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THE IMPACT OF HUMAN RESOURCE MANAGEMENT ON ORGANIZATIONAL PERFORMANCE(MBA THESIS)

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Hasret Balcioglu

The study aims to identify the level of effectiveness in Human Resource Management (HRM) and its impact on employees' satisfaction in the banking sector of Jordan. A questionnaire was developed and administered to 540 employees in the banking sector of Jordan out of which 406 were returned to test research hypotheses. The findings revealed a high level of effectiveness of all HRM practices combined and for four individual practices (HR planning, staffing, training & development and performance appraisal); while, a medium level of compensation effectiveness was yielded. It has been found that employees' satisfaction level was medium. A strong positive relationship has been identified between the effectiveness of HRM and employees' satisfaction. The study recommended improving financial compensation system of banks, which would positively increase the level of employees' satisfaction. On the other hand, comparative studies between Islamic banks and commercial banks are highly suggested.

Bukola Babayeju

In the altering competitive environment, human resources are considered as one significant source for competitive advantage. The human resource systems can subsidize to persistent competitive advantage through enabling the expansion of competencies that the organization needs for its development. The findings revealed that the positive statistical relationships were found between the human resource management practices and organizational performance. The fundamental linkage between HRM practices and organizational performance will enable the HR managers to design packages that will bring out better operating results to attain higher organizational performance. When the organizations grip, gadget and evaluate these practices effectively, then ultimately it will culminate at the higher performance in the organization. Data was collected from the employees working in commercial banks in Dera Ismail Khan. The same data was then analyzed by using diverse statistical tools (correlation & regression analysis).

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In the present era of dynamic business environment, organisations are being posed with new challenges every day. Globalisation, Liberalisation, Mergers and Acquisitions, advances in communication and information technology, changing mix and values of workforce have created enormous challenges for business organisations. The sustainability and success of business organisations depends upon, how effectively these organisations use their resources. To cope with these challenges, human resources play very crucial role. Human Resource Management focuses on optimal utilisation and management of human resource capital to achieve maximum output. Effective human resource management encompasses wide spectrum of approaches and strategies starting from HR planning, Recruitment and Selection till the Separation of employees. The present study attempts to gain insight into HRM practices being followed by Public and Private sector banks and judge the satisfaction level of employees from these practices. A structured questionnaire is constructed containing various questions on three major dimensions of HRM i.e Recruitment & selection, Salary and Compensation, Training and Development. Survey on sample of 100 respondents from both the Public and Private sector Banks has been administered for the purpose of this study. The data is analysed using statistical measure of Chi Square test using SPSS. Keywords: Human Resource Management, Employee Satisfaction, Recruitment and Selection, Salary and Compensation, Training & Development, Chi Square Test

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Artificial intelligence in human resource development: An umbrella review protocol

Roles Conceptualization, Data curation, Methodology, Project administration, Supervision, Validation, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Human Resource Development, The University of Texas at Tyler, Tyler, Texas, United States of America

Roles Conceptualization, Data curation, Methodology, Software, Supervision, Validation, Writing – review & editing

Roles Conceptualization, Data curation, Validation, Writing – review & editing

• Sangok Yoo, 
• Kim Nimon, 
• Sanket Ramchandra Patole

• Published: September 9, 2024
• https://doi.org/10.1371/journal.pone.0310125
• Peer Review
• Reader Comments

The recent surge in artificial intelligence (AI) has significantly transformed work dynamics, particularly in human resource development (HRD) and related domains. Scholars, recognizing the significant potential of AI in HRD functions and processes, have contributed to the growing body of literature reviews on AI in HRD and related domains. Despite the valuable insights provided by these individual reviews, the challenge of collectively interpreting them within the HRD domain remains unresolved. This protocol outlines the methodology for an umbrella review aiming to systematically synthesize existing reviews on AI in HRD. The review seeks to address key research questions regarding AI’s contributions to HRD functions and processes, as well as the opportunities and threats associated with its implementation by employing a technology-aided systematic approach. The coding framework will be used to synthesize the contents of the selected systematic reviews such as their search strategies, data synthesis approaches, and HRD-related findings. The results of this umbrella review are expected to provide insights for HRD scholars and practitioners, promoting continuous improvement in AI-driven HRD initiatives. This protocol is preregistered on the Open Science Framework ( https://doi.org/10.17605/OSF.IO/Z8NM6 ) on May 27, 2024.

Citation: Yoo S, Nimon K, Patole SR (2024) Artificial intelligence in human resource development: An umbrella review protocol. PLoS ONE 19(9): e0310125. https://doi.org/10.1371/journal.pone.0310125

Editor: Juan Correa, Critical Centrality Institute, MEXICO

Received: February 8, 2024; Accepted: August 23, 2024; Published: September 9, 2024

Copyright: © 2024 Yoo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All supplementary files are available in an open-access repository: https://osf.io/af6d7/ .

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Artificial intelligence (AI) refers to the ability of machines to perform near or human-like functions, such as learning, interaction, and problem-solving, encompassing the culmination of computers, computer-related technologies, machines, and information communication technology innovations and developments, giving computers the ability to perform [ 1 , 2 ]. The AI market is anticipated to reach a $407 billion by 2027, indicating substantial growth from its estimated revenue of $86.9 billion in 2022. This surge is projected to make a 21% net contribution to the United States GDP by 2030, highlighting the profound impact of AI on economic growth [ 3 ]. Reasonably, a considerable 64% of businesses believe artificial intelligence will enhance their overall productivity [ 3 ]. Furthermore, according to an annual McKinsey Global Survey conducted in mid-April 2023, generative AI (Gen AI) has captured significant attention across the business landscape. Individuals from various regions, industries, and seniority levels are incorporating Gen AI into their professional and personal activities in their workplaces [ 4 ].

The recent proliferation of AI has dramatically changed the way we work [ 2 , 5 ]. In the field of human resource development (HRD) and related areas, the integration of AI presents opportunities to optimize talent acquisition, streamline learning and development initiatives, and enhance the strategic values of HRD in the workplace [ 5 , 6 ]. The far-reaching impact of AI underscores the need for a nuanced understanding of its role in HRD functions.

In academia, a burgeoning interest in AI in the workplace is evident through the growing body of research, leading to a surge of literature reviews focused on AI in HRD and related areas (e.g., [ 2 , 5 , 7 ]). For example, [ 5 ] conducted a critical review of the literature on AI and its impact on workplace outcomes, specifically within HR functions. [ 6 ] delved into the literature on AI applications, with a particular emphasis on the learning and development function. Despite the valuable contributions of these endeavors, the question of how these individual reviews can be collectively interpreted within the field of HRD remains unanswered.

To attain a comprehensive understanding of the rapidly expanding knowledge base, there is a need to systematically synthesize existing reviews on AI in HRD and related areas. An umbrella review, representing the highest level of evidence, offers a comprehensive overview of existing systematic reviews in a specific field. It enables scholars to compare the findings of systematic reviews relevant to a specific review question [ 8 , 9 ].

Hence, the proposed review outlined in this protocol aims to unveil patterns, trends, and gaps in the current understanding of AI in HRD literature. Additionally, we expect that this umbrella review will provide HRD scholars and practitioners with insights into the evolving concepts and practices associated with AI in HRD, thereby promoting continuous improvement in AI-driven HRD initiatives. The key research questions to be addressed in our umbrella review are:

• RQ1 : How does AI contribute to HRD functions and processes ?
• RQ2 : What are the opportunities and threats of implementing AI in HRD ?

In pursuit of the objective, this protocol proposes a technology-aided umbrella review process to synthesize systematic literature reviews on AI in the field of HRD and related areas. This systematic approach is designed to alleviate subjectivity in the review process, including the selection of search terms, thereby enhancing the rigor and objectivity of this umbrella review.

Materials and methods

Design and setting of the study.

This technology-aided umbrella review protocol adheres to the guidelines of PRISMA-P (Preferred reporting items for systematic review and meta-analysis protocols), serving as a guide for planning and documenting review methods [ 10 , 11 ]. The completed PRISMA-P checklist to confirm essential and minimum components of a systematic review is available in the S1 File . To achieve a comprehensive understanding of AI implementation in HRD, this protocol is designed to systematically incorporate existing systematic literature reviews on AI in HRD and related areas, mitigating subjective decision-making during review conduct [ 10 ]. This protocol is pre-registered on the Open Science Framework (OSF): https://doi.org/10.17605/OSF.IO/Z8NM6 . In the main research using this protocol, we plan to incorporate guidelines from the updated PRISMA 2020 statement to ensure comprehensive reporting of our umbrella review [ 12 ].

Database and data management

A structured search will be conducted in the Scopus and Web of Science databases, selected for their relevance to the field of study and comprehensive coverage. The review process, encompassing screening, will be coordinated utilizing Rayyan to ensure a systematic and efficient workflow [ 13 ].

Search strategy

Keywords to create a comprehensive search string that will be used to search systematic reviews for this umbrella review were collected. Table 1 describes the final search sub-strings of each component. AI-, HRD-, and SLR-related strings include search terms combined using the Boolean operator OR. In the final search string, the Boolean operator AND will be used to combine the three sub-strings. As our umbrella review aims to synthesize existing systematic literature reviews, the SLR-related string includes one search term that narrows the scope of our project. The specific search term identification strategy and term matching details are illustrated in the supplementary files ( S2 and S3 Files).

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https://doi.org/10.1371/journal.pone.0310125.t001

Screening process

We will employ a two-stage screening strategy. First, the relevance of each article will be evaluated based on its title and abstract. Articles that meet the exclusion criteria will be excluded. The second stage will evaluate the relevance of articles based on full texts using the inclusion and exclusion criteria. The screening process will be coordinated using Rayyan.

Eligibility criteria

To uphold consistency and reproducibility in the screening process among coders, the inclusion and exclusion criteria are established. First, eligible studies are systemic literature reviews specifically focused on AI in the field of HRD and related areas. This inclusion criterion aims to contribute to the synthesis of high-quality evidence and insights derived from rigorous research methodologies. The initial search will be confined to peer-reviewed journal articles and conference proceedings written in English and published from 1995 onwards, aligning with the search practices in previous literature reviews on AI (e.g., [ 5 – 7 ]).

Regarding the exclusion criteria, first, studies that do not explicitly explore AI-related technology will be excluded, ensuring a targeted exploration of the subject matter. Second, studies unrelated to a workplace setting will be excluded, as this umbrella review is specifically tailored to the application of AI in the workplace. Third, non-systemic literature reviews, which lack a structured and systematic approach, will also be excluded to maintain the methodological rigor of the review. Fourth, as this umbrella review specifically targets systemic literature reviews, studies employing meta-analysis as the primary research methodology will not be considered for inclusion. Finally, as explained in the inclusion criteria, book chapters and non-referred articles will be excluded to maintain the scholarly standard and reliability of the information under consideration.

Data extraction

We will use Rayyan to extract data. Extraction fields will be set up with the relevant information from the studies, and Rayyan’s tagging and coding features will be used to categorize and organize the extracted data. Disagreements will be discussed and resolved using Rayyan’s conflict resolution feature. The extraction fields for recording the finally selected systematic review studies will include:

• Full study citation
• The number of citations
• Title, abstract, and keywords
• Publication outlet (e.g., journal) and year
• Database, journal types, research context, scope
• Search terms and string(s)
• Scope of AI-related technologies (e.g., AI, machine learning, large language model)
• Scope of HRD-related functions (e.g., training & development, organizational development)
• Analysis approaches (e.g., bibliometrics, contents analysis, topic modeling, clustering)
• HRD-related areas in which AI applies to
• The benefits and possibility of AI adoption in HRD functions
• The enablers and obstacles of AI adoption in HRD functions
• Contributing factors to the effectiveness of AI-based HRD practices
• Other key contents/findings of the study (e.g., Future research directions)

Data synthesis

Thematic coding will be a crucial part of this umbrella review, focusing on discerning patterns in the implementation of AI within HRD. By employing an HRD framework, the goal of the thematic coding is to systematically categorize and analyze relevant literature to identify recurrent themes and trends in AI adoption across various HRD contexts. Furthermore, thematic coding facilitates the identification of key opportunities and challenges associated with AI implementation in HRD. The synthesis can highlight common issues faced by organizations integrating AI into HRD practices and, conversely, showcase successful strategies and innovative approaches. Ultimately, the thematic coding approach provides a comprehensive understanding of the current state of AI in HRD and sets the stage for suggesting future research directions and practical recommendations to enhance AI-driven HRD initiatives.

In addition to thematic coding, the data synthesis plan incorporates descriptive statistics. Descriptive statistics involves quantifying the occurrence of specific themes or concepts related to AI implementation in HRD across the selected systematic literature reviews. Specifically, frequency analysis helps to identify the prevalence of certain trends, challenges, or opportunities and visualization techniques can be employed to present these findings in a clear and accessible manner. R will be utilized for statistical analysis and visualization. We plan to use the base package [ 14 ] for statistical analysis and ggplot2 [ 15 ] for visualization.

Conclusions

This protocol will guide an umbrella review process to synthesize existing systematic reviews on AI in HRD. This umbrella review aims to explore the intersection of AI and HRD using existing reviews in the field of HRD and related areas. The anticipated outcomes of this umbrella review are intended to unveil patterns, opportunities, and threats of AI implementation in HRD. They will provide insights into AI-driven HRD initiatives. All data and analyses will be placed in an open-access repository, and the URL will be provided in the final manuscript.

Despite the expected contributions of this project, several limitations should be discussed. First, the protocol’s reliance on systematic literature reviews may introduce a potential bias, as certain valuable perspectives from non-systematic reviews or other types of reviews may be overlooked. Second, the scope of the review is contingent upon the availability of relevant literature published in English from 1995 onwards; this temporal and linguistic restriction may exclude valuable insights from non-English publications or earlier works that could contribute to a more nuanced understanding of the historical development of AI in HRD. Lastly, it should be mentioned that as AI-related technology is evolving rapidly future updates to this umbrella review will be necessary to ensure that it includes the most updated trends and practices.

Supporting information

S1 file. prisma-p checklist ( https://osf.io/2935t )..

https://doi.org/10.1371/journal.pone.0310125.s001

S2 File. Search term identification strategy ( https://osf.io/vgck2 ).

https://doi.org/10.1371/journal.pone.0310125.s002

S3 File. VosViewer keywords and search terms matching ( https://osf.io/nxc7v ).

* Note : All supplementary files are available in an open-access repository: https://osf.io/af6d7/ .

https://doi.org/10.1371/journal.pone.0310125.s003

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• 4. Chui, M., Yee, L., Hall, B., & Singla, A. (2023, August 1). The state of AI in 2023: Generative AI’s breakout year. QuantumBlack, AI by McKinsey. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year#/ .
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Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers

• Chenglei Si , Diyi Yang , Tatsunori Hashimoto
• Published 6 September 2024
• Computer Science, Linguistics

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Mammalian genome research resources available from the National BioResource Project in Japan

• Open access
• Published: 11 September 2024

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• Saori Mizuno-Iijima 1 ,
• Shoko Kawamoto   ORCID: orcid.org/0000-0002-6404-3443 2 ,
• Masahide Asano   ORCID: orcid.org/0000-0002-9087-6481 3 ,
• Tomoji Mashimo   ORCID: orcid.org/0000-0001-7543-7301 4 ,
• Shigeharu Wakana   ORCID: orcid.org/0000-0002-7037-8354 5 ,
• Katsuki Nakamura   ORCID: orcid.org/0000-0002-2376-8755 6 ,
• Ken-ichi Nishijima   ORCID: orcid.org/0000-0002-7189-465X 7 ,
• Hitoshi Okamoto   ORCID: orcid.org/0000-0002-7512-8549 8 ,
• Kuniaki Saito   ORCID: orcid.org/0000-0001-8233-9283 9 ,
• Sawako Yoshina 10 ,
• Yoshihiro Miwa   ORCID: orcid.org/0000-0003-4827-5022 11 ,
• Yukio Nakamura 12 ,
• Moriya Ohkuma 13 &
• Atsushi Yoshiki   ORCID: orcid.org/0000-0002-9450-5151 1  

Mammalian genome research has conventionally involved mice and rats as model organisms for humans. Given the recent advances in life science research, to understand complex and higher-order biological phenomena and to elucidate pathologies and develop therapies to promote human health and overcome diseases, it is necessary to utilize not only mice and rats but also other bioresources such as standardized genetic materials and appropriate cell lines in order to gain deeper molecular and cellular insights. The Japanese bioresource infrastructure program called the National BioResource Project (NBRP) systematically collects, preserves, controls the quality, and provides bioresources for use in life science research worldwide. In this review, based on information from a database of papers related to NBRP bioresources, we present the bioresources that have proved useful for mammalian genome research, including mice, rats, other animal resources; DNA-related materials; and human/animal cells and microbes.

Avoid common mistakes on your manuscript.

Introduction

Bioresources represent a fundamental component of the research infrastructure that supports the life sciences. The development of bioresources is a lengthy and meticulous process, and they serve as the foundation for discoveries and future research endeavors. The sharing of these resources among researchers is crucial for the advancement of research and development. In response to this need, the Ministry of Education, Culture, Sports, Science and Technology (MEXT) established the National BioResource Project (NBRP) in FY2002. This initiative aims to create a systematic framework for the collection, preservation, and distribution of bioresources, with a particular emphasis on those requiring strategic development at the national level. This review synthesizes information on bioresources that have proven valuable for mammalian genome research. These resources include mice, rats, other animal resources, DNA-related materials, and human/animal cells and microbes. This review draws upon data extracted from a comprehensive database of publications related to NBRP bioresources, offering insights into the current landscape and potential future directions of bioresource utilization in genomic research.

The Core Center of NBRP-Mice is the Experimental Animal Division of the RIKEN BioResource Research Center (RIKEN BRC) (Mizuno-Iijima et al. 2022 ), which has collected mouse strains developed mainly in Japan that have been reported in academic publications (Fig.  1 ) in order to preserve unique and cutting-edge mouse models (Table  1 ). NBRP-Mice performs rigorous quality control, including microbial and genetic testing to ensure the reproducibility of animal experiments. As one of the international hubs for mouse resources, we continuously participate in global mouse resource networks such as International Mouse Strain Resource (IMSR), International Mouse Phenotyping Consortium (IMPC), Asian Mouse Mutagenesis & Resource Association (AMMRA) and Asian Network of Research Resource Centers (ANRRC). NBRP-Mice has archived approximately 10,000 mouse strains, most of which are genetically modified mice, as tools for gene functional analysis tools, including Cre/Flp drivers, fluorescent and luminescent reporters, and human disease models such as the third-generation Alzheimer’s disease model with genetic mutations of Alzheimer’s disease patients (Sasaguri et al. 2018 ; Sato et al. 2021 ) as well as a novel Down syndrome mouse model using a mouse artificial chromosome-based chromosome engineering technique (Kazuki et al. 2020 ). Information on the available mouse strains is disseminated through the NBRP-Mice website ( https://mus.brc.riken.jp/ ) and the IMSR ( https://www.findmice.org/ ), an all-encompassing database of the major international mouse repositories. NBRP-Mice receives requests from research communities worldwide (Fig.  2 ) and distributes live mice, frozen embryos/sperm, recovered litters from frozen embryos/sperm, and organ/tissue/genomic DNA. To date, NBRP-Mice has distributed mouse resources to researchers at 712 domestic and 1003 overseas academic and industry organizations in 44 countries. Outstanding research results from studies using NBRP-Mice have been published in 1300 papers so far (Fig.  3 ) and registered in our database.

Breakdown of collected mouse strains in NBRP-Mice (FY2017-FY2023)

Breakdown of distributed mouse strains from NBRP-Mice (FY2017-FY2023)

Number of publications using NBRP-Mice mouse resources

In addition to genetically modified strains, NBRP-Mice also preserves wild-derived inbred strains such as the Japanese subspecies MSM/MsRbrc (MSM, RBRC00209) and JF1/MsRbrc (JF1, RBRC00639). The enormous number of genomic polymorphisms present between these subspecies and classical inbred strains is useful for understanding the genomic function and diverse biological phenotypes in mice and other mammals including humans as well. MoG + ( https://molossinus.brc.riken.jp/mogplus/ ) (Takada et al. 2022 ) is a mouse genome database designed to support research using Mus musculus subspecies, with a focus on comparisons between mouse subspecies and classical inbred strains. MoG + provides access to more than 40 million polymorphisms found by comparative genomic analysis of 10 Asian wild-derived strains, including Mus musculus molossinus -derived MSM and JF1; Mus musculus musculus -derived KJR/Ms (RBRC00655), SWN/Ms (RBRC00654), CHD/Ms (RBRC00738), NJL/Ms (RBRC00207), and BLG2/Ms (RBRC00653); Mus musculus domesticus -derived BFM/2Ms (RBRC00659) and PGN2/Ms (RBRC00667); and Mus musculus castaneus -derived HMI/Ms (RBRC00657), all of which are available from NBRP-Mice, while linking to mouse resource catalog information, human genome variations, and so on. In addition, public genome polymorphism information on 36 classical inbred strains is stored. MoG + has been utilized for disease and phenotypic analysis (Takeishi et al. 2022 ; Yasuda et al. 2020 ). Reproductive engineering techniques are being developed to support research involving subspecies mouse strains (Hasegawa et al. 2021 ; Hirose et al. 2017 ; Mochida et al. 2014 ). An example of the use of subspecies strains is gene expression analysis based on single nucleotide polymorphisms (SNPs) in F1 hybrid mice (Saito et al. 2024 ; Yagi et al. 2017 , 2020 ). Genomic DNA derived from these multiple subspecies strains has also been used (Bamunusinghe et al. 2013 , 2016 , 2018 ).

The Core Center of NBRP-Rats is the Institute of Laboratory Animals, Graduate School of Medicine, Kyoto University. Scientists conducting research involving rats have conventionally accumulated physiological and pharmacological data. Compared with mice, rats have typically been used for experiments involving drug administration and surgery because of their larger body size and for behavioral studies because of their higher intelligence. NBRP-Rats collects rat strains that have been maintained by individual scientists or laboratories in Japan and overseas, making over 800 strains available, including inbred and genetically modified strains, and has provided about 1500 strains so far (Table  2 ). NBRP-Rats has common inbred strains, spontaneous mutants, congenic strains, recombinant inbred strains, transgenic and newly genetically modified strains, and so on. Recently, genome-edited rats have also been collected. Available strains can be accessed via the NBRP-Rats website ( https://www.anim.med.kyoto-u.ac.jp/nbr/Default.aspx ). NBRP-Rats provides reference information for strain selection, including the results of approximately 200 strains on 109 phenotypic measurements for physiological and behavioral parameters such as body weight at various ages, blood pressure, spontaneous locomotor activity, and the passive avoidance test ( https://www.anim.med.kyoto-u.ac.jp/nbr/phenome.aspx ); the phylogenetic tree of 132 rat strains based on genomic profile data ( https://www.anim.med.kyoto-u.ac.jp/nbr/phylo.aspx ); and a pedigree-like charting tool showing 357 simple sequence length polymorphism (SSLP) marker differences for 179 genotyped rat strains ( https://www.anim.med.kyoto-u.ac.jp/nbr/pedigree/sb.aspx ). NBRP-Rats has also worked to develop reproductive technology and has established optimal freezing and thawing methods for sperm, stable in vitro fertilization (IVF) technology (Honda et al. 2019 ; Mochida et al. 2024 ; Morita et al. 2023 ) and is making progress in the cryopreservation of rat strains.

At the Institute of Medical Science, The University of Tokyo, which is an NBRP-Rats Sub-Core Center, the development of novel genome-edited rat models is underway. Three severely immunodeficient (SCID) rat strains generated using the CRISPR/Cas9 system [F344- Il2rg em1Iexas (NBRP Rat No: 0883), F344- Rag2 em1Iexas (NBRP Rat No: 0894), and F344- Il2rg/Rag2 em1Iexas (NBRP Rat No: 0895)] (Mashimo et al. 2010 ) have already been made available to researchers ( https://www.ims.u-tokyo.ac.jp/animal-genetics/scid/index_en.html ). SCID rats can be transplanted with human induced pluripotent stem cells (iPS cells), cancer cells, liver cells, and so on. Therefore, SCID rats are useful for analyzing human physiological functions in vivo (Eguchi et al. 2022 ; Lahr et al. 2021 ; Miyasaka et al. 2022 ). In fact, the demand from translational research and regenerative medicine is increasing every year. In addition, NBRP-Rats has been collecting and developing new Cre driver rats, and 22 Cre driver strains are available for conditional studies. In the near future, a database of Cre driver rats will be made available on the website, and the results of comprehensive expression analysis, local expression analysis using adeno-associated virus (AAV), behavioral analysis, and magnetic resonance imaging (MRI) analysis will be published as phenotype information.

The other animal resources

In addition to laboratory mice and rats, the NBRP provides Aged mice and Japanese macaques as mammalian resources for researchers in Japan. NBRP-Aged mice provides three standard mouse strains [C57BL/6J, C57BL/6N (B6N), BALB/cA] that are bred for about 2 years in a uniform environment under strict microbiological control. Aged mice are expected to be used for various aging research, such as elucidating the mechanisms of the aging process, aging control, and aging-related diseases. The Japanese macaque is a species of macaque monkey. Due to their close relationship with humans, Japanese macaques are used mainly in the field of neuroscience but also in the fields of infectious diseases, immunology, and regenerative medicine. Compared with other Southeast Asian macaque species such as rhesus and cynomolgus macaques, Japanese macaques have a curious and calm temperament as well as highly developed cognitive and learning abilities, making them suitable for research on higher brain functions and fine motor functions that require complex task acquisition (Kubota et al. 2024 ; Kumano and Uka 2024 ; Sasaki et al. 2024 ). In fact, Japanese macaques have contributed to the elucidation of the pathogenesis and pathology of neurological disorders such as dementia and Parkinson's disease as well as to the development of treatments to restore neurological functions (Chiken et al. 2021 ; Darbin et al. 2022 ; Oyama et al. 2023 ).

The NBRP supports life science research by providing a total of 12 animal bioresources for which whole-genome sequencing has been performed, which is necessary for analyzing orthologs of human genes (Table 3 ). For example, chickens and quails have been used in a variety of fields, particularly in embryology. In vitro culture of primordial germ cells (PGCs) is now possible in 20 chicken strains, and gene transfer and genome editing of chickens using such cells are under development. To meet the demand for fluorescent live imaging of developmental processes, NBRP-Chickens & Quails provides a transgenic chicken strain (pLSi/ΔAeGFP-TG) that expresses enhanced green fluorescent protein (eGFP) almost systemically under the control of the chicken β-actin promoter (Motono et al. 2010 ; Tsujino et al. 2019 ) and a PRDM14-eGFP knock-in chicken strain that express eGFP under the control of the chicken endogenous PRDM14 promoter (Hagihara et al. 2020 ). As a tool for generating new models, Cas9-T2A-mCherry transgenic chickens that expresses Cas9 under the control of the homeostatic human EF1α promoter are also available. NBRP-Chickens & Quails releases the results of quail genome analysis as the Quail Genome Browser ( http://viewer.shigen.info/uzura/index.php ). A PGK:H2B-chFP-TG quail strain that expresses mCherry throughout the body (Huss et al. 2015 ) is used for live imaging of developmental processes, with the advantage of easy microsurgery in embryos (Haneda et al. 2024 ; Yoshihi et al. 2020 ).

Zebrafish are transparent throughout embryogenesis, are easy to breed, have a short life cycle, and are amenable to mutation and genetic modification. NBRP-Zebrafish has about 400 mutant lines and about 1800 transgenic lines. The neuronal composition and neural mechanisms of the zebrafish brain are highly conserved with those of humans, making zebrafish particularly useful in the field of neuroscience. Tg(CM-isl1:GFP), which expresses green fluorescent protein (GFP) in hindbrain motor neurons (Higashijima et al. 2020 ), is useful for imaging neural circuit networks (Derrick et al. 2024 ; Zhao et al. 2024 ). Tg(vglut2a:loxP-DsRed-loxP-GFP), which expresses DsRed in glutamatergic neurons prior to Cre recombinase exposure and GFP in the Cre-recombined cells (Satou et al. 2012 ), has been used to elucidate the mechanisms of neural circuit construction processes (Itoh et al. 2024 ; Schmidt et al. 2024 ) and the relationship between behavior/movement and neuronal activity (Berg et al. 2023 ; Carbo-Tano et al. 2023 ).

Drosophila is used to study life phenomena and in disease research because of its many similarities to humans, including gene homology and basic biological mechanisms. The NBRP- Drosophila conserves many useful mutants for life science research, including about 14,000 RNAi strains and about 30 FlyCas9 strains. CAS-001 (Kondo and Ueda 2013 ), a transgenic line expressing the Cas9 protein, can be crossed with various guide RNA strains to generate gene knock-out mutant flies with high efficiency. The generation of mutant strains with CAS-001 is versatile and has been reported in the development of novel models for metabolic disease research (Martelli et al. 2024 ) and biochemical research (Banreti et al. 2022 ). GAL4 enhancer trap insertion strains (Hayashi et al. 2002 ) are useful for tissue-specific expression and knock-down using the GAL4/UAS system, and approximately 4200 such lines have been conserved. A traffic jam-GAL4 driver strain (DGRC#104055), which is expressed in all stages of ovarian follicle cells at every developmental stage, has been used by many scientists in various fields as well as for the elucidation of reproductive mechanisms (Mallart et al. 2024 ; Taniguchi and Igaki 2023 ).

Ca enorhabditis elegans is useful for understanding gene function because C.elegans has only about 1000 somatic cells, the cell lineage of which has been extensively described, and detailed descriptions of its morphology have been made through serial electron microscopy images. NBRP- C. elegans has about half the number of deletion mutants as there are genes in wild-type C. elegans. The drp-1 deletion mutant (tm1108), an ortholog of the human DMNL1 gene that functions in mitochondrial division, has been used to study mitochondria-related diseases (Chen et al. 2024 ) and aging (Sharifi et al. 2024 ). The brc-1 deletion mutant (tm1145), an ortholog of the human BRCA1 gene that is involved in DNA repair and has been reported to be associated with several diseases including cancer, is used to elucidate DNA repair mechanisms (Bujarrabal-Dueso et al. 2023 ; Wang et al. 2023 ).

DNA-related materials

The Gene Engineering Division, RIKEN BRC provides genetic materials such as plasmids, expression and reporter vectors, and comprehensive clone sets of cDNAs and genomic DNAs. To date, NBRP-DNA-related materials have conserved about 3.8 million resources including about 3400 research tools for imaging and genome editing, about 600,000 human cDNA/genomic DNA clones, about 350,000 mouse cDNA/genomic DNA clones, and 1.3 million animal cDNA/genomic DNA clones (Table  4 ). Mammalian expression vectors for protein production and gene expression, mouse and rat BAC clones, and fluorescent and luminescent protein expression vectors for imaging are used to generate genetically modified mice, rats, and mammalian cells. BAC clones can be searched with the BAC browser, using gene symbols as keywords, and the physical location of BAC clones on the genome can be confirmed. The BAC browsers for B6N and MSM mouse strains ( http://analysis2.nig.ac.jp/mouseBrowser/cgi-bin/index.cgi?org=mm ), for F344/Stm and LE/Stm rat strains ( http://analysis2.nig.ac.jp/ratBrowser/cgi-bin/index.cgi?org=rn ), and for Japanese macaque ( http://analysis2.nig.ac.jp/jmonkeyBrowser/cgi-bin/index.cgi?org=jm ) are published on the website. The B6N BAC library consists of 128,000 clones representing 90.2% of the actual coverage of the haploid genome. The MSM BAC library consists of 200,000 BAC clones.

NBRP-DNA-related materials collects useful tools that are expected to be requested by researchers in the future using artificial intelligence technology. Regarding genome-editing tools, Cas9-poly(A) expressing improved plasmid [T7-NLS hCas9-pA (RDB13130)] (Yoshimi et al. 2016 ) and the expression vector of sgRNA with hSpCas9-Cdt1(mouse) fusion protein [px330-mC (RDB14406)] (Mizuno-Iijima et al. 2021 ) are available. In addition to conventionally used fluorescent and luminescent proteins, NBRP-DNA-related materials also provides the highly photostable and bright GFP StayGold [e.g., (n1)StayGold/pRSET (RDB19605) (Hirano et al. 2022 ) and pRSETB/mStayGold (RDB20214) (Ando et al. 2023 )], and the novel yellow fluorescent protein Achilles [Achilles/pRSETB (RDB15982)] (Yoshioka-Kobayashi et al. 2020 ) to meet the needs of researchers. The highly luminescent luciferases Akaluc [pcDNA3 Venus-Akaluc (RDB15781)] (Iwano et al. 2018 ) and oFluc [pPmat Luc1 (RDB14359)] (Ogoh et al. 2020 ) are also provided. Reporter mice expressing Akaluc or oFluc are available from NBRP-Mice [C57BL/6J- Gt(ROSA)26Sor em13(CAG-luc)Rbrc /#77 (RBRC10451), C57BL/6J- Gt(ROSA)26Sor em14(CAG-Venus/Akaluc)Rbrc /#87 (RBRC10858), C57BL/6J- Gt(ROSA)26Sor em13.1(CAG-luc)Rbrc /#77 (RBRC10919), and C57BL/6J- Gt(ROSA)26Sor em17.1(CAG-Venus/Akaluc)Rbrc /#11 (RBRC10921)] (Nakashiba et al. 2023 ).

Human and animal cells

The Cell Engineering Division, RIKEN BRC has collected many cultured cell lines, including about 4600 human cell lines and about 3800 animal cell lines. Mouse embryonic stem (ES) cell lines with germline-transmission [e.g., B6J-S1 UTR (AES0140), B6NJ-22 UTR (AES0141) (Tanimoto et al. 2008 ), and EGR-G101 (AES0182) (Fujihara et al. 2013 )] are used to generate genetically engineered mice, using both conventional gene targeting and genome-editing technologies (Hasan et al. 2021 ; Noda et al. 2017 , 2019 ; Serizawa et al. 2019 ). As mentioned above, because SCID rat strains are transplantable with human cells, human iPS cells and cancer cells have been transplanted and used for in vivo functional analysis. Some users have reported research results in combination with human iPS cells derived from healthy volunteers provided by NBRP-Human and animal cells (Gima et al. 2024 ; Hayashi et al. 2024 ; Tada et al. 2022 ). NBRP-Human and animal cells also provides human iPS cell lines derived from patients with various diseases (Table  5 ). These disease-specific iPS cell lines are expected to be further used for research with a view toward clinical application.

NBRP-Human and animal cells performs genetic analysis of some disease-specific iPS cells to promote their use. For example, for iPS cells derived from amyotrophic lateral sclerosis, commonly referred to as ALS, the results of target sequence analysis for the casual genes ( SOD1 / TARDBP / ALS10 / TDP-43 genes) have been published on the RIKEN BRC website ( https://cell.brc.riken.jp/en/ga-als ). As for iPS cells derived from spinocerebellar degeneration, the results of the number of repeated sequences in related 8-gene regions have been published ( https://cell.brc.riken.jp/en/ga-scd ). In addition to the cell material itself, human iPS cell lines (disease-specific iPS cells and healthy human iPS cells) provide clinical information such as sex, age and the names of diseases, and researchers can use these data upon appropriate application and review.

The NBRP also manages general microbes (bacteria, archaea, yeast, and filamentous fungi), prokaryotes ( Escherichia coli and Bacillus subtilis ), pathogenic eukaryotic microbes, pathogenic bacteria, and human pathogenic viruses). The most popular paper among NBRP-Mice users’ results sorted by Citation Index is one that identified and isolated 11 gut bacterial strains that strongly induce IFNγ-producing CD8T cells and showed that administration of these strains inhibited infection and tumor growth in mouse strains (Tanoue et al. 2019 ). The influence of the gut microbiota and skin microbiota on phenotype is a topic of great interest, and more results are expected in the future.

Research Resource Circulation

All the article information discussed in this paper, which is based on studies using NBRP resources, is registered and accessible in the Research Resource Circulation (RRC) database ( https://rrc.nbrp.jp/ ) (Fig.  4 ). RRC is an integrated database that connects published research outcomes to the specific bioresources used in those studies. Its primary objective is to aggregate and organize information on published papers and patents that have utilized these resources and to make this information publicly available along with statistical data, thereby enhancing the information content of each resource and promoting their utilization.

Research Resource Circulation (RRC) Database: a system for tracking and analyzing the utilization of NBRP bioresources and research outcomes

A key feature of the RRC is assigning a unique RRC ID to each paper corresponding PubMed information, strain names, citation metrics, and other relevant data. Development of the RRC began in 2007, and it presently contains entries for approximately 55,000 papers and 1300 patents. Users can easily register papers using PubMed IDs or DOIs. Moreover, the RRC is linked with NCBI’s LinkOut service, enabling resource links to be added to corresponding papers in PubMed.

The NBRP provides useful biological resources, technologies, and information for mammalian genome research both in Japan and overseas, and many users’ research results have been reported. Not only the use of individual bioresources but also the combination of bioresources has been reported by many users. We encourage global scientists to conduct a comprehensive search with biological resources of high quality available from NBRP. In addition, we are constantly updating the information on each bioresource to meet the needs of increasingly sophisticated and complex research while reviewing the latest research trends and increasing the number of stored resources. We hope you will make effective use of the NBRP to advance mammalian genome research.

Data availability

No datasets were generated or analysed during the current study.

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Acknowledgements

We sincerely thank Dr. Yuji Kohara, Program Director of NBRP; Dr. Yuichi Obata, Program Officer of NBRP; and Dr. Toshihiko Shiroishi, Director of RIKEN BRC for their thoughtful leadership and guidance. We are grateful to Drs. Ayumi Koso and Asuka Mukai, NBRP Public Relations Team for providing accurate information and advice. The NBRP is supported by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan.

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Experimental Animal Division, RIKEN BioResource Research Center, Tsukuba, Ibaraki, 305-0074, Japan

Saori Mizuno-Iijima & Atsushi Yoshiki

Department of Informatics, National Institute of Genetics, Mishima, Shizuoka, 411-8540, Japan

Shoko Kawamoto

Institute of Laboratory Animals, Graduate School of Medicine, Kyoto University, Sakyo-ku, Kyoto, 606-8501, Japan

Masahide Asano

Division of Animal Genetics, Laboratory Animal Research Center, Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo, 108-8639, Japan

Tomoji Mashimo

Department of Animal Experimentation, Foundation for Biomedical Research and Innovation at Kobe, Kobe, Hyogo, 650-0047, Japan

Shigeharu Wakana

Center for the Evolutionary Origins of Human Behavior, Kyoto University, Inuyama, Aichi, 484-8506, Japan

Katsuki Nakamura

Avian Bioscience Research Center, Graduate School of Bioagricultural Sciences, Nagoya University, Nagoya, Aichi, 464-8601, Japan

Ken-ichi Nishijima

RIKEN Center for Brain Science, Wako, Saitama, 351-0198, Japan

Hitoshi Okamoto

Department of Chromosome Science, National Institute of Genetics, Mishima, Shizuoka, 411-8540, Japan

Kuniaki Saito

Department of Physiology, Tokyo Women’s Medical University School of Medicine, Shinjuku-ku, Tokyo, 162-8666, Japan

Sawako Yoshina

Gene Engineering Division, RIKEN BioResource Research Center, Tsukuba, Ibaraki, 305-0074, Japan

Yoshihiro Miwa

Cell Engineering Division, RIKEN BioResource Research Center, Tsukuba, Ibaraki, 305-0074, Japan

Yukio Nakamura

Microbe Division/Japan Collection of Microorganisms, RIKEN BioResource Research Center, Tsukuba, Ibaraki, 305-0074, Japan

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S.M.-I. made a conceptualization and wrote the main manuscript text. S.K. prepared Fig. 4. M.A., T.M., S.W., K.N., K.-I.N., H.O., K.S., S.Y., Y.M., Y.N., and M.O. provided accurate information and data. A.Y. supervised the manuscripts. All authors reviewed and revised the manuscript.

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Mizuno-Iijima, S., Kawamoto, S., Asano, M. et al. Mammalian genome research resources available from the National BioResource Project in Japan. Mamm Genome (2024). https://doi.org/10.1007/s00335-024-10063-2

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Received : 16 July 2024

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Published : 11 September 2024

DOI : https://doi.org/10.1007/s00335-024-10063-2

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