{"id":{"repo_id":"edinburgh","oai_identifier":"oai:era.ed.ac.uk:1842/44418"},"canonical_url":"https://search.dev.ndltd.org/etd/edinburgh/oai:era.ed.ac.uk:1842/44418","repository":{"repo_id":"edinburgh","name":"University of Edinburgh","base_url":"https://era.ed.ac.uk/server/oai/request"},"display":{"title":"Curating and summarising data from animal research on neurodevelopment at scale","abstract":"Neurodevelopmental conditions (NDCs) are a heterogeneous group of lifelong disabilities affecting brain development. While many NDCs arise from a complex combination of genetic and environmental factors, some NDCs are monogenic; animals with targeted alterations in homologous genes can be used as models to provide a foundational knowledge of the biological mechanisms underlying brain development and avenues for testing potential therapeutics. Complexities in animal research, including construct validity, statistical errors, heterogeneity in experimentaldesign, and threats to internal validity mean, it is important to consider and critique all available evidence. Systematic review is an established evidence synthesis method used to identify, curate, and summarise published research. Systematic reviews of animal research can identify gaps in knowledge, explain heterogeneity in findings, work towards the Replacement, Reduction and Refinement of animals in research (the 3Rs), and be used to make evidence-based decisions. However, the rapid rate of research publication and breadth of research scope limits the feasibility of conducting reviews. Additionally, narrow review topics only give a snapshot of available research. Systematic maps are an alternative approach to evidence synthesis, summarising information at a higher level, allowing broader overviews of research landscapes, and offering a starting point for more in-depth reviews. However, to enable summarisation of large research areas, mapping approaches require streamlined workflows enabling information from various sources to be combined. Additionally – by making use of automation approaches such as code scripts, existing data accessed via application programming interface (API), text mining, and machine learning – we may be able to increase the feasibility of producing systematic maps. In this thesis, I investigate how evidence synthesis methods and automation approaches can be combined to create a systematic map and make the most use out of research using animals to study genetic NDCs. First, I reviewed previously conducted systematic reviews in this research area to understand their aims and methods, including assessing their reporting quality using a draft extension of the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines (PRISMA-Pre). Secondly, I developed and validated automation approaches to enable fast identification genetic NDC animal research, including comprehensive search strategies, a machine learning classifier to identify relevant publications, and named entity recognition to extract key details relating to animal models. Thirdly, I used these automation approaches as the foundation for a continually updating systematic map of research in this area, integrating data from additional scientific data sources, including OpenAlex, and using existing software to extract further data including data and code availability statements and reporting of measures to reduce the risk of bias in animal experiments. Finally, I used my systematic map as the starting point for an in-depth systematic review and validated the systematic map as a comprehensive resource by comparing it to manual systematic searching and screening. Through my review, I also identified key action points for authors of animal studies on how experiments could be reported to improve transparency and better enable their use in evidence synthesis. Overall, my work has contributed to the understanding of how evidence synthesis and automation methods can be integrated to summarise evidence and maximise effective use of data from animal experiments to develop our understanding of how genetics may impact neurodevelopment.","abstract_html":"Neurodevelopmental conditions (NDCs) are a heterogeneous group of lifelong disabilities affecting brain development. While many NDCs arise from a complex combination of genetic and environmental factors, some NDCs are monogenic; animals with targeted alterations in homologous genes can be used as models to provide a foundational knowledge of the biological mechanisms underlying brain development and avenues for testing potential therapeutics. Complexities in animal research, including construct validity, statistical errors, heterogeneity in experimentaldesign, and threats to internal validity mean, it is important to consider and critique all available evidence. Systematic review is an established evidence synthesis method used to identify, curate, and summarise published research. Systematic reviews of animal research can identify gaps in knowledge, explain heterogeneity in findings, work towards the Replacement, Reduction and Refinement of animals in research (the 3Rs), and be used to make evidence-based decisions. However, the rapid rate of research publication and breadth of research scope limits the feasibility of conducting reviews. Additionally, narrow review topics only give a snapshot of available research. Systematic maps are an alternative approach to evidence synthesis, summarising information at a higher level, allowing broader overviews of research landscapes, and offering a starting point for more in-depth reviews. However, to enable summarisation of large research areas, mapping approaches require streamlined workflows enabling information from various sources to be combined. Additionally – by making use of automation approaches such as code scripts, existing data accessed via application programming interface (API), text mining, and machine learning – we may be able to increase the feasibility of producing systematic maps. In this thesis, I investigate how evidence synthesis methods and automation approaches can be combined to create a systematic map and make the most use out of research using animals to study genetic NDCs. First, I reviewed previously conducted systematic reviews in this research area to understand their aims and methods, including assessing their reporting quality using a draft extension of the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines (PRISMA-Pre). Secondly, I developed and validated automation approaches to enable fast identification genetic NDC animal research, including comprehensive search strategies, a machine learning classifier to identify relevant publications, and named entity recognition to extract key details relating to animal models. Thirdly, I used these automation approaches as the foundation for a continually updating systematic map of research in this area, integrating data from additional scientific data sources, including OpenAlex, and using existing software to extract further data including data and code availability statements and reporting of measures to reduce the risk of bias in animal experiments. Finally, I used my systematic map as the starting point for an in-depth systematic review and validated the systematic map as a comprehensive resource by comparing it to manual systematic searching and screening. Through my review, I also identified key action points for authors of animal studies on how experiments could be reported to improve transparency and better enable their use in evidence synthesis. Overall, my work has contributed to the understanding of how evidence synthesis and automation methods can be integrated to summarise evidence and maximise effective use of data from animal experiments to develop our understanding of how genetics may impact neurodevelopment.","abstract_has_math":false,"creators":["Wilson, Emma"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["MacLeod, Malcolm","Kind, Peter","Sena, Emily"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12-12","date_published":"2025-12-12","updated_at":"2026-07-24T02:14:11Z","subjects":["Neurodevelopmental conditions (NDCs)","Animal models","animal research","Systematic review","evidence synthesis","systematic map","text mining","machine learning","API-based data integration"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://doi.org/10.7488/era/6938"],"render_values":[{"text":"https://doi.org/10.7488/era/6938","href":"https://doi.org/10.7488/era/6938","code":true}]}]},"links":{"outbound_url":"https://era.ed.ac.uk/handle/1842/44418","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["MacLeod, Malcolm","Kind, Peter","Sena, Emily"]},{"key":"dc:creator","label":"Author","values":["Wilson, Emma"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-02-17T14:56:18Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-12-12"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["PhD Doctor of Philosophy"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Neurodevelopmental conditions (NDCs)","Animal models","animal research","Systematic review","evidence synthesis","systematic map","text mining","machine learning","API-based data integration"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://era.ed.ac.uk/handle/1842/44418","https://doi.org/10.7488/era/6938"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Neurodevelopmental conditions (NDCs) are a heterogeneous group of lifelong disabilities affecting brain development. While many NDCs arise from a complex combination of genetic and environmental factors, some NDCs are monogenic; animals with targeted alterations in homologous genes can be used as models to provide a foundational knowledge of the biological mechanisms underlying brain development and avenues for testing potential therapeutics. Complexities in animal research, including construct validity, statistical errors, heterogeneity in experimentaldesign, and threats to internal validity mean, it is important to consider and critique all available evidence. Systematic review is an established evidence synthesis method used to identify, curate, and summarise published research. Systematic reviews of animal research can identify gaps in knowledge, explain heterogeneity in findings, work towards the Replacement, Reduction and Refinement of animals in research (the 3Rs), and be used to make evidence-based decisions. However, the rapid rate of research publication and breadth of research scope limits the feasibility of conducting reviews. Additionally, narrow review topics only give a snapshot of available research. Systematic maps are an alternative approach to evidence synthesis, summarising information at a higher level, allowing broader overviews of research landscapes, and offering a starting point for more in-depth reviews. However, to enable summarisation of large research areas, mapping approaches require streamlined workflows enabling information from various sources to be combined. Additionally – by making use of automation approaches such as code scripts, existing data accessed via application programming interface (API), text mining, and machine learning – we may be able to increase the feasibility of producing systematic maps. In this thesis, I investigate how evidence synthesis methods and automation approaches can be combined to create a systematic map and make the most use out of research using animals to study genetic NDCs. First, I reviewed previously conducted systematic reviews in this research area to understand their aims and methods, including assessing their reporting quality using a draft extension of the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines (PRISMA-Pre). Secondly, I developed and validated automation approaches to enable fast identification genetic NDC animal research, including comprehensive search strategies, a machine learning classifier to identify relevant publications, and named entity recognition to extract key details relating to animal models. Thirdly, I used these automation approaches as the foundation for a continually updating systematic map of research in this area, integrating data from additional scientific data sources, including OpenAlex, and using existing software to extract further data including data and code availability statements and reporting of measures to reduce the risk of bias in animal experiments. Finally, I used my systematic map as the starting point for an in-depth systematic review and validated the systematic map as a comprehensive resource by comparing it to manual systematic searching and screening. Through my review, I also identified key action points for authors of animal studies on how experiments could be reported to improve transparency and better enable their use in evidence synthesis. Overall, my work has contributed to the understanding of how evidence synthesis and automation methods can be integrated to summarise evidence and maximise effective use of data from animal experiments to develop our understanding of how genetics may impact neurodevelopment."]},{"key":"dc:title","label":"Title","values":["Curating and summarising data from animal research on neurodevelopment at scale"]}]}],"canonical_facts":{"dc:contributor.advisor":["MacLeod, Malcolm","Kind, Peter","Sena, Emily"],"dc:creator":["Wilson, Emma"],"dc:date.accessioned":["2026-02-17T14:56:18Z"],"dc:date.issued":["2025-12-12"],"dc:description.abstract":["Neurodevelopmental conditions (NDCs) are a heterogeneous group of lifelong disabilities affecting brain development. While many NDCs arise from a complex combination of genetic and environmental factors, some NDCs are monogenic; animals with targeted alterations in homologous genes can be used as models to provide a foundational knowledge of the biological mechanisms underlying brain development and avenues for testing potential therapeutics. Complexities in animal research, including construct validity, statistical errors, heterogeneity in experimentaldesign, and threats to internal validity mean, it is important to consider and critique all available evidence. Systematic review is an established evidence synthesis method used to identify, curate, and summarise published research. Systematic reviews of animal research can identify gaps in knowledge, explain heterogeneity in findings, work towards the Replacement, Reduction and Refinement of animals in research (the 3Rs), and be used to make evidence-based decisions. However, the rapid rate of research publication and breadth of research scope limits the feasibility of conducting reviews. Additionally, narrow review topics only give a snapshot of available research. Systematic maps are an alternative approach to evidence synthesis, summarising information at a higher level, allowing broader overviews of research landscapes, and offering a starting point for more in-depth reviews. However, to enable summarisation of large research areas, mapping approaches require streamlined workflows enabling information from various sources to be combined. Additionally – by making use of automation approaches such as code scripts, existing data accessed via application programming interface (API), text mining, and machine learning – we may be able to increase the feasibility of producing systematic maps. In this thesis, I investigate how evidence synthesis methods and automation approaches can be combined to create a systematic map and make the most use out of research using animals to study genetic NDCs. First, I reviewed previously conducted systematic reviews in this research area to understand their aims and methods, including assessing their reporting quality using a draft extension of the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines (PRISMA-Pre). Secondly, I developed and validated automation approaches to enable fast identification genetic NDC animal research, including comprehensive search strategies, a machine learning classifier to identify relevant publications, and named entity recognition to extract key details relating to animal models. Thirdly, I used these automation approaches as the foundation for a continually updating systematic map of research in this area, integrating data from additional scientific data sources, including OpenAlex, and using existing software to extract further data including data and code availability statements and reporting of measures to reduce the risk of bias in animal experiments. Finally, I used my systematic map as the starting point for an in-depth systematic review and validated the systematic map as a comprehensive resource by comparing it to manual systematic searching and screening. Through my review, I also identified key action points for authors of animal studies on how experiments could be reported to improve transparency and better enable their use in evidence synthesis. Overall, my work has contributed to the understanding of how evidence synthesis and automation methods can be integrated to summarise evidence and maximise effective use of data from animal experiments to develop our understanding of how genetics may impact neurodevelopment."],"dc:identifier.uri":["https://era.ed.ac.uk/handle/1842/44418","https://doi.org/10.7488/era/6938"],"dc:language.iso":["en"],"dc:subject":["Neurodevelopmental conditions (NDCs)","Animal models","animal research","Systematic review","evidence synthesis","systematic map","text mining","machine learning","API-based data integration"],"dc:title":["Curating and summarising data from animal research on neurodevelopment at scale"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["Doctoral"],"dc:type.qualificationname":["PhD Doctor of Philosophy"]},"updated_at":"2026-07-24T02:14:11Z"}