SYSTEMS FOR EMPLOYEE DATA MANAGEMENT IN XYZ STORE

As technology and information systems develop in the digital era, managing employee data has become very important for organizations and companies. An efficient and accurate employee data input process is the first step in managing human resources optimally. XYZ Shop is a shop located in the Banten area, which sells retail goods to end consumers, usually on a small scale and at home. The shop provides a variety of products, ranging from clothing, food, electronic goods, to household equipment. However, the XYZ store still faces challenges in managing employee data, such as limited time and human energy in carrying out this process because it is still manual. To overcome this problem, an employee data input program was designed to be an effective solution. With this program, the employee data management process can become more efficient, accurate and organized. The author used three techniques for data collection activities, namely observation, interviews and literature study. Development of an application program system for managing employee data at XYZ store using the PHP programming language and MySql database. The application system built is web-based, so it can be accessed easily anywhere and anytime. This provides immediate benefits in day-to-day operational efficiency, allowing human resources to focus on more strategic tasks. With the ability to manage employee data efficiently, stores can easily adapt to company growth, including branch expansion and increased employment

A data science pipeline applied to Australia's 2022 COVID-19 Omicron waves

The field of software engineering is advancing at astonishing speed, with packages now available to support many stages of data science pipelines. These packages can support infectious disease modelling to be more robust, efficient and transparent, which has been particularly important during the COVID-19 pandemic. We developed a package for the construction of infectious disease models, integrated it with several open-source libraries and applied this composite pipeline to multiple data sources that provided insights into Australia's 2022 COVID-19 epidemic. We aimed to identify the key processes relevant to COVID-19 transmission dynamics and thereby develop a model that could quantify relevant epidemiological parameters.The pipeline's advantages include markedly increased speed, an expressive application programming interface, the transparency of open-source development, easy access to a broad range of calibration and optimisation tools and consideration of the full workflow from input manipulation through to algorithmic generation of the publication materials. Extending the base model to include mobility effects slightly improved model fit to data, with this approach selected as the model configuration for further epidemiological inference. Under our assumption of widespread immunity against severe outcomes from recent vaccination, incorporating an additional effect of the main vaccination programs rolled out during 2022 on transmission did not further improve model fit. Our simulations suggested that one in every two to six COVID-19 episodes were detected, subsequently emerging Omicron subvariants escaped 30–60% of recently acquired natural immunity and that natural immunity lasted only one to eight months on average. We documented our analyses algorithmically and present our methods in conjunction with interactive online code notebooks and plots.We demonstrate the feasibility of integrating a flexible domain-specific syntax library with state-of-the-art packages in high performance computing, calibration, optimisation and visualisation to create an end-to-end pipeline for infectious disease modelling. We used the resulting platform to demonstrate key epidemiological characteristics of the transition from the emergency to the endemic phase of the COVID-19 pandemic.

Digital Epidemiology of Prescription Drug References on X (Formerly Twitter): Neural Network Topic Modeling and Sentiment Analysis.

Data from the social media platform X (formerly Twitter) can provide insights into the types of language that are used when discussing drug use. In past research using latent Dirichlet allocation (LDA), we found that tweets containing "street names" of prescription drugs were difficult to classify due to the similarity to other colloquialisms and lack of clarity over how the terms were used. Conversely, "brand name" references were more amenable to machine-driven categorization. This study sought to use next-generation techniques (beyond LDA) from natural language processing to reprocess X data and automatically cluster groups of tweets into topics to differentiate between street- and brand-name data sets. We also aimed to analyze the differences in emotional valence between the 2 data sets to study the relationship between engagement on social media and sentiment. We used the Twitter application programming interface to collect tweets that contained the street and brand name of a prescription drug within the tweet. Using BERTopic in combination with Uniform Manifold Approximation and Projection and k-means, we generated topics for the street-name corpus (n=170,618) and brand-name corpus (n=245,145). Valence Aware Dictionary and Sentiment Reasoner (VADER) scores were used to classify whether tweets within the topics had positive, negative, or neutral sentiments. Two different logistic regression classifiers were used to predict the sentiment label within each corpus. The first model used a tweet's engagement metrics and topic ID to predict the label, while the second model used those features in addition to the top 5000 tweets with the largest term-frequency-inverse document frequency score. Using BERTopic, we identified 40 topics for the street-name data set and 5 topics for the brand-name data set, which we generalized into 8 and 5 topics of discussion, respectively. Four of the general themes of discussion in the brand-name corpus referenced drug use, while 2 themes of discussion in the street-name corpus referenced drug use. From the VADER scores, we found that both corpora were inclined toward positive sentiment. Adding the vectorized tweet text increased the accuracy of our models by around 40% compared with the models that did not incorporate the tweet text in both corpora. BERTopic was able to classify tweets well. As with LDA, the discussion using brand names was more similar between tweets than the discussion using street names. VADER scores could only be logically applied to the brand-name corpus because of the high prevalence of non-drug-related topics in the street-name data. Brand-name tweets either discussed drugs positively or negatively, with few posts having a neutral emotionality. From our machine learning models, engagement alone was not enough to predict the sentiment label; the added context from the tweets was needed to understand the emotionality of a tweet.

Enhancing Drug-Related Data Exchange: Advanced Technical Support for Interoperability.

This paper describes the technical support services developed to facilitate the usage of the Canadian Drug Ontology (OCRx). These services include a lookup service named PaperRx, which retrieves OCRx drug entities using description logic queries (DLqueries) conducted within OCRx's classified structure. Additionally, the services feature a SPARQL endpoint, an OWL file downloader, and a RESTful Application Programming Interface. This suite of services supports the application of the Findable, Accessible, Interoperable, and Reusable (FAIR) principles to OCRx.

Unobtrusive Monitoring of Clinical Deterioration in Smart Homes.

Clinical deterioration (CD) is the physiological decompensation that incurs care escalation, protracted hospital stays, or even death. The early warning score (EWS) calculates the occurrence of CD based on five vital signs. However, there are limited reports regarding EWS monitoring in smart home settings. This study aims to design a CD detection system for health monitoring at home (HM@H) that automatically identifies unstable vital signs and alarms the medical emergency team. We conduct a requirement analysis by interviewing experts. We use unified modeling language (UML) diagrams to define the behavioral and structural aspects of HM@H. We developed a prototype using a SQL-based database and Python to calculate the EWS in the front end. A team of five experts assessed the accuracy and validity of the designed system. The requirement analysis for four main users yielded 30 data elements and 10 functions. Three main components of HM@H are the graphical user interface (GUI), the application programming interface (API), and the server. Results show the possibility of using unobtrusive sensors to collect smart home residents' vital signs and calculate their EWS scores in real-time. However, further implementation with real data, for frail elderly and hospital-discharged patients is required.

Optimization of roofs with solar panels using Rao algorithms

Steel space truss roof (SSTR) systems are widely used in structures with large spans such as stadiums, sports halls, and shopping malls, as well as in industrial buildings such as factories, workshops, and production facilities. Solar panels are integrated into SSTR systems to enable these structures to generate energy and to increase the use of sustainable energy sources. With the installation of the solar panels, an additional load was placed on the roofs. However, this raises questions regarding whether existing roofs can withstand this additional weight without compromising their design. This study can be an important reference source for future truss roof designs by increasing sustainable energy use and functionally optimizing the SSTR. Another contribution of this study is that it directly contributes to real-world applications by ensuring that SSTR designs are more efficient and economical for engineering projects. For these purposes, this study presents the size optimization of the SSTR with and without solar panels using the Rao-1 and Rao-2 algorithms, which are metaheuristic algorithms known as the Rao algorithms. Thus, information can be provided regarding whether existing roofs can safely carry solar panels. To optimize the SSTR system, a computer code was created in MATLAB, which works effectively with Rao algorithms and SAP2000-s Open Applicable Programming Interface (OAPI) features and allows repetitive analysis. For size optimization of the SSTR, which consists of 1728 elements, the roof system was divided into three and six groups. Changes in the weight of the SSTR system in the different groups were investigated. The optimum design of both the three-group and six-group SSTR systems with and without solar panels was performed. Based on the results obtained from this study, it was concluded that the Rao-1 algorithm achieved more robust and stable results than the Rao-2 algorithm in both three and six-group SSTR. The SSTR system divided into six groups achieved the minimum weight compared with the SSTR system, which was divided into three groups. In this case, increasing the number of groups provided better results.

ChatGPT versus a customized AI chatbot (Anatbuddy) for anatomy education: A comparative pilot study.

Large Language Models (LLMs) have the potential to improve education by personalizing learning. However, ChatGPT-generated content has been criticized for sometimes producing false, biased, and/or hallucinatory information. To evaluate AI's ability to return clear and accurate anatomy information, this study generated a custom interactive and intelligent chatbot (Anatbuddy) through an Open AI Application Programming Interface (API) that enables seamless AI-driven interactions within a secured cloud infrastructure. Anatbuddy was programmed through a Retrieval Augmented Generation (RAG) method to provide context-aware responses to user queries based on a predetermined knowledge base. To compare their outputs, various queries (i.e., prompts) on thoracic anatomy (n = 18) were fed into Anatbuddy and ChatGPT 3.5. A panel comprising three experienced anatomists evaluated both tools' responses for factual accuracy, relevance, completeness, coherence, and fluency on a 5-point Likert scale. These ratings were reviewed by a third party blinded to the study, who revised and finalized scores as needed. Anatbuddy's factual accuracy (mean ± SD = 4.78/5.00 ± 0.43; median = 5.00) was rated significantly higher (U = 84, p = 0.01) than ChatGPT's accuracy (4.11 ± 0.83; median = 4.00). No statistically significant differences were detected between the chatbots for the other variables. Given ChatGPT's current content knowledge limitations, we strongly recommend the anatomy profession develop a custom AI chatbot for anatomy education utilizing a carefully curated knowledge base to ensure accuracy. Further research is needed to determine students' acceptance of custom chatbots for anatomy education and their influence on learning experiences and outcomes.

New Sustainable Fintech Business Models Created by Open Application Programming Interface Technology: A Case Study of Korea’s Open Banking Application Programming Interface Platform

Fintech facilitates financial inclusion by introducing new sustainable and accessible business models in developing countries. Open banking API technology is used by various fintech businesses in both developing and developed countries, with varying effects on financial markets. It changes the financial market distribution structure by separating financial product manufacturing and distribution and intensifying competition between traditional financial institutions and fintech companies. Fintech companies innovate by using this tool to create new business models. In December 2019, Korea established a standardized “open banking API platform”, sharing an interbank payment network with fintech companies. It has since shown explosive growth, surpassing the trading volume of the UK, the country that introduced open banking APIs for the first time. This study analyzes new business models fintech companies created using this API technology. Based on existing literature, statistics from the platform operator (the Korea Financial Telecommunications and Clearings Institute), and investigations of 30 Korean fintech mobile applications, this study analyzes new fintech business models that provide financial services using this platform. These companies create sustainable business models by combining multiple APIs. Four representative business models (simple funds transfer, simple payment, cross-border remittance, and asset management) are analyzed to reveal how fintech companies create business models with open APIs.

Assessing offshore wind plants for energy and green hydrogen production: A case study in the Mostaganem coastal region, Algeria

This study assesses the feasibility and strategic implications of establishing a 500 MW offshore wind farm utilizing fixed wind turbine technology along Algeria’s western coastal economic zone, with a focus on the Mostaganem region. The study recommends integrating 33 Vestas V-235 wind turbines, each boasting a 15 MW capacity to harness offshore wind resources for electricity and green hydrogen production. Utilizing the Wind Atlas Analysis and Application Program (WAsP), offshore wind potential is assessed based on meteorological data from a land-based station, overcoming the lack of in situ sea level measurements. Results show that the proposed wind farm could yield 1361.63 GWh/year of electricity at a cost of 0.89 $/kWh. Additionally, it would produce 28,571 tons of hydrogen at a cost of 2.12 $/kg. This initiative is anticipated to significantly reduce CO2 emissions, by approximately 595,450 tons/year, highlighting the effectiveness of offshore wind resources in promoting sustainable energy production and advancing green hydrogen as a viable energy carrier in this region.

Review of Baduanjin and resistance exercise for the mental health of patients with hematologic malignancies.

Patients with hematological tumors experience physical and psychological stress, and negative psychological states. Baduanjin, an emerging psychological rehabilitation method combined with resistance exercise, has received widespread attention. This study reviews the current status of the application of Baduanjin combined with resistance exercise in improving the negative psychological state of patients with hematological tumors and discusses its problems and prospects. Through a literature review and comprehensive analysis, the application of Baduanjin and resistance exercise in the psychological rehabilitation of patients with hematological tumors was identified and evaluated. The results showed that Baduanjin with resistance exercise had a positive effect on improving negative psychological states of patients with hematological tumors, which can alleviate anxiety, depression, and other adverse emotions, and improve quality of life. However, there is a lack of unified and standardized exercise intervention programs for practical application, and patient participation and compliance must be improved. Baduanjin combined with resistance exercise can potentially improve the negative psychological status of patients with hematological tumors; however, it is still necessary to further standardize and improve the exercise program improving patient participation and compliance. Future studies should strengthen theoretical exploration and empirical research, providing more effective psychological rehabilitation strategies for patients with hematological tumors.

Quantification of Automatic Speech Recognition System Performance on d/Deaf and Hard of Hearing Speech.

To evaluate the performance of commercial automatic speech recognition (ASR) systems on d/Deaf and hard-of-hearing (d/Dhh) speech. A corpus containing 850 audio files of d/Dhh and normal hearing (NH) speech from the University of Memphis Speech Perception Assessment Laboratory was tested on four speech-to-text application program interfaces (APIs): Amazon Web Services, Microsoft Azure, Google Chirp, and OpenAI Whisper. We quantified the Word Error Rate (WER) of API transcriptions for 24 d/Dhh and nine NH participants and performed subgroup analysis by speech intelligibility classification (SIC), hearing loss (HL) onset, and primary communication mode. Mean WER averaged across APIs was 10 times higher for the d/Dhh group (52.6%) than the NH group (5.0%). APIs performed significantly worse for "low" and "medium" SIC (85.9% and 46.6% WER, respectively) as compared to "high" SIC group (9.5% WER, comparable to NH group). APIs performed significantly worse for speakers with prelingual HL relative to postlingual HL (80.5% and 37.1% WER, respectively). APIs performed significantly worse for speakers primarily communicating with sign language (70.2% WER) relative to speakers with both oral and sign language communication (51.5%) or oral communication only (19.7%). Commercial ASR systems underperform for d/Dhh individuals, especially those with "low" and "medium" SIC, prelingual onset of HL, and sign language as primary communication mode. This contrasts with Big Tech companies' promises of accessibility, indicating the need for ASR systems ethically trained on heterogeneous d/Dhh speech data. 3 Laryngoscope, 2024.

Large Language Models in Otolaryngology Residency Admissions: A Random Sampling Analysis.

To investigate potential demographic bias in artificial intelligence (AI)-based simulations of otolaryngology, residency selection committee (RSC) members tasked with selecting one applicant among candidates with varied racial, gender, and sexual orientations. This study employed random sampling of simulated RSC member decisions using a novel Application Programming Interface (API) to virtually connect to OpenAI's Generative Pre-Trained Transformers (GPT-4 and GPT-4o). Simulated RSC members with diverse demographics were tasked with ranking to match 1 applicant among 10 with varied racial, gender, and sexual orientations. All applicants had identical qualifications; only demographics of the applicants and RSC members were varied for each simulation. Each RSC simulation ran 1000 times. Chi-square tests analyzed differences across categorical variables. GPT-4o simulations additionally requested a rationale for each decision. Simulated RSCs consistently showed racial, gender, and sexual orientation bias. Most applicant pairwise comparisons showed statistical significance (p < 0.05). White and Black RSCs exhibited greatest preference for applicants sharing their own demographic characteristics, favoring White and Black female applicants, respectively, over others (all pairwise p < 0.001). Asian male applicants consistently received lowest selection rates. Male RSCs favored White male and female applicants, while female RSCs preferred LGBTQIA+, White and Black female applicants (all p < 0.05). High socioeconomic status (SES) RSCs favored White female and LGBTQIA+ applicants, while low SES RSCs favored Black female and LGBTQIA+ applicants over others (all p < 0.001). Results from the newest iteration of the LLM, ChatGPT-4o, indicated evolved selection preferences favoring Black female and LGBTQIA+ applicants across all RSCs, with the rationale of prioritizing inclusivity given in >95% of such decisions. Utilizing publicly available LLMs to aid in otolaryngology residency selection may introduce significant racial, gender, and sexual orientation bias. Potential for significant and evolving LLM bias should be appreciated and minimized to promote a diverse and representative field of future otolaryngologists in alignment with current workforce data. N/A Laryngoscope, 2024.

Looking into the black mirror of the overdose crisis: Assessing the harms of collaborative surveillance technologies in the United States response.

Drug overdose is a leading cause of death among adults in the United States, prompting calls for more surveillance data and data sharing across public health and law enforcement to address the crisis. This paper integrates Black feminist science and technology studies (STS) into an anthropological analysis of the collision of public health, policing, and technology as embedded in the US National Overdose Response Strategy and its technological innovation, the Overdose Detection Mapping Application Program (ODMAP). The dystopian Netflix series "Black Mirror," which explores the seemingly useful but quietly destructive potential of technology, offers a lens through which to speculate upon and anticipate the harms of collaborative surveillance projects. Ultimately, I ask: are such technological interventions a benevolent approach to a public health crisis or are we looking into a black mirror of racialized surveillance and criminalization of overdose in the United States?

Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage.

The Human BioMolecular Atlas Program (HuBMAP) aims to construct a reference 3D structural, cellular, and molecular atlas of the healthy adult human body. The HuBMAP Data Portal (https://portal.hubmapconsortium.org) serves experimental datasets and supports data processing, search, filtering, and visualization. The Human Reference Atlas (HRA) Portal (https://humanatlas.io) provides open access to atlas data, code, procedures, and instructional materials. Experts from more than 20 consortia are collaborating to construct the HRA's Common Coordinate Framework (CCF), knowledge graphs, and tools that describe the multiscale structure of the human body (from organs and tissues down to cells, genes, and biomarkers) and to use the HRA to understand changes that occur at each of these levels with aging, disease, and other perturbations. The 6th release of the HRA v2.0 covers 36 organs with 4,499 unique anatomical structures, 1,195 cell types, and 2,089 biomarkers (e.g., genes, proteins, lipids) linked to ontologies and 2D/3D reference objects. New experimental data can be mapped into the HRA using (1) three cell type annotation tools (e.g., Azimuth) or (2) validated antibody panels (OMAPs), or (3) by registering tissue data spatially. This paper describes the HRA user stories, terminology, data formats, ontology validation, unified analysis workflows, user interfaces, instructional materials, application programming interface (APIs), flexible hybrid cloud infrastructure, and previews atlas usage applications.

Reusable rate of wooden structural members and its optimization determined by application programming interface (API)

ABSTRACT Carbon is stored within the structure of wood; however, burnt or landfilled wooden components releases carbon back into the atmosphere. Increasing the reusability rate of wooden components potentially prolongs carbon storage in wood. This study investigated the reusability rate of wooden brace members from a high-rise wooden building by using an application programming interface (API) integrated with Revit. This study demonstrated a method for improving the reusability rate by optimizing the brace cross-sections. The API assists designers in determining the reusability rate early in the design process by computing and representing reusability rates based on the designated converted members. Optimization enhanced the reusability of specific sections, with a reusability rate of approximately 80% achieved for the optimized cross-section area of 28.0 × 51.6 cm2 compared with 62% for the benchmark cross-section area of 38 × 38 cm2. Additionally, a preliminary structural assessment of the optimized wooden brace sections was conducted, which confirmed that the optimization did not affect the structural integrity. Consequently, selecting optimized wooden brace sections with high reusability rates by using the API extends the service life of wood and prolongs carbon sequestration, thereby mitigating carbon dioxide emission into the atmosphere.

Adolescents' unhealthy snacking behaviour during the school journey and the association with transport modes and food outlets along the school route.

Active transport to and from school provides an opportunity for adolescents to engage in physical activity, but travelling through an obesogenic environment may have unintended consequences on their snacking behaviour. This study aimed to: (1) identify whether adolescents' unhealthy snacking behaviour was associated with school transport modes and food outlets on their estimated school route and (2) explore whether food outlet density on the school route differed between school transport modes. Adolescents (n = 406; aged 15.1 ± 1.4 years; 50.7% boys; 63.5% New Zealand European) from all 12 secondary schools in Dunedin city, Aotearoa New Zealand, completed an online survey. School transport modes (active, motorised or mixed) and unhealthy snacking data were collected. Food outlet data were collected using Google Places Application Programming Interface (API). Home-to-school route and distance were estimated using geographical information system (GIS) analysis based on a walkable road network. Data were analysed using the chi-square test and logistic regression. Overall, 26.4% of adolescents reported purchasing and consuming unhealthy snacks or soft drinks on the way to school and 41.4% from school. The odds of unhealthy snacking during the school journey was higher among mixed transport users than active transport users on the way to (odds ratio (OR) = 2.73, 95% confidence interval (CI) = 1.39-5.36) and from school (OR = 2.68, 95% CI = 1.40-5.13). No differences were observed by food outlet type. There were no food outlets on the estimated school route for 44.8% of adolescents. The presence of more than one food outlet per kilometre of the estimated school route differed between active (38.7%), motorised (42.6%) and mixed transport users (46.4%; p = 0.006). School transport modes were significantly associated with adolescents' unhealthy snacking behaviour and food outlets on their school journey. Policy measures which minimise exposure to unhealthy food outlets may reduce unhealthy snacking among adolescents.

GPT is an effective tool for multilingual psychological text analysis

The social and behavioral sciences have been increasingly using automated text analysis to measure psychological constructs in text. We explore whether GPT, the large-language model (LLM) underlying the AI chatbot ChatGPT, can be used as a tool for automated psychological text analysis in several languages. Across 15 datasets (n = 47,925 manually annotated tweets and news headlines), we tested whether different versions of GPT (3.5 Turbo, 4, and 4 Turbo) can accurately detect psychological constructs (sentiment, discrete emotions, offensiveness, and moral foundations) across 12 languages. We found that GPT (r = 0.59 to 0.77) performed much better than English-language dictionary analysis (r = 0.20 to 0.30) at detecting psychological constructs as judged by manual annotators. GPT performed nearly as well as, and sometimes better than, several top-performing fine-tuned machine learning models. Moreover, GPT's performance improved across successive versions of the model, particularly for lesser-spoken languages, and became less expensive. Overall, GPT may be superior to many existing methods of automated text analysis, since it achieves relatively high accuracy across many languages, requires no training data, and is easy to use with simple prompts (e.g., "is this text negative?") and little coding experience. We provide sample code and a video tutorial for analyzing text with the GPT application programming interface. We argue that GPT and other LLMs help democratize automated text analysis by making advanced natural language processing capabilities more accessible, and may help facilitate more cross-linguistic research with understudied languages.

An Introduction to the Global Registry of Scientific Collections (GRSciColl)

The Global Registry of Scientific Collections (GRSciColl) is a comprehensive community-curated repository of scientific collections, their host institutions, and associated staff members. GRSciColl was adopted by the Global Biodiversity Information Facility (GBIF) in 2018, after being developed and hosted at the Smithsonian Institution. GRSciColl houses data on various institutional or personal scientific collections such as natural history collections, archeological collections, and cell culture collections. Users can access information about the collection location, content, accessibility status, coverage (e.g., geographic and taxonomic), and contacts. Additionally, it serves as a tool for enhancing data interoperability by including institution and collection codes and identifiers, as well as external identifiers like Wikidata Q numbers whenever feasible. The digitized specimens published on GBIF.org are linked with GRSciColl institutions and collections based on these codes and identifiers. These specimens are then aggregated to generate metrics and dashboards, which display customized multiple tables and visualization, regardless of how they were published on GBIF. Most of the data in GRSciColl is sourced from Index Herbariorum, the Integrated Digitized Biocollections (iDigBio), and national registries. The information can also be improved by direct contributions from a community of editors or the suggestions made by the users. As of 19 April 2024, GRSciColl provided information on 7,678 scientific collections and 7,906 institutions. However, GRSciColl includes numerous legacy entries, with many institutions lacking associated collections. This discrepancy arises partly from misregistrations, where institution entries should be collections. Additionally, some national registries or surveys provided institution-level information that did not align with the GRSciColl schema for collections. Furthermore, some records pertain to historical institutions that no longer maintain collections. The most up-to-date entries are discoverable and accessible via the website: https://scientific-collections.gbif.org and the GRSciColl Application Programming Interface (API). In this presentation, we will show you how to navigate the GRSciColl website and API to help you find relevant institutions, collections, and specimen records and look up codes and identifiers. We will cover any new functionality developed in the context of the GRSciColl 2023-2024 road map, which aims to support richer, structured descriptions as well as the ability to upload an inventory of species.