Hasil untuk "Norwegian literature"

Trude Mari Christensen, Annelin Nes, Pål André Hegland et al.

Bakgrunn: Om lag en av ti norske intensivpasienter dør hvert år. Internasjonalt anbefales en holistisk og personsentrert tilnærming til lindrende behandling for denne pasientgruppen. The 3 Wishes Project (3WP) er et innovativt behandlingsprogram som har som mål om å fremme personsentrert behandlingspraksis for døende intensivpasienter og deres pårørende. Hensikt: Hensikten med denne studien var å kartlegge eksisterende forskningslitteratur om pasienter, pårørende og helsepersonell sine erfaringer med implementering og bruk av 3WP ved lindrende behandling. Metode: Det ble gjennomført en scoping review basert på Joanna Briggs Institutes metodiske rammeverk. Systematiske litteratursøk ble gjennomført i CINAHL, MEDLINE, Embase, Web of Science og Cochrane Library i april 2023, med oppdateringssøk i august 2024. Studien inkluderte kvalitativ og kvantitativ forskningslitteratur. Det ble gjennomført en deskriptiv analyse, samt en forenklet kvalitativ innholdsanalyse av funnene. Resultat: Det ble inkludert 18 studier. Studiene var gjennomført i Canada og USA, og erfaringer hos helsepersonell og pårørende var hyppigst undersøkt. Ingen studier inkluderte pasientens perspektiv, mens seks av studiene undersøkte implementering av 3WP. Funnene viste at 3WP kunne fremme en personsentrert lindrende behandling som tok hensyn til pasientens livshistorie og individuelle behov, samt sørget for en verdig avslutning på livet. Behandlingsprogrammet viste seg å være støttende for pårørende i sorgbearbeidelsen. Intensivpersonalet erfarte at 3WP tilrettela for et positivt arbeidsmiljø, konsistent omsorg og et faglig fellesskap. Implementeringen av programmet ble i hovedsak godt mottatt, ettersom det var i tråd med institusjonens verdier. Det var imidlertid viktig med ledelsesforankring som sikret strukturelle rammer og tilgjengelige ressurser. Konklusjon: 3WP kan fremme personsentrert lindrende behandling i en intensivkontekst. Videre kan behandlingsprogrammet ivareta pårørende og gi helsepersonell et verktøy ved lindrende behandling. Det er viktig med videre forskning som undersøker pasientens perspektiv, samt studier fra ulike helsesystem. English abstract Experiences with "The 3 Wishes Project" during end-of-life care in the intensive care unit – a scoping review Background: Approximately one in ten patients die every year in the Norwegian Intensive Care Unit (ICU). International guidelines recommend person-centred end-of-life (EOL) care for these patients. The 3 Wishes Project (3WP) is an innovative clinical program with the potential to provide person-centred care for the dying ICU patients and their families. Objective: The purpose of this study was to map the existing research related to the dying intensive care patients’, their families’ and the clinicians’ experiences with the implementation and use of 3WP in EOL care. Methods: A scoping review was conducted based on Joanna Briggs Institutes methodical framework. Systematic literature searches were conducted in CINAHL, MEDLINE, Embase, Web of Science and Cochrane Library in April 2023, with updated searches in August 2024. The study included qualitative and quantitative research litterature. The findings were processed in a descriptive analysis and a basic content analysis. Results: The scoping review included 18 studies. All studies were conducted in Canada and USA, where clinicians’ and families’ experiences were explored most frequently. None of the studies explored the patients’ perspective, while six of the studies explored implementation. The findings indicated that 3WP could promote person-centered EOL care that considered the patient’s life history, individual needs, and ensured a dignified death. 3WP seemed to support families during the grieving process. Clinicians experienced that 3WP facilitated a positive work environment, consistent care, and professional community. The implementation of the program was generally well-received, as it aligned with institutional values. However, supportive leadership was important to ensure structural frameworks and available resources. Conclusion: 3WP can promote person-centered palliative care in the ICU. The clinical program can support families, and provide clinicians with a tool for EOL care. It’s important to conduct further research that explores the patient’s perspective, as well as studies from various healthcare systems.

Dag Herbjørnsrud

I denne teksten vises det hvordan vi har mulighet til å få en mer balansert, vitenskapelig og ikke-etnosentrisk fremstilling av den globale filosofihistorien. Teksten tar utgangspunkt i hvordan både maleren Rafael og andre renessansetenkere gir den muslimsk-arabiske europeeren Ibn Rushd (Averroës) en sentral plassering i filosofihistorien, i motsetning til hvordan filosofistudenter i dag introduseres for filosofi og vitenskap som en «gresk oppfinnelse». Som et alternativ til dagens ekskluderende skandinaviske filosofifremstillinger vises det til nyere, internasjonal forskning der vi har fått et vell av nye, tilgjengelige filosofiklassikere fra Afrika, Asia og Amerika. I sum skisseres et alternativ til et «vestlig kulturmythos» og en kolonialistisk presentasjon av filosofihistorien: et avkolonisert logos basert på et globalfilosofisk perspektiv, med utgangspunkt i en kontekstbasert og komparativ metodologi.

Türker Tuğrul, Sertaç Oruç, Jessica Louise Hall et al.

Abstract Drought is a natural disaster that often remains unnoticed until ecosystem impacts become severe. Therefore, monitoring and detecting droughts are important research topics. Consequently, drought indices with different focuses, such as precipitation or soil moisture, have been developed. Yet, the utility of the indices is limited before the beginning of the drought. To overcome this shortcoming, drought forecasting and providing decision-makers with an early warning to mitigate the effects is an important research topic. This study aims to take on the forecasting of the droughts with its novelty on the spatial focus, Norway (Drammen, Hamar, and Lillehammer). We forecast the Effective Drought Index (EDI) across spatially diverse Norwegian regions without hydrological constraints. To achieve this, we have utilized precipitation data between 1980 and 2025 and trained our machine learning models, namely, Support Vector Machine (SVM), Multi-layer Perceptron (MLP), Extreme Gradient Boosting (XGboost), Long-Short Term Memory network (LSTM), and Categorical Boosting Algorithm (Catboost). Moreover, the latent feature space is extended by wavelet transformation (WT). The innovative aspect of this study and its contribution to the literature is its novel application of the WT to some algorithms. Furthermore, unlike the literature, EDI was chosen as the drought index in this study, further increasing its innovative nature. Our results indicate that long short-term memory networks enhanced by wavelet transformation provide the best forecasts. Here, the best performance, LSTMW-M04, is achieved over Drammen (r = 0.9765, NSE = 0.9510, KGE = 0.8641, PI = 0.3211, and RMSE = 0.2207). Although LSTM is already an innovative and successful algorithm, we have further improved the model performance. This result will help decision-makers in a future drought study with both the model input structure and the algorithm used.

Xavier Vasques, Renaud Richardet, Sean L Hill et al.

Target identification for tractography studies requires solid anatomical knowledge validated by an extensive literature review across species for each seed structure to be studied. Manual literature review to identify targets for a given seed region is tedious and potentially subjective. Therefore, complementary approaches would be useful. We propose to use text-mining models to automatically suggest potential targets from the neuroscientific literature, full-text articles and abstracts, so that they can be used for anatomical connection studies and more specifically for tractography. We applied text-mining models to three structures: two well-studied structures, since validated deep brain stimulation targets, the internal globus pallidus and the subthalamic nucleus and, the nucleus accumbens, an exploratory target for treating psychiatric disorders. We performed a systematic review of the literature to document the projections of the three selected structures and compared it with the targets proposed by text-mining models, both in rat and primate (including human). We ran probabilistic tractography on the nucleus accumbens and compared the output with the results of the text-mining models and literature review. Overall, text-mining the literature could find three times as many targets as two man-weeks of curation could. The overall efficiency of the text-mining against literature review in our study was 98% recall (at 36% precision), meaning that over all the targets for the three selected seeds, only one target has been missed by text-mining. We demonstrate that connectivity for a structure of interest can be extracted from a very large amount of publications and abstracts. We believe this tool will be useful in helping the neuroscience community to facilitate connectivity studies of particular brain regions. The text mining tools used for the study are part of the HBP Neuroinformatics Platform, publicly available at http://connectivity-brainer.rhcloud.com

Markus Steen, Kristin Ystmark Bjerkan, Lillian Hansen et al.

Despite the extensive literature on port sustainability, empirical research has so far paid limited attention to experiences with implementing measures that contribute to decarbonisation in small and medium-sized ports. This study contributes to the literature by investigating decarbonisation measures implemented by Norwegian ports, and drivers and barriers that ports associate with such efforts. We rely on a unique dataset of survey responses from 96 Norwegian port organisations, supplemented with insights from qualitative research. We find that most ports have implemented at least one measure that contributes to decarbonisation. Most prominent is shore power, followed by increased energy efficiency. We find that support from owners and surroundings is prominent in decarbonisation efforts and that political guidelines and steering from port owners are important drivers. Heterogeneity in port types and contexts implies that further empirical research is needed. This study calls for raising the role of ports in the energy transition on the political agenda.

Shubham Agarwal, Gaurav Sahu, Abhay Puri et al.

Literature reviews are an essential component of scientific research, but they remain time-intensive and challenging to write, especially due to the recent influx of research papers. This paper explores the zero-shot abilities of recent Large Language Models (LLMs) in assisting with the writing of literature reviews based on an abstract. We decompose the task into two components: 1. Retrieving related works given a query abstract, and 2. Writing a literature review based on the retrieved results. We analyze how effective LLMs are for both components. For retrieval, we introduce a novel two-step search strategy that first uses an LLM to extract meaningful keywords from the abstract of a paper and then retrieves potentially relevant papers by querying an external knowledge base. Additionally, we study a prompting-based re-ranking mechanism with attribution and show that re-ranking doubles the normalized recall compared to naive search methods, while providing insights into the LLM's decision-making process. In the generation phase, we propose a two-step approach that first outlines a plan for the review and then executes steps in the plan to generate the actual review. To evaluate different LLM-based literature review methods, we create test sets from arXiv papers using a protocol designed for rolling use with newly released LLMs to avoid test set contamination in zero-shot evaluations. We release this evaluation protocol to promote additional research and development in this regard. Our empirical results suggest that LLMs show promising potential for writing literature reviews when the task is decomposed into smaller components of retrieval and planning. Our project page including a demonstration system and toolkit can be accessed here: https://litllm.github.io.

Felipe Gomes, Thiago Mendes, Sávio Freire et al.

Background: The use of gray literature (GL) has grown in software engineering research, especially in studies that consider Questions and Answers (Q&A) sites, since software development professionals widely use them. Though snowballing (SB) techniques are standard in systematic literature reviews, little is known about how to apply them to gray literature reviews. Aims: This paper investigates how to use SB approaches on Q&A sites during gray literature reviews to identify new valid discussions for analysis. Method: In previous studies, we compiled and analyzed a set of Stack Exchange Project Management (SEPM) discussions related to software engineering technical debt (TD). Those studies used a data set consisting of 108 valid discussions extracted from SEPM. Based on this start data set, we perform forward and backward SB using two different approaches: link-based and similarity-based SB. We then compare the precision and recall of those two SB approaches against the search-based approach of the original study. Results: In just one snowballing iteration, the approaches yielded 291 new discussions for analysis, 130 of which were considered valid for our study. That is an increase of about 120% over the original data set (recall). The SB process also yielded a similar rate of valid discussion retrieval when compared to the search-based approach (precision). Conclusion: This paper provides guidelines on how to apply two SB approaches to find new valid discussions for review. To our knowledge, this is the first study that analyzes the use of SB on Q&A websites. By applying SB, it was possible to identify new discussions, significantly increasing the relevant data set for a gray literature review.

Shubham Agarwal, Gaurav Sahu, Abhay Puri et al.

Conducting literature reviews for scientific papers is essential for understanding research, its limitations, and building on existing work. It is a tedious task which makes an automatic literature review generator appealing. Unfortunately, many existing works that generate such reviews using Large Language Models (LLMs) have significant limitations. They tend to hallucinate-generate non-factual information-and ignore the latest research they have not been trained on. To address these limitations, we propose a toolkit that operates on Retrieval Augmented Generation (RAG) principles, specialized prompting and instructing techniques with the help of LLMs. Our system first initiates a web search to retrieve relevant papers by summarizing user-provided abstracts into keywords using an off-the-shelf LLM. Authors can enhance the search by supplementing it with relevant papers or keywords, contributing to a tailored retrieval process. Second, the system re-ranks the retrieved papers based on the user-provided abstract. Finally, the related work section is generated based on the re-ranked results and the abstract. There is a substantial reduction in time and effort for literature review compared to traditional methods, establishing our toolkit as an efficient alternative. Our project page including the demo and toolkit can be accessed here: https://litllm.github.io

S. Quirk, L. Williams, A. O'Neil et al.

BackgroundRecent evidence suggests that diet modifies key biological factors associated with the development of depression; however, associations between diet quality and depression are not fully understood. We performed a systematic review to evaluate existing evidence regarding the association between diet quality and depression.MethodA computer-aided literature search was conducted using Medline, CINAHL, and PsycINFO, January 1965 to October 2011, and a best-evidence analysis performed.ResultsTwenty-five studies from nine countries met eligibility criteria. Our best-evidence analyses found limited evidence to support an association between traditional diets (Mediterranean or Norwegian diets) and depression. We also observed a conflicting level of evidence for associations between (i) a traditional Japanese diet and depression, (ii) a “healthy” diet and depression, (iii) a Western diet and depression, and (iv) individuals with depression and the likelihood of eating a less healthy diet.ConclusionTo our knowledge, this is the first review to synthesize and critically analyze evidence regarding diet quality, dietary patterns and depression. Further studies are urgently required to elucidate whether a true causal association exists.

Egidio Niclas D'Angelo, Karen Walseth Hara, Kristin Halvorsen et al.

Introduction: In Norway, general practitioners (GPs) are the gatekeepers who provide written assessment of patients' functional ability to provide documentation for the Norwegian Labor and Welfare Administration for decisions about welfare benefits. This article investigates the description of functioning in sickness certificates according to the bio-psycho-social model described in WHO's International Classification of Functioning, Disability and Health (ICF). In particular, the study focuses on medical sick notes for young patients with common mental disorders. Methods: This study utilized directed content analysis, where codes were defined a priori. A simplified bio-psycho-social model with ICF as a framework was used to categorize functional assessment. 393 sick notes were analyzed in Norway between January 2018–January 2020. Results: The results show that more than half (50.4%) of the certificates contain no information about function on any level, and that the diagnosis was the only indication of a patient's function. The biological perspective was the most common description in 39,9% of the certificates, 13,5% had a description of the patient's functioning from an individual perspective. The social perspective was only adopted in 12.0% of certificates. Only 4 certificates (1.0%) described all three perspectives (biological, individual, and social) and mentioned what the individual could do despite the illness (resources). Conclusions: We find that information on functional ability is limited on sickness certificates in Norway. The descriptions given were mainly from the biological perspective and without social context, which is consistent with prior research.

Leif Ole Dreyer

The collision regulations include several qualitative terms without providing guidance as to how these terms could be understood in quantitative terms. These terms must therefore be interpreted by navigators, which poses a problem for autonomous ships. Extend the knowledge of how navigators interpret the collision regulations, with a specific focus on how they interpret the rule covering the requirement to proceed at a safe speed. Qualitative study based on interviews of a convenience sample of eight Norwegian navigators. Data was analysed with systematic text condensation. Navigators characterise safe speed as a speed in which they have control. Navigators do not look at different factors mentioned in the collision regulations in isolation, but within the context of the situation. Determining the safe speed of a vessel is more complicated than made out in the literature. As autonomous ships will have to cooperate with conventional vessels, their programming must include the knowledge of how the collision regulations are interpreted by human navigators.

Alicia Martin-Navarro, Maria Paula Lechuga Sancho, Jose Aurelio Medina-Garrido

The aim of this paper is to carry out a systematic analysis of the literature to show the state of the art of Business Processes Management Systems (BPMS). BPMS represents a technology that automates business processes connecting users with their tasks. For this, a systematic review of the literature of the last ten years was carried out, using scientific papers indexed in the main databases of the knowledge area. The papers generated by the search were later analysed and filtered. Among the findings of this study, the academic interest and the multidisciplinary nature of the subject, as this type of studies have been identified in different areas of knowledge. Our research is a starting point for future research eager to develop a more robust theory and broaden the interest of the subject due its economic impact on process management.

Hongrui Gao, Yawen Li, Meiyu Liang et al.

Because most of the scientific literature data is unmarked, it makes semantic representation learning based on unsupervised graph become crucial. At the same time, in order to enrich the features of scientific literature, a learning method of semantic representation of scientific literature based on adaptive features and graph neural network is proposed. By introducing the adaptive feature method, the features of scientific literature are considered globally and locally. The graph attention mechanism is used to sum the features of scientific literature with citation relationship, and give each scientific literature different feature weights, so as to better express the correlation between the features of different scientific literature. In addition, an unsupervised graph neural network semantic representation learning method is proposed. By comparing the mutual information between the positive and negative local semantic representation of scientific literature and the global graph semantic representation in the potential space, the graph neural network can capture the local and global information, thus improving the learning ability of the semantic representation of scientific literature. The experimental results show that the proposed learning method of semantic representation of scientific literature based on adaptive feature and graph neural network is competitive on the basis of scientific literature classification, and has achieved good results.

Kelly Blincoe, Markus Luczak-Roesch, Tim Miller et al.

This article summarizes the literature on trust of digital technologies from a human-centric perspective. We summarize literature on trust in face-to-face interactions from other fields, followed by a discussion of organizational trust, technology-mediated trust, trust of software products, trust of AI, and blockchain. This report was created for the Science for Technological Innovation Veracity Spearhead supported by New Zealand's National Science Challenges.

Y. Zidane, B. Andersen

Projects often face delays and unnecessary use of time due to various factors and reasons, and hence suffer from unfavourable consequences. The purpose of this paper is to identify the universal delay factors from an intensive literature review, complemented by delay factors in major Norwegian construction projects based on empirical data.,The study in which this paper is based includes an intensive literature review, and semi-quantitative open survey questionnaires. This paper addresses frequency and type of delay factors in construction projects, in Norway based on the survey, and worldwide based on the previous studies.,From the study, the causes of delays facing the Norwegian construction industry are: poor planning and scheduling; slow/poor decision-making process; internal administrative procedures and bureaucracy within project organisations; resources shortage (human resources, machinery, equipment); poor communication and coordination between parties; slow quality inspection process of the completed work; design changes during construction/change orders; sponsor/owner/client lack of commitment and/or clear demands (goals and objectives); late/slow/incomplete/improper design; office issues; and users’ issues. And the top 10 universal delay factors are: design changes during construction/change orders; delays in payment of contractor(s); poor planning and scheduling; poor site management and supervision; incomplete or improper design; inadequate contractor experience/building methods and approaches; contractor’s financial difficulties; sponsor/owner/client’s financial difficulties; resources shortage (human resources, machinery, equipment); and poor labour productivity and shortage of skills.,When it comes to the identification of delay factors in major Norwegian projects, the research is based on a sample of 202 respondents from an open survey questionnaire. It should be noted that analysing a large population of respondents that have been asked open questions can be challenging due to the vague findings it might lead to. Also, when it comes to the identification of the universal delay factors, there were different methods used by different authors, within different context. Similar future studies in Norway based on qualitative and quantitative methods will give better verification for the findings.,This paper has documented the critical delay factors/causes in Norway. The results of this study will help project managers, in Norway and elsewhere, to be aware and know about the potential causes of delay in their construction projects, which will help to identify the possible risks in the early phases of the project. Another practical implication is to make project managers and policy makers conscious that delays are quite universal, making it necessary to identify them as a first step.,The identification of delays factors and causes can permit projects to implement mitigation actions to avoid delays, thus allowing delivering schools, hospitals and other necessary infrastructure on schedule or ahead of schedule to society.,This paper highlights most (almost all) of the studies in the literature, including to the study done in Norway, concerning the delay factors in construction projects and large construction projects in general. This wide review of relevant literature will save time other academicians from having to conduct similar studies. This study will assist both academic and professional experts providing more insight about the delay causes in large-scale construction projects.

Qingyu Chen, Jingcheng Du, Alexis Allot et al.

The rapid growth of biomedical literature poses a significant challenge for curation and interpretation. This has become more evident during the COVID-19 pandemic. LitCovid, a literature database of COVID-19 related papers in PubMed, has accumulated over 180,000 articles with millions of accesses. Approximately 10,000 new articles are added to LitCovid every month. A main curation task in LitCovid is topic annotation where an article is assigned with up to eight topics, e.g., Treatment and Diagnosis. The annotated topics have been widely used both in LitCovid (e.g., accounting for ~18% of total uses) and downstream studies such as network generation. However, it has been a primary curation bottleneck due to the nature of the task and the rapid literature growth. This study proposes LITMC-BERT, a transformer-based multi-label classification method in biomedical literature. It uses a shared transformer backbone for all the labels while also captures label-specific features and the correlations between label pairs. We compare LITMC-BERT with three baseline models on two datasets. Its micro-F1 and instance-based F1 are 5% and 4% higher than the current best results, respectively, and only requires ~18% of the inference time than the Binary BERT baseline. The related datasets and models are available via https://github.com/ncbi/ml-transformer.

Attila Varga

It is a matter of debate whether a shrinking proportion of scholarly literature is getting most of the references over time. It is also less well understood how a narrowing literature usage would affect the circulation of ideas in the sciences. Here we show, that the utilization of scientific literature follows dual tendencies over time: while a larger proportion of literature is cited at least a few times, citations are also concentrating more on the top of the citation distribution. Parallel to the latter trend, a paper's future importance increasingly depends on its past citation performance. A random network model shows that the citation concentration is directly related to the greater stability of citation performance. The presented evidence suggests that the growing heterogeneity of citation impact restricts the mobility of research articles that do not gain attention early on. While concentration grows from the beginning of the studied period in 1970, citation dispersion manifest itself significantly only from the mid-1990s when the popularity of freshly published papers has also risen. Most likely, advanced information technologies to disseminate papers are behind both of these latter trends.

Eva Birgitte Aamodt, Eva Birgitte Aamodt, Till Schellhorn et al.

Background: Neurocognitive disorder (NCD) is common after stroke, with major NCD appearing in about 10% of survivors of a first-ever stroke. We aimed to classify clinical- and imaging factors related to rapid development of major NCD 3 months after a stroke, so as to examine the optimal composition of factors for predicting rapid development of the disorder. We hypothesized that the prediction would mainly be driven by neurodegenerative as opposed to vascular brain changes.Methods: Stroke survivors from five Norwegian hospitals were included from the “Norwegian COgnitive Impairment After STroke” (Nor-COAST) study. A support vector machine (SVM) classifier was trained to distinguish between patients who developed major NCD 3 months after the stroke and those who did not. Potential predictor factors were based on previous literature and included both vascular and neurodegenerative factors from clinical and structural magnetic resonance imaging findings. Cortical thickness was obtained via FreeSurfer segmentations, and volumes of white matter hyperintensities (WMH) and stroke lesions were semi-automatically gathered using FSL BIANCA and ITK-SNAP, respectively. The predictive value of the classifier was measured, compared between classifier models and cross-validated.Results: Findings from 227 stroke survivors [age = 71.7 (11.3), males = (56.4%), stroke severity NIHSS = 3.8 (4.8)] were included. The best predictive accuracy (AUC = 0.876) was achieved by an SVM classifier with 19 features. The model with the fewest number of features that achieved statistically comparable accuracy (AUC = 0.850) was the 8-feature model. These features ranked by their weighting were; stroke lesion volume, WMH volume, left occipital and temporal cortical thickness, right cingulate cortical thickness, stroke severity (NIHSS), antiplatelet medication intake, and education.Conclusion: The rapid (<3 months) development of major NCD after stroke is possible to predict with an 87.6% accuracy and seems dependent on both neurodegenerative and vascular factors, as well as aspects of the stroke itself. In contrast to previous literature, we also found that vascular changes are more important than neurodegenerative ones. Although possible to predict with relatively high accuracy, our findings indicate that the development of rapid onset post-stroke NCD may be more complex than earlier suggested.

Souvik Das, Sougata Saha, Rohini K. Srihari

The Covid-19 pandemic has caused a spur in the medical research literature. With new research advances in understanding the virus, there is a need for robust text mining tools which can process, extract and present answers from the literature in a concise and consumable way. With a DialoGPT based multi-turn conversation generation module, and BM-25 \& neural embeddings based ensemble information retrieval module, in this paper we present a conversational system, which can retrieve and answer coronavirus-related queries from the rich medical literature, and present it in a conversational setting with the user. We further perform experiments to compare neural embedding-based document retrieval and the traditional BM25 retrieval algorithm and report the results.

Antonio Jimeno Yepes, Xu Zhong, Douglas Burdick

Scientific literature contain important information related to cutting-edge innovations in diverse domains. Advances in natural language processing have been driving the fast development in automated information extraction from scientific literature. However, scientific literature is often available in unstructured PDF format. While PDF is great for preserving basic visual elements, such as characters, lines, shapes, etc., on a canvas for presentation to humans, automatic processing of the PDF format by machines presents many challenges. With over 2.5 trillion PDF documents in existence, these issues are prevalent in many other important application domains as well. Our ICDAR 2021 Scientific Literature Parsing Competition (ICDAR2021-SLP) aims to drive the advances specifically in document understanding. ICDAR2021-SLP leverages the PubLayNet and PubTabNet datasets, which provide hundreds of thousands of training and evaluation examples. In Task A, Document Layout Recognition, submissions with the highest performance combine object detection and specialised solutions for the different categories. In Task B, Table Recognition, top submissions rely on methods to identify table components and post-processing methods to generate the table structure and content. Results from both tasks show an impressive performance and opens the possibility for high performance practical applications.