dataesr/scientific-paragraphs-categorization huggingface dataset:
The next challenges are how best to use and analyse these data, and the importance of harmonizing worldwide cohorts in order to better compare inter-study findings and have public policy applications. Quality and valid RWD must use standardized and harmonized codes, algorithms, terminologies and vocabularies, especially when pooling data from different data sources (electronic health records, billing or hospitalisation databases, etc. ). This is defined as the Common data models or formats  (Reisinger et al. , 2010; Schneeweiss et al. , 2020). Having harmonized cohorts ready to be used will further expedite investigations, and allow for near real-time queries, and greater impact on patient care. Finally, RWE also has the potential to fill unmet needs in subpopulations routinely excluded from clinical trials such as pregnant women, children, and the very old with multicomorbidities as well as for sub-groups underrepresented in studies such as those with limitations, immigrants, racialized groups, and First Nations.
Today, large datasets and big-data analyses are crucial to advancing research and treatments. Hence, it is of utmost importance to have more representative and large cohorts with a longer follow-up to: 1) study real-world prescribing and use of new drugs that have been newly marketed; 2) study rare adverse events for which large sample sizes are required for the analyses; 3) study adverse outcomes or conditions that occur or are diagnosed later such as cancers or metabolic disorders; or 4) assess the impact of sociodemographic variables or insurance plan (access, restrictions, and reimbursement) on the occurrence of adverse health outcomes. RWE improves how research data is organized, accessed or used, which allows for fast-track research and relevance. This is even more important in times of crises such as in the COVID-19 pandemic.
Although observational research is more prone to biases than randomized clinical trials  (Pandis, 2014) , RWE emerging from the use of these large real-life databases would have given immediate findings that could have been used to treat patients with COVID-19 during the very first wave of the pandemic. It would have been especially important when the decision was made in the US to use hydroxychloroquine as an emergency COVID-19 treatment based on theoretical effectiveness  (Thomson and Nachlis, 2020). This led to increased demand and prescriptions for hydroxychloroquine to treat COVID-19 even when later efficacy data showed no effect, which resulted in shortage of the drug to treat indicated conditions such as rheumatoid arthritis or lupus. Patient data confidentiality is often used as the main reason to delay access to real-world billing or hospital data.
The International Cancer Research Partnership (ICRP) is an active network of cancer research funding organizations, sharing information about funded research projects in a common database. Data are publicly available to enable the cancer research community to find potential collaborators and avoid duplication. This study presents an aggregated analysis of projects funded by 120 partner organizations and institutes in 2006-2018, to highlight trends in cancer research funding. Overall, the partners' funding for cancer research increased from $5. 562 billion (bn) US dollars (USD) in 2006 to $8. 511bn USD in 2018, an above-inflation increase in funding. Analysis by the main research focus of projects using Common Scientific Outline categories showed that Treatment was the largest investment category in 2018, followed by Early Detection, Diagnosis, and Prognosis; Cancer Biology; Etiology; Control, Survivorship, and Outcomes; and Prevention.
and Cancer Control). 3 All fields (with the exception of project funding amount) are visible on the ICRP public website, and project funding amount is visible to partners who contribute data. The database includes current and historic projects, enabling researchers to identify potential collaborators and to avoid duplicating previous or existing research. The primary objective of this analysis was to assess if investment in types of research (CSO) and cancer sites had changed significantly between 2006 and 2018. As it was not possible to present detailed trends for all cancer sites within this paper, secondary objectives were to assess trends in investment in CSO categories for a cancer site with the highest percent increase in funding during this timeframe (pancreatic cancer) and to look at detailed trends in research types with the lowest investment levels, namely, prevention (CSO 3) and survivorship (CSO 6).
METHODOLOGY The ICRP database includes projects across the spectrum of cancer research. Projects within the database are for hypothesis-driven research or resources and infrastructure to support these. Some cancer research investments by funding partners (eg, capital expenditure on buildings for cancer research) are not included. Calendar Years (CY)2006-CY2018 were selected for this analysis, as project-level data were complete for all of the largest ICRP member funding organizations or consortia during this timeframe. In particular, to avoid sampling bias, CY2006 was selected as the start year because changes to project and/or award reporting at US National Institutes of Health (NIH) in FY07 are reflected in the ICRP database only from CY2006 onward. Figures for NIH investment in CY2005 and earlier in the ICRP database were not comparable with those for CY2006 and later and could have given rise to artificially low figures for CY2005, thus over-representing the increase for 2018.
For the organizations included in this analysis, in 2018, the majority of $8,396 million (M) USD investment came from organizations that had also been in existence in 2006-data for these organizations were included in full for the period 2006-2018. Nearly $115M USD of the 2018 investment came from organizations whose cancer research programs had started after 2006, thereby representing real new investment in cancer research during this timeframe. Details of projects active in CY2006-CY2018 (inclusive) were extracted from the database and project dollars per CY calculated based on budget start and end dates, and days in a specific calendar year. Cancer site and CSO investments were calculated using percent allocations per project to avoid double accounting. All project funding amounts that were not in USD were converted to USD using the average CY2018 exchange rate, to avoid confounding underlying trends with those based on year-on-year currency fluctuations.
Trends in CSO or cancer site investment were calculated using proportional annual investment, and significance was assessed using regression lines and R 2 values. Statistical analysis suggested that polynomial regressions provided the best fit for the data because most trends were not linear. Normalization of investment figures means that the figures presented here differ from those in individual partner organizations' annual reports, which may represent fiscal years or other accounting conventions. A top-level assessment of the effect of inflation over 2006-2018 was calculated using the CPI Inflation calculator. 4  All projects in the portfolio were coded to one or more cancer sites and CSO categories. Cancer sites used by ICRP 3 consist of 61 site codes, linked to one or more neoplasm codes in the International Classification of Diseases-10.
5 Percent investment in site-specific cancers was compared with global cancer incidence and mortality rates for the same sites for 2018, sourced from the Global Cancer Observatory. 6  2014, the ICRP partners migrated to a new version of the CSO (v2) and all earlier projects in the database were recoded. As part of this migration, a statistical assessment of inter-rater reliability was conducted for both the CSO and cancer sites. [9]  In addition, projects that were wholly or partially relevant to childhood, adolescent, and young adult (CYA) cancers were tagged. Project dollars from organizations that exclusively funded CYA cancers were included in full. Investment in CYA projects from organizations funding research in both childhood and adult cancers was calculated using a combination of the CYA cancer project tag and a filter to exclude any cancer site investment that was not common in childhood, to avoid overstating investment in CYA cancers.
Trends in the Overall Portfolio Over the 10-year period CY2006 there was a peak in funding (not shown), largely attributable to additional investments of the US Government's American Recovery and Reinvestment Act (ARRA), which added around $0. 5-$1bn USD per annum to research investment during this period, primarily to projects in the United States. The analysis included data from 120 funding organizations with active research projects during the time period selected for the analysis. The largest contributor ($7. 3bn USD in 2018) to the overall portfolio was the US NIH, including $M $500M $1,000M $1,500M $2,000M $2,500M $3,000M 1 -Biology 2 - R 2 = 0. 6759 R 2 = 0. 909 R 2 = 0. 8856 R 2 = 0. 8503 R 2 = 0. 8918 R 2 = 0. % Annual Investment R 2 = 0. 9669 R 2 = 0. 865 R 2 = 0. 2784 R 2 = 0. 9524 R 2 = 0.
19 Although charities or private funders make up only around 7%-10% of the ICRP portfolio, any reduction in investment from this sector will be significant globally. This analysis provides a baseline from which the potential impact of reduced research budgets because of COVID-19 can be measured. We anticipate that a full analysis of the impact of COVID-19 on research investment in 2020 and 2021 will be possible in 2023. Cancer Site R 2 = 0. 42 R 2 = 0. 6401 R 2 = 0. 8872 R 2 = 0. 5353 R 2 = 0. 9151 R 2 = 0. The ICRP partners welcome additional data from other funders to help complete the picture of global cancer research funding and to help ensure that cancer research investments are evidence-based and focused strategically at an international level.
Data analysis We applied Spearman correlation analysis to compare the raw blood biomarker values obtained in the reference conditions. Biomarker values obtained under experimental conditions were normalized against their reference condition (%recovery). We calculated group median %recovery per experimental condition. When the median changed > 10%, we considered this a relevant (clinically significant) change. This arbitrary value was chosen to balance intra-assay variability inherent to fluid biomarker measurements (generally < 10%), with the observation that plasma Aβ42/40 is decreased by only ≈20% in patients with AD compared to controls. 9 Data were visualized using R version 1. 1. 463.
ACKNOWLEDGMENTS We thank all GBSC members that participated in the inventory study, and all volunteers that donated blood for our research purposes. Further, we acknowledge all beyond the authors on this paper who provided support and guidance in conducting this study, in random order and not limited to  Zulaiga Hussainali  ( Amsterdam UMC ),  Lynn  ( 2017-0383 ), the  Marianne  and  Marcus Wallenberg  foundation (2015. 0125), the  Strategic Research Area MultiPark (Multidisciplinary Research in Parkinson's disease) at Lund University , the  Swedish Alzheimer Foundation  ( AF-939932 ), the  Swedish Brain Foundation  ( FO2019-0326 ), The  Parkinson foundation of Sweden  ( 1280/20 ), the  Skåne University Hospital Foundation  ( 2020-O000028 ),  Regionalt Forskningsstöd  ( 2020-0314 ), and the  Swedish federal government  under the ALF agreement ( 2018-Projekt0279 ).
Source of Data The study collected experimental and survey data. Risk attitudes were elicited using an incentivized risk-elicitation task, the panel lotteries originally proposed by  [36] , S-GG hereafter. We built on the original S-GG by introducing an additional three treatments. Thus, our panel lotteries have four treatments, each with the nomenclature small gain one (SG1), small gain two (SG2), large gain one (LG1), and large gain two (LG2). Each treatment has four panels each. Readers are referred to the working paper on the implementation note on S-GG panel lotteries for the advantages and limitations of this risk elicitation method  [37]  et. Applications of the previous version of S-GG panel lotteries can be found in  [38] [39] [40]  Rice farmers were sampled across 46 different rice growing locations within the 4 agricultural zones in Ogun State Agricultural Development Programme (OGADEP), Nigeria between March and May, 2016.
Two smart android phones, aided by open data kit (ODK collect) software were used for data collection. Thus, the geographical information system (GPS) coordinates were recorded. Notwithstanding the poor or the absence of mobile networks in most visited villages, the locations of each sampled farmer were manually recorded, and the record was later used to obtain the coordinates. Three post-graduate students were employed as research assistants for the data collection. They were trained shortly before the commencement of the survey on how to use the ODK collect. Moreover, useful information on the main rice growing locations was collected from the OGADEP office prior to the survey. On many occasions the extension agents accompanied the researchers and provided guidance on rice growing locations.
16 The Icarus package in R was used to adjust data sample weights iteratively for the aforementioned variables using raking. 17 swers to the open-ended questions were analyzed using inductive content analysis. 18 We coded participants' responses with the aim to identify the circumstances in which each alternative care modality was considered an appropriate replacement of traditional care. A preliminary coding scheme was developed by 1 of the authors (T. O. ) based on analysis of the first 250 responses and the literature. 19 This coding scheme was reviewed by another author (D. B. ), who used it to independently code 25% of the 250 responses. The authors compared codes and arrived at a consensus for the preliminary coding scheme, which was then used to code all remaining responses.
JAMA Network Open | Health Policy Preferences for Alternative Care Modalities Among French Adults With Chronic Illness (T. O. ). New codes were created as needed, 20% of the data set was independently coded as quality control (D. B. ), and the authors resolved discrepancies. When all data were coded, codes that described similar concepts were clustered (T. O. , D. B. and V. T. T. ). For additional information, see the living codebook of the study. 13, 20 We used a predictive modeling method to estimate the degree of data saturation for each question  21  to determine the number of additional responses that would have to be analyzed to detect 1 additional code.
Strengths and Limitations This study has several strengths. This is the first study, to our knowledge. to examine patients' vision of postpandemic care and identify appropriate uses of alternative care modalities. We analyzed responses from 1529 patients. The model used to predict data saturation indicates that if we doubled the number of participants in our study, we would have identified no additional appropriate and inappropriate uses for teleconsultations, 2 additional uses for symptom-checkers, and 3 for remote monitoring (eFigure 3 in the Supplement). Our diverse sample included older and multimorbid patients and was weighted to reflect the general population of chronically ill patients more closely. This study also has limitations. Because ComPaRe is an e-cohort, all participants have internet access.
Adverse outcome pathways The adverse outcome pathway (AOP) approach helps to assemble current knowledge on well-accepted critical events linked to disease progression. The approach is being used in chemical toxicology to understand key events (KEs) causing diseases that are of relevance to the human population  (Villeneuve et al. 2014). AOPs begin with a molecular initiating event (MIE) defined as the first interaction of the stressor with a cell. This initiation then leads to downstream KEs that are critical hallmarks along the path to the adverse outcome (AO). A KE is represented at the molecular, cellular, tissue, organ, and individual level and linked by key event relationships (KERs) that are supported by in silico, in vitro, and in vivo experimental data  (OECD 2016a). The Organisation for Economic Cooperation and Development (OECD) launched a program in 2012 to support the AOP approach  (Ankley et al.
The goal is to compile key evidence, focusing on studies that support the evidence streams of the modified Bradford-Hill criteria for causality  (Becker et al. 2015). The working group agreed that the best path forward was to examine the entire body of evidence, irrespective of dose, dose-rate and ionizing radiation types. Particularly, since the evidence in the area of low dose and low dose-rate (<100 mGy, <5 mGy/h) is somewhat still limited and sometimes variable, it would be practical to start structuring findings from high doses (1-5 Gy) where evidence would be rich and more consistent  (Tapio et al. , 2021). It was also noted that datasets for both acute and chronic radiation exposures could potentially be available. However, there is uncertainty on the extent that this data meets the stringent requirements of the modified Bradford-Hill criteria for incorporation in AOP.
Proposed hypothetical AOP Based on the four-day discussion, it was evident that mapping out a simplified, linear, unidirectional AOP from deposition of energy from radiation exposure to CVD would be challenging. As a result, the proposed AOP evolved from a complex pathway to three distinct sub-classes of diseases for AOP development: vascular remodeling, myocardium pathology, and fibrosis (Figure  2 ). Deposition of energy is designated as the MIE in this AOP and leads to a KE of increased oxidative stress at the macromolecular level. Oxidative stress would be instrumental in the initiation of further tissue-and organ-level responses resulting in inflammation, an event that contributes to the more severe CVD adverse outcomes. This proposed AOP is a starting point for the collation of data and appropriate studies meeting the modified Bradford-Hill criteria.
Next steps include the compilation of evidence to support the KERs (linkages) in the AOP. That will need to be done systematically and transparently to identify literature that supports the dose-, time-and incidence-concordance as well as biological plausibility. Work is also required to identify the inconsistencies and controversies in the published literature. Following this evidence review and mapping, the AOP could be refined to those KEs that are data-rich and mechanistically well-defined. Eventually, the KEs that lack sufficient data or have only emerging data could be excluded.
Conclusions The CVD working group discussions led to productive conversations and highlighted important processes and pathways for consideration in AOP development for IR-induced cardiovascular effects. Discussions centered on the scope of building AOPs, the potential KEs, MIE, data composition, and relevant scenarios of exposure. Knowledge gaps in the area of low dose and low dose-rate (<100 mGy, <5 mGy/h) irradiation were highlighted, emphasizing the need for more studies in the context of the cellular communication where the interplay of cardiac cells contributes to heart injury following exposure  (Tapio et al. 2021). Moreover, the retrospective studies on archival autopsies such as collected from nuclear workers can be a better alternative for animal or cellular models to investigate the late and lasting effect of low dose, chronic radiation exposure on the human heart  (Rybkina et al.
2014; Azimzadeh, Azizova, et al. 2017; Azimzadeh et al. 2020). The AOP proposed here allows for structuring and simplification of the available mechanistic information about radiation-induced CVD and can facilitate predictive interpretations, beyond cellular or animal models, at the human population level. Collation of data will provide the linkages from macromolecular events to population relevance using the large body of epidemiological data supporting the relationship between IR and CVD, thus making this AOP directly relevant for regulatory applications. Overall, organization of the literature surrounding this disease within an AOP framework could provide avenues for prioritizing experiments and targets for the development of mitigation strategies to address the population health issues.
Acknowledgements The authors are grateful to  Dr. Minna Kaikkonen-M€  a€ att€ a,  Dr. Sami Qutob  and  Dr. Katya Feder  for a critical review and constructive comments of the manuscript. The contents of this manuscript neither constitute, nor necessarily reflect the views of the authors' institution of employment. This work was supported by the  Human Research Program of the Human Exploration and Operations Mission Directorate of the National Aeronautics and Space Administration  [JLH] and by the  Human Health and Performance  contract  NNJ15HK11B  [ZSP].
We construct a novel database containing hundreds of thousands geotagged messages related to the COVID-19 pandemic sent on Twitter. We create a daily index of social distancing -at the state level -to capture social distancing beliefs by analyzing the number of tweets containing keywords such as "stay home", "stay safe", "wear mask", "wash hands" and "social distancing". We find that an increase in the Twitter index of social distancing on day t-1 is associated with a decrease in mobility on day t. We also find that state orders, an increase in the number of COVID cases, precipitation and temperature contribute to reducing human mobility. Republican states are also less likely to enforce social distancing. Beliefs shared on social networks could both reveal the behavior of individuals and influence the behavior of others.
Twitter Data We use geotagged messages from Twitter to capture beliefs at the state level. 3 To construct our database, we use a web scraping tool, and we extract all tweets containing the following keywords: "stay home", "stay safe", "save lives", "wash hands", "wear mask" and "social distancing" 4. We find that the number of messages protesting against social distancing measures is very low: the percentage of tweets containing keywords or hashtags such as #ReOpenAmerica, #LockdownProtest (or related keywords) represents less than 1% of all tweets in our sample. As more than 99% of the tweets containing social distancing keywords 3 We focus on the state level instead of the county level, as the number of tweets we collect was too low to construct a reliable daily indicator of information at the county level are encouraging social distancing, we do not use a sentiment analysis algorithm to derive the polarity of each message.
(2015) , and geotagged tweets are written more often by young people and by women  Pavalanathan and Eisenstein (2015). While we acknowledge that this could limit the generalizability of our findings, we add state fixed effects, and we focus on variation across states and over time to limit the bias due to the specific sample of Twitter users. Geotagged messages only represent 1 to 2% of all messages sent on Twitter every day. However, given the very large number of messages sent every day on the platform (approximately 500 million tweets), we still obtain a large database of 402,005 messages containing at least one social distancing keyword sent between February 15 and May 31, 2020. Tweets in our sample have an average of 9. 21 likes, 1. 84 retweets and 0. 62 replies. Appendix 2 shows the evolution of the total number of tweets related to social distancing during our sample period.