dataesr/scientific-paragraphs-categorization huggingface dataset:
To implement this concept in practice, we first searched for a suitable source of historic rainfall data that could be used to categorize past seasons. We identified the CHIRPS dataset, which has global coverage and is open access  (Funk et al. , 2015). CHIRPS daily rainfall estimates are derived from geostationary infrared satellite retrievals of cloud top temperature, merged with available rain gauge data. Data can be accessed through a global map interface at ClimatSERV 1  , a data service jointly offered by NASA and USAID, allowing users to download daily rainfall estimates at country and sub-country levels. Next, we identified a seasonal climate forecast product that best serves our design criteria. We chose the Copernicus Climate Change Service (C3S) multi-model forecast 2  because it provides a probabilistic rainfall forecast (probabilities of a rather dry, average, or rather wet season) and visualizes these forecasts as global maps.
In addition to being globally applicable and freely accessible online, our evaluation showed that a probabilistic map was understood by seed sector decisionmakers. In stakeholder consultations with Seed Co, and to address our design criteria, we decided to implement all analyses in one Microsoft Excel workbook, with embedded weblinks to ClimatSERV (to download rainfall data) and C3S (to obtain the most recent seasonal climate forecast). The workbook comprises six sheets: Sheet 1: Enter sales data. The user is asked to provide data on historic seed demand by variety and per annum (seed sold or distributed in the region of interest). Data can be supplied for up to 50 years and up to ten different crop varieties (cf. Step 1 in Fig. 1 ). Sheet 2: Get rainfall data. This sheet includes the weblink to Cli-matSERV.
The second result resembles the first but provides statements for each individual variety (cf. step 6 in Fig. 1 ). We created a first prototype of this Excel workbook and refined its design in multiple iterations of user testing. We performed user evaluations by presenting early versions to decision makers at Seed Co and collecting qualitative feedback. We also performed independent tests with two users previously unfamiliar with the entire concept, using the think aloud method  (Jaspers et al. , 2004). These test users (one woman, one man) were a geographer and a biologist based in Germany, with no pre-existing expertise on smallholder seed systems. These interactions were especially helpful for simplifying the language (avoiding jargon) and adding instructions to the prototype. After each test, we adapted features of the Excel workbook.
Case study with Seed Co (Zimbabwe) To understand the usefulness and define potential use cases of our prototype, we explored it in practice and with real world data, together with decision-makers at Seed Co in Zimbabwe. This case study was carried out in mid-2020, aiming at forecasting seed demand in the November-December-January (NDJ) season 2020/21. We compiled historic sales data on five Seed Co maize varieties with different times to maturity (ranging from 'ultra-early' to 'late'). For each variety, sales records were aggregated per calendar year across all of Zimbabwe, covering the years 2012 through 2019. Because Zimbabwe has one planting season typically centered on NDJ, aggregated seed sales recorded in a calendar year can be expected to be associated with the agricultural season starting that same year.
We downloaded daily precipitation estimates, averaged over Zimbabwe, from ClimatSERV (see previous Section) for the period of 1993-2019. For each year in the reference period, cumulative rainfall over the forecast season was calculated (i. e. , November 1 to January 31). Because the reference period used by the C3S seasonal forecast to calculate tercile thresholds is 1993-2016 by default, we used this 24year period to calculate tercile thresholds for Zimbabwe, too. Using these thresholds (346 mm and 441 mm), each of the seasons 2012-2019 (for which seed demand data were available) was classified as either dry, average, or wet. In addition, seasonal rainfall for a 'typical' dry, medium, and wet season was calculated by taking the median of the eight reference seasons per tercile: 315 mm (dry), 388 mm (medium), and 506 mm (wet).
Validation exercise with Oromia Seed Enterprise and Ethiopia Seed Enterprise (Ethiopia) To ensure the scenario planning procedure can be used by users previously unfamiliar with the concept, we facilitated a one-day workshop with decision-makers from two public seed enterprises in Ethiopia. Rather than gradually implementing and iteratively improving the decision support procedure, as we did with Seed Co in Zimbabwe, we aimed at carrying out the entire procedure in one go. Three decisionmakers from each Oromia Seed Enterprise (OSE) and Ethiopia Seed Enterprise participated, and the exercise used seed demand data from the East Wellega zone, provided by OSE. During this test, we continuously collected qualitative feedback from participants. We recorded their assumptions and expectations around the procedure and its outputs.
While there are relevant resources that require registration, our procedure was built around using CHIRPS rainfall estimates and the C3S rainfall forecast, which are free and immediately accessible. Low time investment: Some time commitment for becoming familiar with the Excel tool and the online resources, as well as for compiling the seed demand data seems inevitable for applying the procedure properly. However, we minimized overall time requirement by suggesting the assignment of a workshop facilitator. We estimate the facilitator needs about two hours for preparing the workshop, and additional time for compiling and cleaning seed demand data, which will depend on the level of data standardization and curation within the seed supply organization. The other participants (about 3-5 persons) should spend about three hours in the workshop.
Actionable output: Feedback from decision-makers in Zimbabwe and Ethiopia suggested that results are well understood and are perceived as useful inputs to discussions around the prioritization of varieties in postproduction treatment, as well as in allocation to different warehouses or depots. Until now, however, the relatively rigid, linear succession on operations in seed supply limits the scope for using the outputs of seasonal seed scenario planning in adaptive decision-making. Universal application: We deliberately focused on a near-global rainfall dataset (CHIRPS) and a global climate forecast (C3S) to ensure our implementation of seasonal seed scenario planning can be easily applied in any regional context. There are, however, also continental, regional, or national climate forecasts, which often provide higher resolution than C3S.
Materials and methods: We chose R and Stata programming languages for writing nordcan. R. Additionally, the internationally used CRG Tools programme by International Agency for Research on Cancer (IARC/WHO) was employed. A formal assessment of (GDPR-compliant) anonymity of all nordcan. R outputs was performed. In order to demonstrate that nordcan. R also works with non-Nordic data, we used data from the Netherlands Cancer Registry. Results: nordcan. R, publicly available on Github, takes as input cancer and general population data and produces tables of statistics. Each NORDCAN participant runs nordcan. R locally and delivers its results to IARC for publication. According to our anonymity assessment the data can be shared with international organizations, including IARC. nordcan. R incidence results on Norwegian and Dutch data are highly similar to those produced by two other independent methods.
Introduction The Nordic cancer registries have collaborated closely since the first registries were established in the 1940s and early 1950s. The collaboration was formalized in 1966 as the Association of Nordic Cancer Registries (ANCR) and has resulted in numerous projects and collaborations to describe incidence and mortality trends in the Nordic countries and to develop statistics to support cancer surveillance, decision making and research in the Nordic countries  (1) (2) (3) (4) (5) (6) (7). A crucial part of these projects is to prove the quality of the Nordic cancer registry data through quality estimates like completeness, validity, timeliness and comparability  (8, 9). NORDCAN, the Nordic cancer database and webtool for cancer incidence, mortality, prevalence and survival  (10)  was created in the mid-90's in a collaboration between the International Agency for Research on Cancer (IARC) and driving forces in some of the Nordic cancer registries.
NORDCAN on the web was established in 2002. A work group including representatives from the cancer registries in all the Nordic countries was established to maintain and develop NORDCAN. The history, organization and use of NORDCAN and the comparability of the Nordic cancer registries have been described earlier  (11) (12) (13). The European General Data Protection Regulation (GDPR) (14) in effect since 2018, strengthened standards for sharing personal data. Although the GDPR allows for sharing personal data within the European Union and EEA, the stricter statutory interpretation of GDPR in most Nordic countries creates additional barriers. This includes the definition of personal data, individual consent to research use and international collaboration. Sharing personal data with UN agencies such as the IARC (WHO), which are exempted from national legislation and thus not subject to the GDPR regulations, is not allowed.
It became apparent that the established NORDCAN procedures on data extraction, data preparation and data processing should be changed. The old and new data flow are shown in Figure  1. The counts and statistics produced by nordcan. R are sent to IARC through a file transfer and are stored in the NORDCAN database to be visually represented in the NORDCAN webtool. Incorporated in the webtool is the logic to perform calculation of rates, predictions, estimated annual percentage change and survival improvement, based on the output files from nordcan. R. All statistics are based on the counts and analyses done in nordcan. R, and rates are available using the standard populations World, Europe and Nordic. The aim of this article is to present the new GDPR compliant federated analysis programme (nordcan.
Materials and methods In 2018 we started rebuilding the data management, quality control, data flow and webtool of NORDCAN on research grants FIGURE 1 Old and new data flow for NORDCAN. from the Danish Cancer Society, the Nordic Cancer Union (NCU) and in kind-contributions from the Nordic cancer registries. There were two key issues to address. First, we had to ensure that personal data on cancer patients, in line with the national implementation of GDPR, would not be released from the cancer registries. Second, we had to make sure that released (aggregated) data to the NORDCAN database in IARC were comparable between countries and to previous releases. To keep personal data on cancer patients within each cancer registry, we developed a simple federated system; a program that could be run separately in each country, producing all counts and statistics necessary for NORDCAN.
The program was named nordcan. R. We established an IT-group with representatives from each of the Nordic cancer registries. The IT-group decided on architecture, technology, software and development guidelines to be used for the federated analysis application, developed the application itself, and tested the tool. Prior experience in designing and programming a system for data preprocessing and aggregation for the Finnish Cancer Registry was taken as the model. We decided to use R  (15)  and Stata  (16)  for the development of the new tool. In addition, we chose to continue using the IARC check and conversion program  (17)  for conversions from International Classification of Diseases for Oncology, third edition (ICD-O-3)  (18)  to International Classification of Diseases, tenth edition (ICD-10)  (19)  and for implementation of the multiple primary rules  (20).
We specified four common input datasets to be used in nordcan. R: a cancer case dataset  (21) , a cancer death dataset  (22) , population datasets (including population projections)  (23)  and life tables to be used in survival analysis  (24). Specifications on cancer entities (the cancer dictionary) used in NORDCAN already existed, but were revised to better meet the current needs of clinicians, cancer unions, patient organizations, researchers, and politicians  (25, 26). We also developed a conversion table for mortality to be used for the conversion of old ICD-codes (ICD6-9) to. Before starting the development of nordcan. R, we identified the core functionality of the program (Table  1 ). The NORDCAN secretariat also provided all participating countries with an overview of the necessary tools to be used, with links to installation files and sources, and additional information on licensing where necessary (Table  2 ).
We did a risk assessment on the aggregate data of the output files  (31). We drew knowledge from other sources on statistical disclosure control and data anonymization  (32) (33) (34)  to evaluate the risk of disclosure from the datasets created by nordcan. R and to identify possible risk-reducing measures. We established a proof-of-concept project between the NORDCAN secretariat and the Netherlands Comprehensive Cancer Organization (IKNL) to test the usability of nordcan. R outside of the Nordic countries. We used data from the Netherlands Cancer Registry (NCR) hosted by IKNL for the period 2000-2019. For the comparison of the output, we calculated all rates in Stata, using the World standard population  (35)  for age-adjusted rates.
Results The first version of nordcan. R (nordcan_9. 0_1. 0) was published to the NORDCAN-participants on November 27 th , 2020. The most recent, nordcan_9. 3_1. 3, was released May 4 th , 2023. The application has so far been used to update data in NORDCAN from 2016 to 2021. The main result of revamping the software was that all Nordic countries were able to deliver updated, anonymous, tabular data to the NORDCAN database and webtool, hosted by IARC, without being subject to legal discussions and hindrance due to differences in the interpretation of GDPR. Through nordcan. R, all data were.
Discussion As shown in the results, we achieved our main goal. We have produced the necessary data and we have been able to share the data with IARC for updating the NORDCAN database and webtool. As GCO and nordcan. R both use the IARCcrgTool for conversions and exclusions of multiple primary cancers, and both cancer registries adhere to international rules on cancer registration, we expected that the trend lines would be quite similar. It is still valuable to make this comparison to see that nordcan. R did not introduce any unforeseen errors. Comparison with official national statistics produced by the national cancer registries is of additional value to see that the numbers and rates in NORDCAN do not deviate even if the IARCcrgTool is not used. The slight differences we see might be due to changes in the original data caused by updates, differences in implementation of multiple primary rules or corrections or small differences in exclusions and inclusions of cases.
We have a common call for data to be adhered to by all participants. We aimed to make this call for data as similar as possible to calls for data issued by IARC for Cancer Incidence in Five Continents (CI5) -Volume XII (39) and by The European Network of Cancer Registries (ENCR) in collaboration with the Joint Research Centre of the European Commission (JRC) for the update of the European Cancer Information System (ECIS) (40). NORDCAN has, however, focused mainly on the traditional cancer registry-variables which are needed to calculate incidence, mortality, prevalence and survival on country and regional level, whereas the JRC/ENCR call for data also includes variables on stage, treatment and more detailed geographical area. Incorporating quality checks, conversions and inclusions/ exclusions into the data management process when running nordcan.
A big advantage of the new procedure is that the cancer registries run their own data through the entire process and see all errors and warnings. Another big advantageand a prerequisite for the development of nordcan. Ris that all personal and sensitive health data is kept within each cancer registry. The disadvantage of this data flow is that there is a possibility that the countries select and map their original data in a slightly different way to fit the call for data, and thereby introduce a few minor differences. However, taking all pros and cons into consideration, our view is that the cost of introducing a few differences has less impact than the benefit of users with firsthand knowledge of the data doing all data processing, aided by a tailor-made tool. Using a common check and conversion program like the one from IARC is also an advantage, as it ensures that all data in NORDCAN have been subject to the same conversion and check rules.
A disadvantage of the current version of the IARC check and conversion program is that it is not updated with the most recent morphology codes from the ICD-O-3 second revision. Also, although nordcan. R includes written instructions on what selections to use in the tool, there is still room for manual errors that can lead to data being less comparable. In the current version of nordcan. R, all entities or cancer groups are predefined through a table in the application. The user cannot change the groups or run different specifications without reprogramming parts of nordcan. R. This is an advantage, as it ensures comparability as to which ICD10-codes are included in the different entities, and which morphologies might be included or excluded in addition to the ICD10-codes, but makes nordcan.
R less useful if the user wishes to prepare statistics for other cancer sites or wishes to compare to international statistics with different cancer groups. When running nordcan. R, the user gets a visualization and an output showing how similar the current counts are to previous counts. Although this does not ensure comparability between countries, it ensures that data within one country are comparable over time, and that any bigger changes affecting the comparability of data can be documented and explained. Most cancer registries are "living organisms", where data are changed and updated over time, also for previously published data. It is therefore not unusual to see changes in counts from one version of NORDCAN to the next, but the reasons for changes should be clear and well documented, and any bigger changes should be accompanied by a warning on the webpage.
The usability of nordcan. R outside of NORDCAN was tested in our proof-of-concept project with Dutch cancer data. The first main hindrance was the necessity of installing three different types of software to run nordcan. R: R and the R-packages, Stata, and the IARC check and conversion tool. As nordcan. R operates on sensitive data, all tools needed to be installed in a secure environment which allowed for this kind of data processing. For Stata, there is an additional hindrance that installation and use require a license which the institution might not be willing to purchase. Not every potential user can fulfill the requirement of an on-site Stata installation. However, this only excludes the computation of survival statistics while other statistics are unaffected. Preparing a dataset according to the specifications given for nordcan.
Future perspectives The steps taken so far only an introduction future challenges and possibilities. Although nordcan. R fulfills its purpose for the current requirements of NORDCAN, there are still missing parts. For instance: we can currently show survival in Norway and Sweden compared to each other, but not survival for Norway and Sweden together. With the current problems we have in sharing data with IARC, we see the need for a better alternative. This is why we look to federated analysis as one possible solution for sharing data and knowledge across borders without the current hindrances we meet in laws and regulations. A future incarnation of NORDCAN should allow for more complex analyses than are currently possible and retain the successful federated analysis approach. Establishing common data models in each cancer registry, for instance using the OMOP Common Data Model  (42) , might make it easier to implement different federated learning infrastructures to run analysis on local data and only share the results.
Accessibility to software and specifications Documentation for nordcan. R, the application itself and the packages it consists of can be downloaded from the Cancer Registry of Norway GitHub repositories NORDCAN, basicepistats, n o r d c a n e p i s t a t s , n o r d c a n s u r v i v a l , n or d c a n c o r e an d nordcanpreprocessing  (43). The code and packages are free to use as is under the given license. The NORDCAN work group and ITgroup maintains and further develops nordcan. R for NORDCAN purposes and needs. Conversion tables, cancer dictionary and other documentation used in NORDCAN and nordcan. R which is not available through the GitHub-pages can be shared from the secretariat upon request. Questions can be directed to head of the NORDCAN secretariat, Siri Larønningen (sla@kreftregisteret.
Acknowledgments The authors would like to acknowledge  Henrik Mulvad Hansen ,  Johanne Jerven Hestad ,  Sheila Kristensen ,  Lina Steinrud Mørch ,  Tomas Tanskanen  and  Anni Virtanen  from the  NORDCAN  work group,  Gijs Geleijnse  in  IKNL ,  Freddie Bray ,  Morten Ervik ,  Frederic Lam  and  Mathieu Laversanne  in  IARC , the ANCR board, NCU and  JRC-ENCR , as well as all colleagues in the Nordic cancer registries. The process of replacing the NORDCAN database and webtool in IARC with a modernized platform was financially supported by  The Danish Cancer Society , and the rebuild of the pipeline and the development of a new tool was financially supported by the  Nordic Cancer Union (NCU)  and in kind-contributions from the  Nordic cancer  registries.
Check of input filesnordcan. R should check that the input-files adhere to the call for data Integration with the IARC check and conversion program nordcan. R should create the necessary input files for the IARC check and conversion program, and be able to read the output files from this program back into nordcan. R. Enrichment of original data nordcan. R should enrich the original data using output-files from the IARC check and conversion program and specifications from the NORDCAN group. This includes grouping data into NORDCAN entities and documenting reasons for exclusion of cases. nordcan. R should do recoding and calculations necessary for the final computation of data. Create necessary output files nordcan. R should create the following output: A zip-file with all counts and analyses necessary for the NORDCAN database -Counts for cancer cases, cancer deaths and prevalent cases by year, sex, region/country and entity (28) -Age standardized relative survival using the Pohar Perme estimator via stnet in Stata (29, 30).
To this end, benchmarks constitute the backbone of this process and fundamentally influence the way we measure progress and identify where future research efforts should be focused. These datasets, almost solely, put the emphasis on performance-driven comparison and create an impression of reliable estimates of progress at a task scale, whereas, in reality, they might not be informative about the way the models would solve human problems or help solve them. In this paper, we illustrate an example of transitioning from two biomedical IR benchmarks, i. e, NFCorpus and TREC-COVID, to a practical case of seeking information about a specific topic in scientific publications: vitamin B's impact on human health. In this context, we provide an expertannotated collection of relevance judgements on 1811 publications related to vitamin B and health.