Dataset Card for Dataset Name

The datascience instruct dataset is a collection of question answers based around various topics of datascience.

  Dataset Description

The primary goal of this dataset is to fine tune base LLMs for responding to data science queries. According to our observation, most base LLMs (2B to 7B) are good in understanding data science concepts but they lack in responding step by step. This dataset contains well structured user agent interaction… See the full description on the dataset page: https://huggingface.co/datasets/hanzla/datascience-instruct.

The original contributions presented in the study are included in the article and online through the TAME Toolkit, available at: https://uncsrp.github.io/Data-Analysis-Training-Modules/, with underlying code and datasets available in the parent UNC-SRP GitHub website (https://github.com/UNCSRP). This dataset is associated with the following publication: Roell, K., L. Koval, R. Boyles, G. Patlewicz, C. Ring, C. Rider, C. Ward-Caviness, D. Reif, I. Jaspers, R. Fry, and J. Rager. Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research. Frontiers in Toxicology. Frontiers, Lausanne, SWITZERLAND, 4: 893924, (2022).

The size of the Computational Biology Industry market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 13.33% during the forecast period. The computational biology industry is booming, driven by the growth in volumes of biological data generated by advancing genomics, proteomics, and systems biology. It involves an interdisciplinary approach that links biology, computer science, and mathematics to analyze complicated biological systems and processes-deemed indispensable for drug discovery, personalized medicine, and agricultural biotechnology. The rising incidence of chronic diseases necessitates targeted therapies and precise diagnostics, thereby becoming a key driver for market growth. The tools of computational biology, which include bioinformatics software, machine learning algorithms, and modeling simulations, enable the extraction of meaningful insights from vast datasets, accelerating the pace of scientific discovery. Technological advancements are further enhancing the functionality of computational biology. The way biological data is interpreted in terms of analysis is undergoing a fundamental shift with AI and machine learning being increasingly integrated in data analysis. Moreover, cloud computing makes it easy for researchers to share data as well as collaborate, making innovation in this field flourish. Geographical center, North America, strong existence of research institutions, biotechnology firms, and investments by funding in life sciences research. Asia-Pacific is emerging, with increased investments in the healthcare and biotechnology sectors and growing importance of personalized medicine. Essentially, the overall industry of computational biology would seem to have excellent chances for sustained expansion based on the further advancing nature of technology, be it a need to gain a clearer sense of incredible data sizes or the overall emphasis to expand focus around precision health solutions. Biological science continually advancing, through computation will unlock new sights, it will be driving an innovation engine across every single domain of healthcare delivery services. Recent developments include: February 2023: The Centre for Development of Advanced Computing (C-DAC) launched two software tools critical for research in life sciences. Integrated Computing Environment, one of the products, is an indigenous cloud-based genomics computational facility for bioinformatics that integrates ICE-cube, a hardware infrastructure, and ICE flakes. This software will help securely store and analyze petascale to exascale genomics data., January 2023: Insilico Medicine, a clinical-stage, end-to-end artificial intelligence (AI)-driven drug discovery company, launched the 6th generation Intelligent Robotics Lab to accelerate its AI-driven drug discovery. The fully automated AI-powered robotics laboratory performs target discovery, compound screening, precision medicine development, and translational research.. Key drivers for this market are: Increase in Bioinformatics Research, Increasing Number of Clinical Studies in Pharmacogenomics and Pharmacokinetics; Growth of Drug Designing and Disease Modeling. Potential restraints include: Lack of Trained Professionals. Notable trends are: Industry and Commercials Sub-segment is Expected to hold its Highest Market Share in the End User Segment.

This paper addresses the gap between the practice of biological science and biology education as it pertains to data science and quantitative literacy, and the role that educational gateways can play in closing that gap. We discuss general opportunities and challenges for educational gateways, including those specific to bringing data science to the undergraduate classroom. We then introduce a free open-source web application currently under active development called Serenity, which is being designed to address these opportunities and challenges. Serenity will be deployed on the education gateway QUBES (Quantitative Undergraduate Biology Education and Synthesis, https://qubeshub.org).

This paper reviews some ingredients of the current “Data Science moment”, including recent commentary about data science in the popular media, and about how/whether Data Science is really different from Statistics.

Presentation made by Lou Gross et al. as part of the "Bringing Research Data to the Ecology Classroom: Opportunities, Barriers, and Next Steps” Session at the Ecological Society of America annual meeting, August 8th, 2017, Portland Oregon

Current and future data analysis needs of National Science Foundation (NSF) Biological Sciences Directorate (BIO) principal investigators (PIs): Bioinformaticians versus others, large versus small research groups.

Leveraging prior viral genome sequencing data to make predictions on whether an unknown, emergent virus harbors a ‘phenotype-of-concern’ has been a long-sought goal of genomic epidemiology. A predictive phenotype model built from nucleotide-level information alone is challenging with respect to RNA viruses due to the ultra-high intra-sequence variance of their genomes, even within closely related clades. We developed a degenerate k-mer method to accommodate this high intra-sequence variation of RNA virus genomes for modeling frameworks. By leveraging a taxonomy-guided ‘group-shuffle-split’ cross validation paradigm on complete coronavirus assemblies from prior to October 2018, we trained multiple regularized logistic regression classifiers at the nucleotide k-mer level. We demonstrate the feasibility of this method by finding models accurately predicting withheld SARS-CoV-2 genome sequences as human pathogens and accurately predicting withheld Swine Acute Diarrhea Syndrome coronavirus (...

According to Cognitive Market Research, the Global Bioinformatics Services Market Size will be USD XX Billion in 2023 and is set to achieve a market size of USD XX Billion by the end of 2031 growing at a CAGR of XX% from 2024 to 2031.

• The global Bioinformatics services Market will expand significantly by XX% CAGR between 2024 and 2031.

• Based on technology, Because of the growing number of platform applications and the need for improved tools for drug development, the bioinformatics platforms segment dominated the market.

• In terms of service type, The sequencing services segment held the largest share and is anticipated to grow over the coming years

• Based on application, The genomic segment dominated the bioinformatics market

• Based on End-user, academic institutes and research centers segment hold the largest share.

• Based on speciality segment, The medical bioinformatics segment holds the large share and is anticipated to expand at a substantial CAGR during the forecast period.

• The North America region accounted for the highest market share in the Global Bioinformatics Services Market. CURRENT SCENARIO OF THE BIOINFORMATICS SERVICES

Driving Factors of the Bioinformatics Services Market

Expansive uses of bioinformatics across multiple sectors is propelling the market's growth.

Several industries, such as the food, bioremediation, agriculture, forensics, and consumer industries, are also using bioinformatics services to improve the quality of their products and supply chain processes. Companies in a variety of sectors are rapidly utilizing bioinformatics services such as data integration, manipulation, lead generation, data management, in silico analysis, and advanced knowledge discovery.

• Bioinformatics Approaches in Food Sciences

In order to meet the needs of food production, food processing, enhancing the quality and nutritional content of food sources, and many other areas, bioinformatics plays a significant role in forecasting and evaluating the intended and undesired impacts of microorganisms on food, genomes, and proteomics research. Furthermore, bioinformatics techniques can be applied to produce crops with high yields and resistance to disease, among other desirable qualities. Additionally, there are numerous databases with information about food, including its components, nutritional value, chemistry, and biology.

Genome Canada is proud to partner with five Institutes where there are five funding pools within this opportunity and Genome Canada is partnering on the Bioinformatics, Computational Biology and Health Data Sciences pool. (Source:https://genomecanada.ca/genome-canada-partners-with-cihr-to-launch-health-research-training-platform-2024-25/)

• Bioinformatics in agriculture

Bioinformatics is becoming more and more crucial in the gathering, storing, and processing of genomic data in the field of agricultural genomics, or agri-genomics. Generally referred to as agri-informatics, some of the various applications of bioinformatics tools and methods in agriculture focus on improving plant resistance against biotic and abiotic stressors as well as enhancing the nutritional quality in depleted soils. Beyond these uses, computer software-assisted gene discovery has enabled researchers to create focused strategies for seed quality enhancement, incorporate extra micronutrients into plants for improved human health, and create plants with phytoremediation potential.

India/UK-based Agri-Genomics startup, Piatrika Biosystems has raised $1.2 Million in a seed round led by Ankur Capital. The company is bringing sustainable seeds and agri chemicals to market faster and cheaper. The investment will be used to build a strong Product Development team, also for more profound research, and to accelerate the productionising and commercialization of MVP. (Source:https://pressroom.icrisat.org/agri-genomics-startup-piatrika-biosystems-raises-12-million-in-seed-funding-led-by-ankur-capital)

This expansion in the application areas of bioinformatics services is likely to drive the overall market growth. Bioinformatics services such as data integration, manipulation, lead discovery, data management, in silico analysis, and advanced knowledge discovery are increasingly being adopted by companies across various industries.&...

Progression of the students in the different exercises of the biological data science courses at the University of Mons, Belgium for the academic year 2019-2020.

Activity of the students was recorded to monitor their individual progression in asynchronous exercises. The courses were taught in flipped classroom by Philippe Grosjean (philippe.grosjean@umons.ac.be) and Guyliann Engels (guyliann.engels@umons.ac.be) the University of Mons. These authors designed almost all the teaching material, the exercises, and the related software. The courses were also taught at the Campus Charleroi by Raphaël Conotte (raphael.conotte@umons.ac.be) that also contributed to a part of the learnr exercises and of the inline course.

How to use these data?

The README file provides detailed information on the purpose, collection and management of the data. The data are presented in tabular format in CSV files. Metadata in the `datapackage.json` document the different tables and their fields. It is in the Frictionless data format (https://frictionlessdata.io). You can get a view of a part of these metadata by uploading the file `datapackage.json` into the inline data package creator at https://create.frictionlessdata.io. There is a large set of libraries and tools for different programming languages available at https://frictionlessdata.io/tooling/libraries/. Otherwise, any CSV library should import the data in your favourite software. Please, note that encoding is UTF8. For R, the {learnitdown} package provides specific functions to import these data and/or convert them in a SQLite database (https://www.sciviews.org/learnitdown/).

For any question, send an email at sdd@sciviews.org.

Progression of the students in the different exercises of the biological data science courses at the University of Mons, Belgium for the academic year 2020-2021.

Activity of the students was recorded to monitor their individual progression in asynchronous exercises. The courses were taught in flipped classroom by Philippe Grosjean (philippe.grosjean@umons.ac.be) and Guyliann Engels (guyliann.engels@umons.ac.be) the University of Mons. These authors designed almost all the teaching material, the exercises, and the related software. The courses were also taught at the Campus Charleroi by Raphaël Conotte (raphael.conotte@umons.ac.be) that also contributed to a part of the learnr exercises and of the inline course.

How to use these data?

The README file provides detailed information on the purpose, collection and management of the data. The data are presented in tabular format in CSV files. Metadata in the `datapackage.json` document the different tables and their fields. It is in the Frictionless data format (https://frictionlessdata.io). You can get a view of a part of these metadata by uploading the file `datapackage.json` into the inline data package creator at https://create.frictionlessdata.io. There is a large set of libraries and tools for different programming languages available at https://frictionlessdata.io/tooling/libraries/. Otherwise, any CSV library should import the data in your favourite software. Please, note that encoding is UTF8. For R, the {learnitdown} package provides specific functions to import these data and/or convert them in a SQLite database (https://www.sciviews.org/learnitdown/).

For any question, send an email at sdd@sciviews.org.

Open data science and algorithm development competitions offer a unique avenue for rapid discovery of better computational strategies. We highlight three examples in computational biology and bioinformatics research in which the use of competitions has yielded significant performance gains over established algorithms. These include algorithms for antibody clustering, imputing gene expression data, and querying the Connectivity Map (CMap). Performance gains are evaluated quantitatively using realistic, albeit sanitized, data sets. The solutions produced through these competitions are then examined with respect to their utility and the prospects for implementation in the field. We present the decision process and competition design considerations that lead to these successful outcomes as a model for researchers who want to use competitions and non-domain crowds as collaborators to further their research.

Abstract for poster on using synthetic biology to introduce students to meaningful data mining, analysis, and application to engineering novel biological constructs.

Dotmatics is a cutting-edge scientific research and development platform that offers a comprehensive solution for molecular biology researchers. With a focus on improving the ease and efficiency of cloning procedures, Dotmatics' platform provides a range of tools and applications for data analysis, biologics, flow cytometry, and more.

Through its various applications, including SnapGene, Geneious, and others, Dotmatics empowers researchers to design, visualize, and document complex cloning procedures with ease. With its intuitive interface and advanced features, the platform simplifies the process of molecular biology research, enabling scientists to achieve better results in less time. By providing a comprehensive platform for molecular biology research, Dotmatics is revolutionizing the way scientists approach their work, ultimately driving discovery and innovation in their respective fields.

This short activity can be used to introduce the NAS Data Science For Undergraduates report's definition of data acumen and engage participants in a self assessment of how they connect with those 10 data science concepts.

The global biological software market is experiencing robust growth, driven by the increasing adoption of advanced technologies in life sciences research and healthcare. The market, estimated at $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of approximately 12% from 2025 to 2033, reaching an estimated market value of $7 billion by 2033. This expansion is fueled by several key factors: the escalating demand for high-throughput data analysis in genomics and proteomics, the rising prevalence of chronic diseases necessitating advanced diagnostic tools, and the growing adoption of cloud-based solutions for enhanced collaboration and accessibility. Furthermore, the continuous development of sophisticated algorithms and user-friendly interfaces is making biological software more accessible to a wider range of researchers and clinicians. The segment encompassing experimental design and data analysis software holds a significant market share, reflecting the crucial role of computational tools in optimizing research workflows and extracting meaningful insights from complex biological datasets. North America currently dominates the market, owing to the robust presence of established biotechnology companies and a well-funded research infrastructure. However, Asia-Pacific is expected to witness significant growth in the coming years due to the expanding healthcare sector and increasing government investments in research and development. Market restraints include the high cost of software licenses, the requirement for specialized training to effectively utilize these tools, and the potential challenges associated with data security and integration across different platforms. Nevertheless, the ongoing innovation in software capabilities, coupled with the increasing adoption of subscription-based models and cloud-based solutions, is expected to mitigate these constraints. The competitive landscape is characterized by a mix of established players like Thermo Fisher Scientific and DNASTAR, along with smaller specialized companies offering niche solutions. This dynamic competitive environment fosters innovation and drives the development of advanced biological software solutions tailored to the specific needs of diverse research and clinical applications. Future growth will be influenced by factors such as advancements in artificial intelligence and machine learning within the software, integration with laboratory automation systems, and increasing collaboration between software providers and research institutions.

The digital biology market is experiencing robust growth, driven by the convergence of advanced computing, data analytics, and life sciences. The increasing availability of large biological datasets, coupled with advancements in artificial intelligence (AI) and machine learning (ML), is fueling the development of innovative tools and platforms for drug discovery, personalized medicine, and agricultural biotechnology. This market, estimated at $15 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033, reaching approximately $60 billion by 2033. Key drivers include the rising demand for faster and more efficient drug development processes, the increasing prevalence of chronic diseases necessitating personalized treatments, and the growing adoption of precision agriculture techniques. The market's segmentation encompasses software solutions, hardware infrastructure, and services, with leading players like DUNA Bioinformatics, Precigen, Dassault Systèmes, Genedata AG, and Simulations Plus actively shaping the market landscape through continuous innovation. The North American region currently holds a significant market share due to substantial investments in R&D and the presence of major players, although growth in other regions like Europe and Asia-Pacific is accelerating. While the market's growth trajectory is positive, certain restraints exist. High upfront investment costs for software and hardware, the need for skilled personnel to operate advanced systems, and data security and privacy concerns are some challenges that the industry needs to address. However, ongoing technological advancements are mitigating these limitations. The development of user-friendly interfaces, cloud-based solutions, and improved data security measures are steadily increasing market accessibility and fostering wider adoption. Further fueling market expansion are collaborative initiatives between academic institutions, pharmaceutical companies, and technology providers, fostering the creation of innovative and cost-effective solutions. This collaborative approach is crucial for overcoming the challenges and unlocking the immense potential of digital biology in transforming various sectors.

Over the past year, biology educators and staff at the U.S. Department of Energy Systems Biology Knowledgebase (KBase) initiated a collaborative effort to develop a curriculum for bioinformatics education. KBase is a free web-based platform where anyone can conduct sophisticated and reproducible bioinformatic analyses via a graphical user interface. Here, we demonstrate the utility of KBase as a platform for bioinformatics education, and present a set of modular, adaptable, and customizable instructional units for teaching concepts in Genomics, Metagenomics, Pangenomics, and Phylogenetics. Each module contains teaching resources, publicly available data, analysis tools, and Markdown capability, enabling instructors to modify the lesson as appropriate for their specific course. We present initial student survey data on the effectiveness of using KBase for teaching bioinformatic concepts, provide an example case study, and detail the utility of the platform from an instructor’s perspective. Even as in-person teaching returns, KBase will continue to work with instructors, supporting the development of new active learning curriculum modules. For anyone utilizing the platform, the growing KBase Educators Organization provides an educators network, accompanied by community-sourced guidelines, instructional templates, and peer support, for instructors wishing to use KBase within a classroom at any educational level–whether virtual or in-person.

Digital Biology Market Outlook

In 2023, the global market size for Digital Biology was estimated at $4.2 billion and is projected to reach $15.6 billion by 2032, growing at a CAGR of 15.4% over the forecast period. The primary growth factor driving this market is the increasing integration of digital tools and technologies in biological research and applications. As the field of biology continues to evolve, the adoption of digital solutions offers unprecedented capabilities in data analysis, simulation, and modeling.

One of the key growth factors for the Digital Biology market is the accelerating pace of technological advancements in bioinformatics and computational biology. The introduction of high-throughput sequencing technologies and advanced data analytics tools has revolutionized the way biological data is collected, processed, and interpreted. This technological progression enables more accurate and faster analysis, which is critical for the development of personalized medicine, advanced research, and innovative biotechnological products. Such advancements are likely to further fuel the demand for digital biology solutions in the coming years.

Another significant factor contributing to the growth of the Digital Biology market is the increasing investment in life sciences research and development. Governments, private organizations, and academic institutions worldwide are investing heavily in R&D activities to discover new drugs, understand complex biological systems, and develop sustainable agricultural practices. These investments are driving the need for sophisticated digital biology tools that can handle complex datasets, model biological processes, and provide insights that were previously unattainable. As funding and support for biological research continue to rise, the demand for digital biology solutions is expected to grow correspondingly.

Moreover, the growing emphasis on personalized medicine and healthcare is also a major driver of market growth. Personalized medicine aims to tailor medical treatment to the individual characteristics of each patient, which requires a deep understanding of genetic, environmental, and lifestyle factors. Digital biology tools provide the necessary computational power and analytical capabilities to process vast amounts of biological data, identify patterns, and predict outcomes. This capability is essential for the development of targeted therapies and precision medicine, making digital biology an indispensable tool in modern healthcare.

Biosimulation Technology is emerging as a transformative force within the digital biology landscape. By enabling the virtual testing and modeling of biological processes, biosimulation technology allows researchers to predict the behavior of biological systems under various conditions. This capability is particularly valuable in drug development, where biosimulation can reduce the time and cost associated with clinical trials by identifying promising drug candidates and optimizing their formulations before they reach the testing phase. Furthermore, biosimulation technology supports the advancement of personalized medicine by simulating how individual patients might respond to specific treatments, thus paving the way for more tailored and effective healthcare solutions.

Regionally, North America holds a significant share of the Digital Biology market, driven by the presence of a robust healthcare infrastructure, a high level of technological adoption, and substantial investment in research and development. The Asia Pacific region is expected to witness the highest growth rate, with a CAGR of 17.1%, due to increasing government initiatives, rising healthcare expenditure, and growing awareness about the benefits of digital biology. Europe also represents a substantial market share, attributed to the strong presence of pharmaceutical companies and research institutes in the region.

Component Analysis

The Digital Biology market is segmented into software, hardware, and services. The software segment holds the largest market share due to the increasing demand for bioinformatics software, data analysis tools, and simulation models. As biological data becomes increasingly complex, the need for sophisticated software solutions capable of handling large datasets and providing accurate results is paramount. These software solutions enable researchers to model biological processes, analyze genetic data, and simulate drug interactions, making them indispensable tools in

Since 2010, the European Molecular Biology Laboratory's (EMBL) Heidelberg laboratory and the European Bioinformatics Institute (EMBL-EBI) have jointly run bioinformatics training courses developed specifically for secondary school science teachers within Europe and EMBL member states. These courses focus on introducing bioinformatics, databases, and data-intensive biology, allowing participants to explore resources and providing classroom-ready materials to support them in sharing this new knowledge with their students.In this article, we chart our progress made in creating and running three bioinformatics training courses, including how the course resources are received by participants and how these, and bioinformatics in general, are subsequently used in the classroom. We assess the strengths and challenges of our approach, and share what we have learned through our interactions with European science teachers.