This data is collected on content from five different library and information science journals: Behavioral & Social Science Librarian, Collection Management, Journal of Electronic Resources Librarianship, Journal of Library Administration and College & Undergraduate Libraries over a five-year period from 2012-2016 to investigate the green deposit rate.

The global Laboratory Information System (LIS) market is estimated to reach USD 1,820 million by 2033, exhibiting a CAGR of 5.1% during the forecast period of 2023-2033. The increasing demand for efficient and accurate laboratory testing services, the rising adoption of electronic health records (EHRs), and the growing prevalence of chronic diseases are driving the market growth. The market is segmented by type into on-premises LIS and cloud-based LIS. On-premises LIS held the larger market share in 2023, but cloud-based LIS is expected to witness faster growth during the forecast period due to its cost-effectiveness and scalability. The market is also segmented by application into hospitals, clinics, independent laboratories, and others. Hospitals accounted for the largest market share in 2023, and this trend is expected to continue during the forecast period. Geographically, North America accounted for the largest market share in 2023, followed by Europe and Asia Pacific. Asia Pacific is expected to exhibit the highest CAGR during the forecast period due to rising healthcare expenditure and increasing government initiatives to improve healthcare infrastructure.

The Laboratory Information System (LIS) software market is experiencing robust growth, driven by the increasing adoption of electronic health records (EHRs), the rising demand for improved laboratory efficiency and workflow automation, and the growing need for better data management and analysis in healthcare settings. The market's expansion is fueled by a shift towards cloud-based solutions offering scalability, cost-effectiveness, and accessibility. Large enterprises are leading the adoption, but the SME segment is demonstrating significant growth potential as they recognize the benefits of streamlined operations and reduced manual processes. The integration of LIS with other healthcare IT systems, such as hospital information systems (HIS) and picture archiving and communication systems (PACS), is further boosting market growth, enabling seamless data exchange and improved patient care coordination. While the on-premise deployment model still holds a significant market share, the cloud-based segment is anticipated to dominate the market in the coming years due to its inherent advantages. Factors such as high initial investment costs, data security concerns, and the need for specialized IT infrastructure are potential restraints, but ongoing technological advancements and increasing vendor support are mitigating these challenges. We estimate the global market size to be approximately $2.5 billion in 2025, with a CAGR of around 8% over the forecast period (2025-2033), resulting in a market value exceeding $4.5 billion by 2033. This growth will be distributed across various regions, with North America and Europe maintaining significant market shares due to high healthcare spending and technological advancements. However, the Asia-Pacific region is projected to exhibit significant growth, driven by increasing healthcare investments and expanding healthcare infrastructure in developing economies. The competitive landscape is marked by established players and emerging vendors offering a range of solutions tailored to specific laboratory needs. The continued development of advanced features such as artificial intelligence (AI) and machine learning (ML) for data analysis and predictive maintenance within LIS software will further propel market growth. The increasing focus on interoperability and data standardization within the healthcare industry is creating opportunities for LIS vendors to provide solutions that facilitate seamless data exchange across different systems. Furthermore, rising government initiatives promoting digital healthcare and the growing adoption of telehealth are creating favorable market conditions. The key to success for LIS vendors lies in offering solutions that address specific customer needs, provide robust security measures, and integrate seamlessly with existing healthcare IT infrastructure. The market's sustained growth trajectory indicates a promising future for LIS software providers who can adapt to the evolving demands of the healthcare industry.

List of foreign open access journals in the field of LIS is presented

The global Laboratory Information Systems (LIS) market, valued at $913.4 million in 2025, is projected to experience steady growth, driven by the increasing demand for efficient laboratory operations and improved patient care. A compound annual growth rate (CAGR) of 3.1% is anticipated from 2025 to 2033, indicating a substantial market expansion. This growth is fueled by several key factors. The rising prevalence of chronic diseases necessitates higher testing volumes, pushing laboratories to adopt automated and integrated systems like LIS for better management of workflow and data. Furthermore, the increasing adoption of electronic health records (EHR) systems necessitates seamless integration with LIS, driving market expansion. Stringent regulatory requirements for data security and compliance are also contributing to the adoption of sophisticated LIS solutions. The market is segmented based on deployment type (cloud-based, on-premise), component (hardware, software, services), and end-user (hospitals, clinics, reference labs). Competitive rivalry among key players like Allscripts, Cerner, Epic Systems, and McKesson, is driving innovation and technological advancements within the LIS market. The market's growth trajectory, however, is not without challenges. High initial investment costs associated with implementing LIS, particularly in smaller laboratories with limited budgets, represent a significant restraint. Additionally, the need for specialized personnel to operate and maintain the systems and the complexities involved in data integration with existing laboratory equipment could hinder broader adoption in certain regions. Nonetheless, the long-term benefits of improved efficiency, reduced errors, and enhanced diagnostic accuracy are expected to outweigh these challenges, ensuring consistent market growth throughout the forecast period. The shift towards cloud-based solutions is expected to alleviate some of the cost concerns and complexity issues. The development of user-friendly interfaces and integration with other healthcare IT systems will likely drive future market penetration.

This dataset includes the interview questions and codebook for the 5 interviews collected as part of the project "LIS program representatives’ perspectives on preparing students for careers in research data management and data-related librarianship". Due to the small sample size and the risk of re-identification of the participants and/or their institutions, the data itself cannot be shared. We hope that the inclusion of the questions used in the semi-structured interviews and the codebook will assist readers of the related article to better understand the work done.

WILIS 2 was a follow-up study to develop an alumni tracking system aimed at recent graduates that could potentially be used by all LIS programs. The project built on WILIS 1, a comprehensive IMLS funded study of career patterns of graduates of LIS programs in North Carolina. WILIS 2 builds on WILIS 1 by fully developing and testing the career tracking model on a national level. The WILIS 2 survey was designed for recent graduates of LIS programs in North America. The survey gathered data on demographics, employment, LIS Master’s Program experience and evaluation and knowledge and skills provided by the LIS Program. 39 LIS programs participated in the study. Programs were asked to select a random sample of 250 of their master’s degree graduates from the previous five years; however, several programs included a few graduates from earlier years. Fewer than four percent of these respondents graduated prior to 2003. Programs with multiple degrees were able to select the degree programs included in their sample. The graduates received an email invitation and three email reminders. A few programs mailed paper invitations to encourage better response rates. The response rate for the survey was 41% (n=3507). Response rates for individual programs ranged from 16 % to 80%. The dataset of the 39 LIS programs includes alumni that graduated between 2000 and 2009.

To reveal and systematically investigate the effectiveness of the proposed method in the real world, a real low-light image dataset for instance segmentation is necessary and urgently needed. Considering there is no suitable dataset, therefore, we collect and annotate a Low-light Instance Segmentation (LIS) dataset using a Canon EOS 5D Mark IV camera.

It exhibits the following characteristics:

Paired samples. In the LIS dataset, we provide images in both sRGB-JPEG (typical camera output) and RAW formats, each format consists of paired short-exposure low-light and corresponding long-exposure normal-light images. We term these four types of images as \textit{sRGB-dark, sRGB-normal, RAW-dark, and RAW-normal}. To ensure they are pixel-wise aligned, we mount the camera on a sturdy tripod and avoid vibrations by remote control via a mobile app.

Diverse scenes. The LIS dataset consists of 2230 image pairs, which are collected in various scenes, including indoor and outdoor. To increase the diversity of low-light conditions, we use a series of ISO levels (\eg, 800, 1600, 3200, 6400) to take long-exposure reference images, and we deliberately decrease the exposure time by a series of low-light factors (\eg, 10, 20, 30, 40, 50, 100) to take short-exposure images for simulating very low-light conditions.

Instance-level pixel-wise labels. For each pair of images, we provide precise instance-level pixel-wise labels annotated by professional annotators, yielding 10504 labeled instances of 8 most common object classes in our daily life (bicycle, car, motorcycle, bus, bottle, chair, dining table, tv).

Detailed information about the Organisation LIS-3.

Laboratory Information System Market Outlook

The laboratory information system (LIS) market size is poised for significant growth, with a current valuation of approximately USD 2.5 billion in 2023 and a forecasted expansion to around USD 4.8 billion by 2032, reflecting a compound annual growth rate (CAGR) of 7.3%. This robust growth trajectory is driven by several factors, including the rapid technological advancements in laboratory systems, increasing demand for automation in laboratory processes, and the escalating need for efficient management of laboratory data. The integration of LIS solutions is seen as a pivotal move towards enhancing laboratory productivity, improving accuracy in diagnostics, and facilitating effective data management. These aspects are critical as laboratories strive to meet the rising demand for testing and diagnostics, especially in the wake of global healthcare challenges that necessitate swift and reliable testing solutions.

One of the primary growth drivers of the LIS market is the growing emphasis on improving healthcare infrastructure globally. As countries aim to enhance their healthcare systems, there is an accelerated adoption of advanced healthcare technologies, including LIS, to improve operational efficiency and patient care. The shift towards personalized medicine, which requires precise and efficient laboratory diagnostics, further fuels the demand for LIS. Additionally, the growing volume of diagnostic testing, driven by an aging population and the increasing prevalence of chronic diseases, underscores the need for robust laboratory information systems that can handle large datasets efficiently and securely. These systems are crucial in improving the turnaround times for tests, thereby enabling quicker clinical decisions and enhancing patient outcomes.

Another significant growth factor is the increasing regulatory compliance requirements in the healthcare sector. Laboratories are increasingly being required to adhere to stringent regulations concerning the accuracy and traceability of test results. LIS solutions offer the technological backbone required to ensure compliance with these regulations by facilitating standardized processes and accurate data recording. This ensures that laboratory operations adhere to the quality and reporting standards required by regulatory bodies, thus minimizing the risk of errors and enhancing the credibility of laboratory data. Furthermore, with the advent of electronic health records (EHRs) and digitalization in healthcare, laboratories are compelled to adopt LIS solutions that can seamlessly integrate with other healthcare systems, thereby promoting interoperability and efficient data sharing across different healthcare platforms.

The need for cost efficiency and reduction in operational costs is also a significant catalyst in the adoption of LIS solutions. Laboratories are under constant pressure to perform efficiently, with limited resources, making cost-effective operations a necessity. By automating routine laboratory tasks, LIS solutions help reduce manual errors, optimize resource allocation, and streamline the overall workflow. This leads to significant cost savings while maintaining high standards of accuracy and efficiency in testing. Moreover, in the context of competitive healthcare markets, the ability of laboratories to offer rapid, reliable, and cost-effective diagnostic services can be a key differentiator, thus driving further investment in LIS technologies.

Component Analysis

In the LIS market, the component analysis primarily revolves around software, hardware, and services. Software solutions form the core of LIS offerings, providing laboratories with the necessary tools to manage data efficiently, automate laboratory processes, and ensure compliance with industry standards. The software segment is expected to register substantial growth, primarily due to ongoing innovations in data analytics, artificial intelligence, and machine learning, which enhance the capabilities of LIS. These advanced features empower laboratories with predictive insights, aid in the efficient management of laboratory workflows, and improve the accuracy and speed of diagnostics. The customization of LIS software to cater to specific needs of different laboratories is also a significant trend, enabling more precise and tailored solutions.

Hardware components, though less emphasized compared to software, play a critical role in the overall LIS ecosystem. The integration of sophisticated hardware such as barcode readers, printers, and laboratory instruments is essential for the seamless operation of a LIS. These components ensure the accu

Locations that generally represent significant and long-standing locations of water quality monitoring in Long Island Sound.

For more information on the data sources used and the creation of this layer, please refer to The Long Island Sound Blue Plan Appendix on Significant Human Use Areas.

LIS is a publicly accessible legume resource that integrates genetic and molecular data from multiple legume species and enables cross-species genomic, transcript and map comparisons. The intent of the LIS is to help researchers leverage data-rich model plants to fill knowledge gaps across crop plant species and provide the ability to traverse between interrelated data types. LIS, a component of the Model Plant Initiative (MPI), is being developed as part of a cooperative research agreement between the National Center for Genome Resources (NCGR) and the USDA Agricultural Research Service (ARS).

Motivation

My motivation in providing this dataset is to invite more interests from Indonesia's librarian to understand their diverse field of study.

Method

This dataset is harvested in 19 January 2019 from Scopus database provided by The University of Sydney. I used the keyword "bibliometric" in title, sort the search results by total citation, then download the first 2000 papers as RIS file. This file can be converted to other formats like bibtex or csv using available reference manager, like Zotero.

Visualisations

I did two small visualisations using the following options:

1. "create a map based on bibliographic data"
2. "create a map based on text data"

Both mappings are done using VosViewer open source app from CWTS Leiden University.

The data provided in this data set are simulated using the Noah-Multiparameterization Land Surface Model (Noah-MP LSM) Version 3.6 within the NASA Land Information System (LIS) Version 7.2. The data files contain estimates of water, energy fluxes, and land surface states for the High Mountain Asia (HMA) region.

Education and further studies: refers to various learning, education and related information collections.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

The Lightning Imaging Sensor (LIS) Science Data was collected by the LIS instrument on the Tropical Rainfall Measuring Mission (TRMM) satellite used to detect the distribution and variability of total lightning occurring in the Earth’s tropical and subtropical regions. This data can be used for severe storm detection and analysis, as well as for lightning-atmosphere interaction studies. The LIS instrument makes measurements during both day and night with high detection efficiency. These data are available in both HDF-4 and netCDF-4 formats, with corresponding browse images in GIF format.

The Non-Quality Controlled Lightning Imaging Sensor (LIS) on International Space Station (ISS) Science Data were collected by the LIS instrument on the ISS used to detect the distribution and variability of total lightning occurring in the Earth’s tropical and subtropical regions. This dataset consists of science data. These data can be used for severe storm detection and analysis, as well as for lightning-atmosphere interaction studies. The LIS instrument makes measurements during both day and night with high detection efficiency. The data are available in both HDF-4 and netCDF-4 formats.

Overview of Dataset

This dataset was developed through a collaboration between Dalhousie University and the University of Montréal. This project aims to help break down the silos in which the two primary target audiences- information science researchers and academic librarians- conduct their research. The Canadian Publications in Library and Information Science dataset makes visible the work that librarians do and allows other Canadian researchers to discover the research of their colleagues.

The dataset contains 1,326 distinct authors, 850 of which were classified as practitioners and 476 as academics. It has a total of 13,775 records out of which 8,230 are authored by at least one academic and 5,740 are authored by at least one practitioner.

File descriptions

Table 1. Canadian LIS authors table (authors)

Table 2. Works table (publications)

Table 3. Author publications table (authors_publications)

Table 4. Author IDs table (authors_ids)

Table 5. Publication source table (sources)

Table 6. Institutions table (institutions)

Table 7. Institution IDs table (institutions_ids)

Table 8. Authorship institutional affiliation table (authors_publications_institutions)

Table 9. Citations table (citations)

To submit updates

For those interested in submitting updates to this dataset, you may send them by email to Philippe Mongeon (PMongeon@dal.ca). Please specify whether you want to modify, add, or delete existing data entries. Files in any format (e.g., XLS, BIB, Word, or a list of DOIs) are accepted.

Data paper

Find the corresponding data paper that describes the objectives of this dataset and the steps of its creation here: https://arxiv.org/abs/6053305.

How to cite this dataset

Sauvé, J.-S., Hare, M., Krause, G., Poitras, C., Riddle, P., & Mongeon, P. (2024). Canadian publications in Library and Information Science / Publications canadiennes en bibliothéconomie et sciences de l'information [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14302591

This GIS layer contains a point overlay showing the location of samples with Total Organic Carbon (TOC). This layer shows the distribution of samples used in the creation of the TOC polygon layer, listoc.