dataesr/scientific-paragraphs-categorization huggingface dataset:
Network building and collaboration CanScreen5 aims to collect information and data directly from each country's Ministry of Health (MoH). IARC's existing network of research collaborators across the globe is leveraged to reach out to the MoH. The contact person in the MoH is requested to identify program coordinators or other experts capable of providing reliable information and data. We also liaise with WHO regional offices to establish contact with the MoH. If such contact with the MoH cannot be established in a particular country, we approach the academic or public health institutes (or both) associated with the implementation and evaluation of a screening program to identify potential data providers.
Training of potential data providers Identified data providers complete a self-paced virtual learning module. The module describes the objectives of the project, how to collect data using the data collection tools and how to submit the same online to the CanScreen5 portal. The definition of the various performance indicators and how those indicators will be estimated in the project are also explained. The e-learning modules, which are available free of charge on the IARC website (https://learning. iarc. fr/edp/courses/ pgm-cancer-screening/), go beyond just describing the methodology of the project. The modules cover principles of cancer screening, planning and implementation of screening programs, and particularly focus on the principles, steps and value of quality assurance in the context of cancer screening programs.
Data collection To be able to submit data to CanScreen5, a country (or a region within the country) should have a 'screening program' as per the CanScreen5 definition. The project defines a screening program as one characterized by having at least a formal commitment from the health authorities to provide screening services to a defined eligible population  27. This commitment must be documented as a law, an official notification or a recommendation. A documented screening protocol and a mechanism of monitoring and supervision are also required to fulfill the criteria of being a screening program. Data providers can download the qualitative and quantitative data from the project portal, which is available in English, French, Russian and Spanish, to collect information and data from breast, cervical and CRC screening programs separately.
Qualitative, freely downloadable data tools available on the portal are used to collect information on screening policies and protocols, governance and financing mechanisms, systems of invitation and recall, process of data collection for program monitoring and protocol for quality assurance. The set of data collection tools includes the corresponding guideline on each item; definitions of key terms are provided (https://canscreen5. iarc. fr/?page=datasources). Screening performance data are collected in quantitative forms across the screening continuum (from invitation to treatment), from national or regional programs. At the time of data submission, the data provider has to specify whether they are reporting for a national or a regional program. During our communications with them, after data submission, we further confirm whether the collected data reflect the entire country or a region only.
The minimum set of quantitative data requested from the programs is the number of individuals screened and the outcomes of the screening tests. Data are not processed further unless this minimum dataset is available from a program. Data providers are advised to submit the most recent qualitative information on the screening program and quantitative data for any year within the last 5 years. Data may be submitted for multiple consecutive years, if available. Selection of the year(s) for which quantitative data are submitted is at the discretion of the data provider based on the completeness of the data (including follow-up of screen-positive individuals). A data provider who does not have quantitative data may submit only qualitative information related to the program.
Data quality checks and data validation process Data submission by any of the data providers to the CanScreen5 data platform triggers a notification to the IARC Secretariat that initiates the internal validation to check for data consistency, completeness and validity. Submitted information is cross-verified with information available from the policy and protocol documents of the programs. The Secretariat tries to resolve any queries or discrepancies through email exchanges and virtual meetings with the data providers. The project has a scientific committee (SC) consisting of 15 international experts in the field of cancer control. The internally validated data from each country are shared with two SC members to be reviewed independently. The data provider is contacted again to resolve any queries from the SC reviewers.
Key definitions All the quantitative indicators have been clearly defined on the Can-Screen5 portal. The numerators and denominators needed to calculate each indicator are described in Supplementary Table  4. The data used for the numerator and denominator to estimate any indicator should be collected over the same time period (a particular year(s) or one round of screening); an individual tested twice during the specified period should be counted only once during that period. Even the terms used in the qualitative data collection form have been clearly defined on the portal to ensure harmonization of data collection. Some of these key definitions are given below.
It includes the assessment or evaluation of quality, the identification of problems or shortcomings in the delivery of care, the design of activities to overcome these deficiencies and follow-up monitoring to ensure the effectiveness of corrective steps. Quality assurance of the screening process requires a robust system of program management and coordination, ensuring that all aspects of the service are performing adequately. Essential criteria for organized screening. The qualitative data collection tools collect data to assess 13 of the following 16 essential criteria identified by an IARC expert group to define organized cancer screening:  17  (1) the cancer screening program has a protocol or guideline describing at least the target population, screening intervals, screening tests, referral pathway and management of positive cases; (2) there is a system in place for identifying the target population; (3) there is a system in place for inviting eligible individuals for screening; (4) the cancer screening program has a policy framework from the health authorities defining governance structure, financing, and the goals and objectives of the program; (5) performance of the screening program is evaluated with appropriate indicators; (6) the protocol or guideline at least describes monitoring and evaluation; (7) there is a system in place for notifying the results to the screened individuals and informing them about follow-up; (8) there is a system in place for sending a recall notice to noncompliant individuals; (9) the program can be audited; (10) a specified team or organization is responsible for quality assurance and improvement; (11) the performance of the cancer screening program is evaluated, published and widely disseminated on a regular basis; (12) all activities along the screening pathway are planned, coordinated and evaluated through a quality improvement framework (quality assurance); (13) there is an evidence-based protocol or guideline developed in consensus with most stakeholders; (14) an information system exists with appropriate links between population databases, screening information and cancer registries for screening implementation and evaluation; (15) the screening program has a provision for continued training for service providers; (16) the performance of the screening program is evaluated with reference standards for the indicators.
Acknowledgements This study is supported by the  IARC  intramural funding;  European Union Public Health Programme  (scientific and technical support to the  European Partnership for Action Against Cancer  and follow-up of the implementation of the  Council Recommendation on Cancer Screening) ;  Centre for Global Health Inequalities Research, Norway ;  American Cancer Society ;  National Cancer Institute, United States of America ; and  Medical Research Council, United Kingdom. We acknowledge all advisory board members:  S. Kapambwe ;  S. Al-Homoud ;  I. Fadhil ;  Y. L. Qiao ;  R. Sankaranarayanan ;  A. Anttila ;  J. Dillner ;  T. Andreas Eikemo ;  H. de Koning ;  A. Ponti ;  S. Arrossi ;  R. Herrero ;  A. Migowski ;  R. Murillo ;  S. Luciani ;  D. Puricelli Perrin ;  L. Rabeneck ;  R. Smith ;  S.
The COSMOS Survey The COSMOS survey covers 2 deg 2 and boasts a plethora of deep multiwavelength data in over 40 bands from the world's major facilities  (Scoville et al. 2007). The new version of the COSMOS source catalog, COSMOS2020  (Weaver et al. 2022 ),  15  constitutes a major improvement over the previous catalog  (Laigle et al. 2016) , in that it includes detections and new ultra-deep optical/NIR imaging and multiwave band photometry of 1. 7 million sources over the entire COSMOS field, with ∼89,000 measured in all of the available broadband filters. Key additions for COSMOS2020 include new ultradeep optical data from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) public data release 2 (PDR2;  Aihara et al. 2019) , new data from the Visible Infrared Survey Telescope for Astronomy (VISTA) data release 4 (DR4;  McCracken et al.
2012) , reaching up to one magnitude deeper over the full area than previous data, and the inclusion of all Spitzer IRAC data in COSMOS  (Moneti et al. 2021). Legacy data sets (such as the Suprime-Cam imaging) have also been reprocessed (see  Weaver et al. 2022 for details). COSMOS2020 consists of two independent photometric catalogs. The first is the Classic catalog, where standard aperture photometry is performed  (Bertin & Arnouts 1996)  on point-spread function (PSF)-homogenized optical/NIR images, except for the IRAC images where the software IRACLEAN  (Hsieh et al. 2012 ) is used to do PSF photometry. The second catalog is created using a new profile-fitting photometric software developed specifically for COSMOS2020 called THE FARMER. Using The Tractor software  (Lang et al. 2016)  for the source modeling, THE FARMER generates reproducible source detection and photometry to generate a full multiwavelength catalog.
Physical Parameters: LePhare and EAZY The physical properties of the COSMOS2020 galaxies, such as stellar masses and SFRs, are also available from the two relevant codes (see  Weaver et al. 2022  for details on how these quantities are calculated). In this paper, however, we did not use the SFR estimates provided by the two codes. Tests showed significant run-to-run variation in the SFR estimates, particularly for LePhare, and between the two codes. In the case of LePhare, the code is run twice, once to obtain the photoz and then again with the photoz fixed to obtain physical parameters such as stellar mass and SFR. For more details, see Section 6 in  Weaver et al. (2022). LePhare imposes a declining SFH for both star-forming and quiescent galaxies (though at the redshifts we are considering, the delayed SFH is likely to be in the rising SFR epoch), whereas EAZY makes no assumption about the SFH.
We, therefore, chose to discard redshift bins higher than z = 9. 23 since beyond this cutoff, it is not possible to say anything meaningful about the overdensities. This is because a large number of the galaxies in the higher bins (z = 9. 59 ± 0. 18 and z = 9. 81 ± 0. 19) either have a large fraction of its p(z) outside the z = 10 boundary for LePhare or in the case of the highest bin, the p(z) peaks right at the z = 10 boundary showing the code is not adequately able to model the probability distribution for these sources.
5. We show a trend in the evolution of the stellar masses between the z = 6. 05 bin and the higher-z bins by comparing their histograms and CDFs, with moremassive galaxies at lower redshift. Combined with a higher specific SFR for the candidates in the higherredshift bins, we appear to trace the evolution of the protocluster galaxies from being more efficiently starforming at z > 6 to more massive at z = 6. Many of the protocluster candidates presented here will be covered by COSMOS-Web (Cycle 1, ID. #1727), a large program on the James Webb Space Telescope (JWST), which will survey the COSMOS field using the MIRI and NIRCam instruments. Our results emulate what will be possible with Euclid combined with large ground-based optical surveys, which, via deep NIR imaging of tens of square degrees, will uncover dozens of z 6 protocluster targets.
Following these up with spectroscopic campaigns using 8-10 m class telescopes on the ground, JWST, and ALMA will offer uniquely detailed studies of the assembly processes of protoclusters and their galaxies during reionization. The Cosmic Dawn Center (DAWN) is funded by the Danish National Research Foundation under grant No. 140. We gratefully acknowledge the contributions of the entire COS-MOS collaboration consisting of more than 100 scientists in constructing the COSMOS2020 catalog as well as for the feedback and discussion. The research with the COSMOS survey is also partly supported by the Centre National d'Etudes Spatiales (CNES). T. R. G. and M. B. are grateful for support from the Carlsberg Foundation via grant no. CF20-0534. galaxies), (e)-(f) z = 6. 47 ± 0. 11 (95 galaxies), (g)-(h) z = 6.
Observations and Data analysis We selected Fermi-LAT events inside the 15 • × 15 • regions centered at the positions of the SNRs listed in Table  1  from the Fermi Pass 8 (P8R3) public database  [1]. The LAT data we downloaded spans in time from 2008/09 to 2022/09. The publicly available software fermitools (version 2. 0. 8) and the python package fermipy (version v1. 0. 1) were used to analyze the data. We selected only event class data (P8R3 SOURCE) and type 3 events (i. e. , FRONT+BACK events) and applied standard data quality selection criteria (DATA_QUAL > 0 && LAT_CONFIG == 1) for analyzing the dataset. The zenith angle was restricted to a maximum of 90 • to avoid contamination due to the Earth's limb. We used the latest versions of the instrument response functions (IRFs, version P8R3_SOURCE_V2), the diffuse Galactic interstellar emission (gll_iem_v07.
Large Language Models (LLMs) have recently gathered attention with the release of ChatGPT, a user-centered chatbot released by OpenAI. In this perspective article, we retrace the evolution of LLMs to understand the revolution brought by ChatGPT in the artificial intelligence (AI) field. The opportunities offered by LLMs in supporting scientific research are multiple and various models have already been tested in Natural Language Processing (NLP) tasks in this domain. The impact of ChatGPT has been huge for the general public and the research community, with many authors using the chatbot to write part of their articles and some papers even listing ChatGPT as an author. Alarming ethical and practical challenges emerge from the use of LLMs, particularly in the medical field for the potential impact on public health.
From the optimization of InstructGPT for dialog, ChatGPT was born. Despite these advancements, ChatGPT still sometimes writes plausible-sounding but incorrect or nonsensical answers, due to its inability of fact-checking and its knowledge limited until 2021  (1). In Table  1  we summarize the strengths and weaknesses of large language models that can be considered important steps to understand the development of ChatGPT. The evolution of LLMs is still in its early stages and the release of GPT-4 on the 14th of March 2023 has been another step forward in LLMs' rapid advancement. GPT-4 has already been integrated into ChatGPT and this model seems to be more reliable, creative, and able to handle much more nuanced instructions, even if only a few technical details on the model were provided by OpenAI, for competitive and safety concerns.
7B,  (23, 24)  that were trained exclusively on biomedical abstracts and papers and used for medical question answering and text generation. Med-PaLM, a LLM trained using few-shot prompting, exceeds previous state-of-the-art models on MedQA, a medical question answering dataset consisting of United States Medical Licensing Exam (USMLE) style questions  (25). The performance of ChatGPT on USMLE was recently evaluated and it achieved around 50-60% accuracy across all examinations, near the passing threshold, but still inferior to Med-PaLM  (26). GPT-4 exceeds the passing score on USMLE by over 20 points and outperforms earlier LLMs. Nevertheless, there is a large gap between competency and proficiency examinations and the successful use of LLMs in clinical applications  (27). In the NLP task of text summarization GPT-2 was one of the best-performing models used for summarizing COVID-19 scientific research topics, using a database with over 500,000 research publications on COVID-19 (CORD-19)  (28, 29).
CORD-19 was also used for the training of CoQUAD, a questionanswering system, designed to find the most recent evidence and answer any related questions  (30). A web-based chatbot that produces high-quality responses to COVID-19-related questions was also developed, this user-friendly approach was chosen to make the LLM more accessible to the general audience  (31). LLMs have also been used for abstract screening for systematic reviews, this allows the use of unlabelled data in the initial step of scanning abstracts, thus saving researcher's time and effort  (32). LLMs facilitate the implementation of advanced code generation capabilities for statistical and data analytics, two large-scale AI-powered code generation tools have recently come into the spotlight: OpenAI Codex, a GPT language model fine-tuned on publicly available code from GitHub,  (33)  and DeepMind AlphaCode, designed to address the main challenges of competitive programming  (34).
Its ability to write scripts in multiple programming languages and provide clear explanations of how the code works, makes it a useful asset for improving understanding and efficiency. Examples of these ChatGPT's abilities are provided in Figure  1. ChatGPT can also be used to suggest titles, write drafts, and help to express complex concepts in fluent and grammatically correct scientific English. This can be particularly useful for researchers who may not have a strong background in writing or who are not native English speakers. By supplementing the work of researchers, rather than replacing it, automating many of the repetitive tasks, ChatGPT may help researchers focus their efforts on the most impactful aspects of their work. The high interest of the scientific community in this tool is demonstrated by the rapid increase in the number of papers published on this topic, shortly after its release.
Commonly used GPT-2 detectors were flawed in recognizing text written by AI when generated by ChatGPT (50), new detectors were rapidly developed and released to address this gap, but these tools do not perform well in identifying GPT-4 generated text. This poses a continuous unfair competition to improve detectors that need to follow the pace of LLMs' rapid advancement, leaving a gap for malicious intent. As a result, this poses a continuous unfair competition to improve detectors that need to follow the pace of LLMs' rapid advancement, leaving a gap for malicious intent. ChatGPT output to our prompt requesting to make examples of its abilities to support researchers in suggesting research questions, generate hypotheses, writing scripts in multiple programming languages and providing explanations of how the code works.
Discussion Large language models have already shown hints of their potential in supporting scientific research and in the next months we expect a growing amount of papers talking about the use of ChatGPT in this field. The accessibility and astonishing abilities of ChatGPT made it popular across the world and allowed it to achieve a milestone, setting AI conversational tools to the next level. But soon after its release possible threats emerged, ChatGPT's ability to follow user's instruction is a double-edged sword: on one hand, this approach makes it great at interacting with humans, on the other hand being submissive ab origine exposes it to misuse, for example by generating convincing human-like misinformation. The field of medical research may be a great source for both opportunities and threats coming from this novel approach.
As the data processing is suspected to potentially have a significant impact on the results, various software solutions were considered. Two of those solutions (UNIFI and waters_connect) are compliant with reg-ulatory requirements in terms of data integrity (including 21 CFR Part 11)  [24, 25] , and an open-source solution (Skyline) was tested in parallel. UNIFI can be used for the process of all MS peptide mapping data, while waters_connect contains an additional application, Peptide MAM, specifically dedicated to MAM processing. This adds more performance and flexibility to the process. Skyline is open-source software with full flexibility and control over the process, but it cannot be used in a regulated environment. Such a comparison of different data treatment procedures has never been reported before in the context of peptide MAM.
Study Design The peptide mapping of a reference mAb product was conducted in three different laboratories equipped with strictly similar benchtop LC-MS equipment. In the present study, the different samples were prepared in one single laboratory, and aliquots of the digests were shipped to the other participants. As a result, any observed variability could not be attributed to the tryptic digestion procedure, which is known to be one of the potential sources of variability in peptide MAM workflows  [17]. In addition, the data treatment was also performed in one unique laboratory having a high degree of expertise, and various software solutions were considered to evaluate its impact, including two 21 CFR Part 11-compliant software solutions, UNIFI and waters_connect, and an opensource solution, Skyline.
8 and 1. 2), because of similar hydrophobicity and overlapping mass spectra. This limited resolution makes the integration of the peak of interest (eluted at 34. 30 min) difficult. Depending on the way the integration is performed by the software (the software employed in this work can either perform an automated integration of peaks based on settings entered by the user (UNIFI and waters_connect), or a manual integration set by the users (Skyline)), the peak areas of the native and deamidated peptides can vary, leading to differences in the percentage of deamidation, as highlighted in Figure  2. In addition, some minor differences in terms of C18 column selectivity and the LC pumping system between the three different instruments can also be responsible for the slight changes in selectivity/resolution observed between the different instruments (see Figure  2 ).
In all cases, the percentage of the C-terminal lysine variant was very low and comparable, with an average amount of 2. 1%, and values ranging from 1. 5 to 2. 5%. Interestingly, no statistical difference was observed between the three instruments. However, it appears that one of the software solutions (UNIFI) systematically underestimates the amount of C-terminal lysine variant by about 0. 7%.