Package For Data Research Articles

RTF: an R package for modelling time course data.

The retarded transient function (RTF) approach serves as a complementary method to ordinary differential equations (ODEs) for modelling dynamics typically observed in cellular signalling processes. We introduce an R package that implements the RTF approach, originally implemented within the MATLAB-based Data2Dynamics modelling framework. This package facilitates the modelling of time and dose dependencies, and it includes the possibility of model reduction to minimize overfitting. It can be applied to experimental data or trajectories of ODE models to characterize their dynamics. Additionally, it can generate a low-dimensional representation based on the fitted RTF parameters of a set of time-resolved data, aiding in the identification of key targets of experimental perturbations. The R package RTF is available at https://github.com/kreutz-lab/RTF.

Программный комплекс обработки данных спутниковой альтиметрии космической геодезической системы «ГЕО-ИК-2»

The Central Research Institute for Machine Building JSC has created a software package for processing primary satellite altimetry data of the “GEO-IK-2” space geodetic system, adapted for high-performance computing systems. The software package performs step-by-step multi-level processing of measuring and auxiliary information with sequential recording of intermediate and final results in a database. The technology of processing level 0 data received from the spacecraft up to and including level 2 has been implemented. The article provides information about the input data used at different levels of processing, about the algorithms and functionality of the software package. In the process of debugging the software package on high-performance computing systems all the valid data of the “GEO-IK-2” space geodetic system from 2018 to 2022 were processed for the first time. The developed software package should serve as a prototype of the satellite altimetry data processing subsystem of the ground-based special complex of the promising new generation space geodetic system

TOMOMAN: a software package for large scale cryo-electron tomography data preprocessing, community data sharing, and collaborative computing.

Cryo-electron tomography (cryo-ET) and subtomogram averaging (STA) are becoming the preferred methodologies for investigating subcellular and macromolecular structures in native or near-native environments. While cryo-ET is amenable to a wide range of biological problems, these problems often have data processing requirements that need to be individually optimized, precluding the notion of a one-size-fits-all processing pipeline. Cryo-ET data processing is also becoming progressively more complex due to an increasing number of packages for each processing step. Though each package has its own strengths and weaknesses, independent development and different data formats makes them difficult to interface with one another. TOMOMAN (TOMOgram MANager) is an extensible package for streamlining the interoperability of packages, enabling users to develop project-specific processing workflows. TOMOMAN does this by maintaining an internal metadata format and wrapping external packages to manage and perform preprocessing, from raw tilt-series data to reconstructed tomograms. TOMOMAN can also export this metadata between various STA packages. TOMOMAN also includes tools for archiving projects to data repositories; allowing subsequent users to download TOMOMAN projects and directly resume processing where it was previously left off. By tracking essential metadata, TOMOMAN streamlines data sharing, which improves reproducibility of published results, reduces computational costs by minimizing reprocessing, and enables distributed cryo-ET projects between multiple groups and institutions. TOMOMAN provides a way for users to test different software packages to develop processing workflows that meet the specific needs of their biological questions and to distribute their results with the broader scientific community.

Expowo: An R package for mining global plant diversity and distribution data

AbstractPremiseData on plant distribution and diversity from natural history collections and taxonomic databases are increasingly becoming available online as exemplified by the Royal Botanic Gardens, Kew's Plants of the World Online (POWO) database. This growing accumulation of biodiversity information requires an advance in bioinformatic tools for accessing and processing the massive data for use in downstream science. We present herein expowo, an open‐source package that facilitates extracting and using botanical data from POWO.Methods and ResultsThe expowo package is implemented in R and designed to handle the entire vascular plant tree of life. It includes functions to readily distill taxonomic and distributional information about all families, genera, or species of vascular plants. It outputs a complete list of species in each genus of any plant family, with the associated original publication, synonyms, and distribution, and plots global maps of species richness at the country and botanical country levels, as well as graphs displaying species‐discovery accumulation curves and nomenclatural changes over time. To demonstrate expowo's strengths in producing easy‐to‐handle outputs, we also show empirical examples from a set of biodiverse countries and representative species‐rich and ecologically important angiosperm families.ConclusionsBy harnessing bioinformatic tools that accommodate varying levels of R programming proficiency, expowo functions assist users who have limited R programming expertise in efficiently distilling specific botanical information from online sources and producing maps and graphics for the further interpretation of biogeographic and taxonomic patterns.

Move2: R package for processing movement data

Abstract Studying animal movement is key to understanding ecological processes and studying the impacts of global change. The pivotal importance of collecting movement data drives technological development, and as a consequence, movement trajectories are gathered at unprecedented rates. For the analysis of the ever‐increasing volume of movement tracks, software packages that bridge between data storage, analysis and visualization are crucial. To facilitate analysis, it is key that the metadata, as well as projections, measurements units, and attributes for the track as a whole and auxiliary sensor measurements, are retained. At the same time, the software needs to be flexible enough to accommodate data from the multitude of different sources and tracking technologies. Here, we present the newly developed R package move2 that facilitates import, manipulation, analysis and visualization of movement tracks. The package enables the usage of new programming techniques developed in recent years and has been developed based on the experiences made in the past decade with the move package. The package is tightly integrated into the Movebank ‘ecosystem’ by being a key data format in MoveApps and providing a direct download option from movebank. However, move2 allows the integration of movement data sources of any provenance, providing a generic entry point for movement data analysis in R.

Entry-Level Occupational Therapy Student Volunteer Activities During COVID-19 and Beliefs about Practitioner Roles in Crisis Management

Background: Due to the global novel coronavirus disease 2019 (COVID-19) pandemic, many students in entry-level occupational therapy programs faced changes to their daily roles, habits, and routines. To understand beliefs about OT practitioner roles in a public health crisis, a cross-sectional study was designed to understand occupational therapy students (OTS) and occupational therapy assistant students (OTAS) volunteer activities during the COVID-19 pandemic. Methods: A survey was developed and distributed in April 2020, and closed after five weeks. Quantitative data was analyzed in Statistical Package for Social Sciences (Version 27) and open response data was categorized in Microsoft Excel. Results: Respondents included students from entry-level OT/OTA accredited programs such as occupational therapy doctorate, entry level master’s, bachelor’s to master’s and occupational therapy assistant (n=853, 2.8% of OTS/OTAS population). Nearly all respondents endorsed that OT practitioners have at least one role during a crisis (n=851, 99.8%) and after a crisis (n=848, 99.4%) like COVID-19. A minority of students volunteered in response to COVID-19 (n=60, 7.0%). Volunteer activities addressed clinical work, food insecurity, loneliness, social justice, healthcare workers, COVID-19 transmission, and personal protective equipment. Inspirations for volunteering included altruism, justice, personal responsibility, changes to routines, calls to action, and fulfilling obligations. Discussion: It is hoped that this study serves as a springboard for future research related to the OTS/OTAS response to COVID-19. Educators can prepare students for future public health emergencies by exposing them to information regarding OT’s role in disaster management.

GDPS: an open-source python-based software package for multi-GNSS data preprocessing

GDPS: an open-source python-based software package for multi-GNSS data preprocessing

CountASAP: A Lightweight, Easy to Use Python Package for Processing ASAPseq Data.

Declining sequencing costs coupled with the increasing availability of easy-to-use kits for the isolation of DNA and RNA transcripts from single cells have driven a rapid proliferation of studies centered around genomic and transcriptomic data. Simultaneously, a wealth of new techniques have been developed that utilize single cell technologies to interrogate a broad range of cell-biological processes. One recently developed technique, transposase-accessible chromatin with sequencing (ATAC) with select antigen profiling by sequencing (ASAPseq), provides a combination of chromatin accessibility assessments with measurements of cell-surface marker expression levels. While software exists for the characterization of these datasets, there currently exists no tool explicitly designed to reformat ASAP surface marker FASTQ data into a count matrix which can then be used for these downstream analyses. To address this, we created CountASAP, an easy-to-use Python package purposefully designed to transform FASTQ files from ASAP experiments into count matrices compatible with commonly-used downstream bioinformatic analysis packages. CountASAP takes advantage of the independence of the relevant data structures to perform fully parallelized matches of each sequenced read to user-supplied input ASAP oligos and unique cell-identifier sequences.

Geographic And Taxonomic Occurrence R-based Scrubbing (gatoRs): An R package and workflow for processing biodiversity data.

Digitized biodiversity data offer extensive information; however, obtaining and processing biodiversity data can be daunting. Complexities arise during data cleaning, such as identifying and removing problematic records. To address these issues, we created the R package Geographic And Taxonomic Occurrence R-based Scrubbing (gatoRs). The gatoRs workflow includes functions that streamline downloading records from the Global Biodiversity Information Facility (GBIF) and Integrated Digitized Biocollections (iDigBio). We also created functions to clean downloaded specimen records. Unlike previous R packages, gatoRs accounts for differences in download structure between GBIF and iDigBio and allows for user control via interactive cleaning steps. Our pipeline enables the scientific community to process biodiversity data efficiently and is accessible to the R coding novice. We anticipate that gatoRs will be useful for both established and beginning users. Furthermore, we expect our package will facilitate the introduction of biodiversity-related concepts into the classroom via the use of herbarium specimens.

Institutional Framework as a Determinant of Variations in Service Quality Between Public and Private Hospitals in Tanzania

Purpose of the article: This study was set to determine the influence of institutional framework on service quality in both private and public hospitals. The paper sought to establish the perception of respondents regarding the institutional factors that explain the existence of variations in service quality between public and private hospitals. The institutional framework aspects that were considered in comparing health service quality in public and private hospitals were: institutional culture, control, stability and structure. A descriptive survey design was used because the study sought to know the perception of respondents regarding the factors that influence quality of service in hospitals. Research methods: A multistage sampling method was used to select three public hospitals and three private hospitals from the health service sector. The three public hospitals were Muhimbili referral hospital in Dar es Salaam City, Dodoma regional referral hospital and Geita referral hospital in Geita municipality. Private hospitals included in the study were Bugando referral hospital in Mwanza City, St Francis referral hospital in Ifakara town in Morogoro region and Nkinga referral hospital in Tabora region. Medical doctors, nurses and patients were selected using a systematic random sampling method and patients were selected using a convenience sampling method. The total population for the study comprised 10,650 people (i.e. 2,610 doctors and nurses and 8,040 patients) and the selected sample size, which was determined using McCall’s Table was 400 people. Both primary and secondary data collection sources were used. Analysis of the quantitative data was done using the Statistical Package for Social Sciences (SPSS) and qualitative data were transcribed verbatim, coded and analysed manually. All ethical considerations were observed. Main findings: The findings revealed that private hospitals were better in cleanliness compared to public hospitals and physical arrangement was user friendly in private hospitals compared to public hospitals. There was also more control in private hospitals compared to public hospitals in management of resources and this demonstrates an application of strong institutional framework in private hospitals compared to public hospitals in terms of control, structure and culture. Likewise, there was a more friendly atmosphere in private hospitals compared to public hospitals. However, there was more stability in public hospitals compared to private hospitals in terms of financial stability, affordability of service and medical supplies. It is concluded that quality of service provided in private hospitals is higher compared to quality of service in public hospitals. The possible explanation for the causes for differences in levels of service quality could be the existence of strong institutional framework in private hospitals. It is recommended that for higher service quality, organizations should practice the institutional framework aspects in terms of culture, control, stability and structure.

Otargen: GraphQL-based R Package for Tidy Data Accessing and Processing From Open Targets Genetics.

Open Target Genetics is a comprehensive resource portal that offers variant-centric statistical evidence, enabling the prioritization of causal variants and the identification of potential drug targets. The portal uses GraphQL technology for efficient data query and provides endpoints for programmatic access for R and Python users. However, leveraging GraphQL for data retrieval can be challenging, time-consuming, and repetitive, requiring familiarity with the GraphQL query language and processing outputs in nested JSON(JavaScript Object Notation) format into tidy data tables. Therefore, developing open-source tools are required to simplify data retrieval processes to integrate valuable genetic information into data-driven target discovery pipelines seamlessly. otargen is an open-source R package designed to make data retrieval and analysis from the Open Target Genetics portal as simple as possible for R users. The package offers a suite of functions covering all query types, allowing streamlined data access in a tidy table format. By executing only a single line of code, theotargen users avoid the repetitive scripting of complex GraphQL queries, including the post-processing steps. In addition, otargen contains convenient plotting functions to visualize and gain insights from complex data tables returned by several key functions. otargen is available at https://amirfeizi.github.io/otargen/.

Analyzing the Usefulness of Demonstration Plots as Perceived by the Farmers in District Faisalabad, Pakistan

Agricultural extension is the driver of motivation of farmers towards the adoption of agricultural innovations. Therefore, different extension methods are employed to enhance the effectiveness of extension services and bring change in farmers' behaviour. In this study, an attempt is made to explore the use of demonstration plots as perceived by the farmers in district Faisalabad. In this study, a total of 130 randomly selected farmers, five Agriculture Officers (AOs) and five Field Assistants (FAs) participated. From the farmers, data were collected through face-to-face interview techniques on a structured and validated interview schedule whereas from the extension field staff, the data were collected on an interview guide. Quantitative data were analysed using Statistical Package for Social Sciences (SPSS) and qualitative data were analysed using a content analysis approach. Qualitative results indicated that the demonstration plots helped farmers to change their behaviour from a traditional farming approach to a modern approach of farming for more production and profits. Group discussion, method and result demonstration methods were focused areas while demonstrating technologies at the demonstration plot. Farmers found demonstration plots useful for the reasons of being active, cooperation from the extension staff, equal treatment to the farmers, participation of farmers in group meetings, enhanced learning and motivating farmers to adopt the latest technologies. Considering the usefulness, there is a need to enhance the number of demonstration plots. More research studies are also recommended to be conducted in other districts of the Punjab s well to investigate the further room for improvement and strengthen the extension services for farmers.

NOMspectra: An Open-Source Python Package for Processing High Resolution Mass Spectrometry Data on Natural Organic Matter.

The present study introduces NOMspectra, a Python package for processing high resolution mass spectrometry data on complex systems of natural organic matter (NOM). NOM is characterized by multicomponent composition reflected as thousands of signals producing very complex patterns in high resolution mass spectra. This complexity sets special demands on the methods of data processing used for analysis. The developed NOMspectra package offers a comprehensive workflow for processing, analyzing, and visualizing information-rich mass spectra of NOM and HS including algorithms for filtering spectra, recalibrating, and assigning elemental compositions to molecular ions. Additionally, the package includes functions for calculating various molecular descriptors and methods for data visualization. A graphical user interface (GUI) has been developed to make a user-friendly interface for the proposed package.

Geographic and Taxonomic Occurrence R-Based Scrubbing (gatoRs): An R package and workflow for processing biodiversity data

Geographic and Taxonomic Occurrence R-Based Scrubbing (gatoRs): An R package and workflow for processing biodiversity data

Code and Data - Geographic and Taxonomic Occurrence R-Based Scrubbing (gatoRs): An R package and workflow for processing biodiversity data

Code and Data - Geographic and Taxonomic Occurrence R-Based Scrubbing (gatoRs): An R package and workflow for processing biodiversity data

Pyseistr: A Python Package for Structural Denoising and Interpolation of Multichannel Seismic Data

AbstractNew sensing techniques like the nodal geophones and distributed acoustic sensing enable a spatial sampling ratio that was unprecedentedly high in earthquake seismology. The much higher sampling of seismic wavefields that is close to the level in exploration seismology calls for a unified processing approach for multichannel seismic data regardless of the research interest, for example, oil and gas oriented or earthquake-study oriented. Here, we present the first Python package for multichannel seismic data that benefits both communities, that is, exploration and earthquake seismology, called Pyseistr. The Pyseistr is a Python package that is designed to make full use of the structural patterns in multichannel seismic data to facilitate the data processing. The Pyseistr package currently includes several fundamental functions like slope estimation, structural mean and median filtering, and structural reconstruction of missing data. The Pyseistr package is continuously developed to include more functions that benefit both exploration and earthquake communities.

Processing of Accelerometry Data with GGIR in Motor Activity Research Consortium for Health

The Mobile Motor Activity Research Consortium for Health (mMARCH) is a collaborative network of clinical and community studies that employ common digital mobile protocols and collect common clinical and biological measures across participating studies. At a high level, a key scientific goal which spans mMARCH studies is to develop a better understanding of the interrelationships between physical activity (PA), sleep (SL), and circadian rhythmicity (CR) and mental and physical health in children, adolescents, and adults. mMARCH studies employ wrist-worn accelerometry to obtain objective measures of PA/SL/CR. However, there is currently no consensus on a standard data processing pipeline for raw accelerometry data and few open-source tools which facilitate their development. The R package GGIR is the most prominent open-source software package for processing raw accelerometry data, offering great functionality and substantial user flexibility. However, even with GGIR, processing done in a harmonized and reproducible fashion across multiple analytical centers requires a nontrivial amount of expertise combined with a careful implementation. In addition, there are many statistical methods useful for analyzing PA/SL/CR patterns using accelerometry data which are implemented in non-GGIR R packages, including methods from multivariate statistics, functional data analysis, distributional data analysis, and time series analyses. To address the issues of multisite harmonization and additional feature creation, mMARCH developed a streamlined harmonized and reproducible pipeline for loading and cleaning raw accelerometry data via GGIR, merging GGIR, and non-GGIR features of PA/SL/CR together, implementing several additional data and feature quality checks, and performing multiple analyses including Joint and Individual Variation Explained, an unsupervised machine learning dimension reduction technique that identifies latent factors capturing joint across and individual to each of three domains of PA/SL/CR. The pipeline is easily modified to calculate additional features of interest, and allows for studies not affiliated with mMARCH to apply a pipeline which facilitates direct comparisons of scientific results in published work by mMARCH studies. This manuscript describes the pipeline and illustrates the use of combined GGIR and non-GGIR features by applying Joint and Individual Variation Explained to the accelerometry component of CoLaus|PsyCoLaus, one of mMARCH sites. The pipeline is publicly available via open-source R package mMARCH.AC.

AniMotum, an R package for animal movement data: Rapid quality control, behavioural estimation and simulation

Abstract Animal tracking data are indispensable for understanding the ecology, behaviour and physiology of mobile or cryptic species. Meaningful signals in these data can be obscured by noise due to imperfect measurement technologies, requiring rigorous quality control as part of any comprehensive analysis. State–space models are powerful tools that separate signal from noise. These tools are ideal for quality control of error‐prone location data and for inferring where animals are and what they are doing when they record or transmit other information. However, these statistical models can be challenging and time‐consuming to fit to diverse animal tracking data sets. The R package aniMotum eases the tasks of conducting quality control on and inference of changes in movement from animal tracking data. This is achieved via: (1) a simple but extensible workflow that accommodates both novice and experienced users; (2) automated processes that alleviate complexity from data processing and model specification/fitting steps; (3) simple movement models coupled with a powerful numerical optimization approach for rapid and reliable model fitting. We highlight aniMotum's capabilities through three applications to real animal tracking data. Full R code for these and additional applications is included as Supporting Information, so users can gain a deeper understanding of how to use aniMotum for their own analyses.

An analysis of knowledge management practices at lilongwe university of agriculture and natural resources, Malawi

Knowledge management constitutes all the processes in which knowledge assets are mobilised to create value for an organisation. Knowledge is derived from information, which is processed data. In universities, knowledge is generated through research. The current study targeted academic and senior administrative staff at Lilongwe University of Agriculture and Natural Resources (LUANAR). It was underpinned by the Socialisation Externalisation Combination and Internalisation (SECI) Knowledge Creation Model. Using a mixed-method approach, a survey and case study designs were used to collect data while a census was used to get a sample of 173. Quantitative data were analysed using the Statistical Package for Social Sciences (SPSS) and qualitative data were analysed thematically. Findings of the study revealed that LUANAR, Bunda Campus creates theses, dissertations, emails, memos, policies and minutes as the main types of explicit knowledge while expertise, skills and experiences as the main types of tacit knowledge. However, the curation of plant and animal specimens was identified as a special type of knowledge the University creates. The knowledge is mainly shared through emails and meetings which enhance research, teaching and facilitate learning, help in the curriculum development process and improve decision-making. On the other hand, the study unveiled that inadequate awareness about the importance of knowledge management, lack of top management support, lack of knowledge management policy and lack of knowledge management advocate/champion impede the knowledge management practices. Therefore, the study recommends that top management should initially identify a knowledge management advocate/champion to extensively conduct knowledge management awareness and drive the knowledge management agenda at the institution. The knowledge management champion should also initiate the development of a knowledge management policy and the establishment of a knowledge management unit to coordinate knowledge management activities.

Curation of BIDS (CuBIDS): A workflow and software package for streamlining reproducible curation of large BIDS datasets

The Brain Imaging Data Structure (BIDS) is a specification accompanied by a software ecosystem that was designed to create reproducible and automated workflows for processing neuroimaging data. BIDS Apps flexibly build workflows based on the metadata detected in a dataset. However, even BIDS valid metadata can include incorrect values or omissions that result in inconsistent processing across sessions. Additionally, in large-scale, heterogeneous neuroimaging datasets, hidden variability in metadata is difficult to detect and classify. To address these challenges, we created a Python-based software package titled “Curation of BIDS” (CuBIDS), which provides an intuitive workflow that helps users validate and manage the curation of their neuroimaging datasets. CuBIDS includes a robust implementation of BIDS validation that scales to large samples and incorporates DataLad––a version control software package for data––as an optional dependency to ensure reproducibility and provenance tracking throughout the entire curation process. CuBIDS provides tools to help users perform quality control on their images’ metadata and identify unique combinations of imaging parameters. Users can then execute BIDS Apps on a subset of participants that represent the full range of acquisition parameters that are present, accelerating pipeline testing on large datasets.