dataesr/scientific-paragraphs-categorization huggingface dataset:
Sea ice Fig. 6a  displays a map of sea ice concentration from AMSR-E/2 data in the vicinity of the Larsen B embayment on 19 January 2022. Fig. 6b  shows a time series of sea ice extent (concentration multiplied by area of pixel) for the date of 19 January for each year from 2010 to 2022 (except 2011/2012, when AMSR-E/2 sensor data are not available) in a selected region (gray box in Fig. 6a ). The selected region represents a potential ocean swell corridor leading to the Larsen B embayment from 2010 to 2022 (see Section 5. 2). For the 8-year period (2013 to 2020 inclusive) the sea ice extent in this region of the northwest Weddell Sea was over 125,000 km 2 (>50% of the box area). In 2011, sea ice extent was just 100,000 km 2 on 19 January; however, we note that the fast ice formed later in this year (March).
Elevation changes We used ICESat-2 altimetry and WV-1, -2, and -3 stereo-image DEMs to assess elevation changes of the Larsen B embayment glaciers from 2017 to present. For each glacier, we analyzed three reference points along the near-centerline to these changes. Lower Crane Glacier (red box, Fig. 10 ) may have thinned by up to 16 m immediately after the fast ice break-out, however the trend is incomplete due to the glacier's retreat and the analysis point calving off. The middle and upper section of Crane (orange and blue box, Fig. 10 ) shows a thickening from 2017 to 2022, consistent with Needell and Holschuh's (2023) findings. Thinning may have been initiated in those regions, however the data are inconclusive, as the thinning is only 1 to 3 m as of February 2023, which is within the natural variability of the glacier surface and measurement error.
, 2022; Wille et al. , 2021; Wille et al. , 2022). Strong westerly winds increase sea ice drift eastward and northward, exposing the AP's eastern coast and ice fronts to open ocean. This may have caused the low sea ice cover in the Weddell Sea in summer 2021/2022 and low sea ice concentration in the corridor region in January 2022  (Turner et al. , 2022). We found that the climate of the Larsen B region was anomalously warm from November 2021 to January 2022. However, despite the climate being warmer, the number of melt days over the fast ice derived from the passive microwave data in this 2021/2022 season was not a record, and cumulative melt days in 2018/2019 and 2019/2022 significantly exceeded the total in 2021/2022 (Fig. 5 ). In 2021/2022, days with melt over 100% of the study area (i.
Acknowledgements We would like to thank  Chris Shuman  and  Mark Fahnestock  for their help in monitoring the break-out and suggesting processing tools. The  British Antarctic Survey  pilots who captured images of the initial fast ice break-up on 31 January 2022. The  NSIDC  for data availability and technical support. TAS and NEO received support from  NASA  award no. 80NSSC22K0386  and  USGS  award no. 140G0118. GPS data was collected using the LARISSA award  NSF   OPP 0732602  and  NSF   OPP 0732921. AFB received support from the  U. S. National Science Foundation (NSF)  under award no. 1841607. (Howat et al. , 2022, https://www. pgc. umn. edu/data/rema/). The Worldview DEMs are available from  Polar Geospatial Center  upon request.
Data Availability Sea ice extent data is available on the University of Bremen sea ice webpage (https://seaice. uni-bremen. de/sea-iceconcentration/amsre-amsr2). Operation IceBridge data is available at NSIDC (https://nsidc. org/data/icebridge) as well as the ICESat-2 data (https://nsidc. org/data/atl06/versions/6). MODIS imagery can be viewed and downloaded on the Worldview interface (https://worldview. earthdata. nasa. gov). ERA-5 data are available at the Copernicus data store (https://cds. climate. copernicus. eu/cdsapp#!/home). WaveWatch III is available on CSIRO (https://data. csiro. au/collection/csiro:39819). The AMSR-E/2 data are available to download here: https://perscido. univgrenoble-alpes. fr/datasets/DS391. The Reference Elevation Model of Antarctica is available via the Polar Geospatial Center https://doi.
The Investigation of Serial Studies to Predict Your Therapeutic Response through Imaging and Molecular Analysis 2 (I-SPY2-NCT01042379) is a platform trial investigating high-risk clinical stage II or III breast cancer, and it is a well-known representative of the potential of platform design in research. A mainstay of I-SPY2 is offering new tailored and targeted rapid treatments (or combination of treatments), thus enabling a prompt assessment of the efficacy for specific subtypes of trial participants based on predictors established in I-SPY1  [6, 7, 8]. Furthermore, the platform trial design has been recently adopted to evaluate potential COVID-19 therapies. A valuable review of the advantages of platform trials in COVID-19 research has been delivered by Stallard et al. [9]. The authors described the advantages of having an adaptive design available when information such as the progress of a disease and the features of patients is uncertain.
Work package 3: Specific aims Within the EU-PEARL project, WP3 is designing an innovative method to inform the protocol designers for the selection of clinical sites through evidence of existing patients matching protocol eligibility criteria in high-quality EHR systems. WP3 is developing a generic process for interrogating EHRs to accelerate the processes of site selection and potential participant recruitment, in compliance with the General Data Protection Regulation (GDPR) requirements for the protection of personal data (679/2016)  [18]. The outcomes would allow querying of EHR data for healthcare organisations not connected to a central federated data platform, mainly using the Observational Medical Outcomes Partnership Common Data model  [19]  (OMOP CDM) to translate the master protocols eligibility criteria into computable elements.
OMOP CDM is a central component of The Observational Health Data Sciences and Informatics (OHDSI) program, a multi-stakeholder, interdisciplinary, and collaborative ecosystem aimed to bring out the value of health data through large-scale analytics  [20]. In OHDSI, all solutions are open-source. This would make it easy to be run against hospital EHRs already mapped to the OMOP data model, as well as against hospital EHRs not yet mapped to the OMOP data model, facilitating the conversion of the queries to the hospital-specific EHR format. To be able to do so, as the hospitals are the main providers of data, it is essential to identify sites capable of using their EHRs for research, referred to as EHRs-enabled sites, from those who cannot. WP3 has developed an EHR system and data readiness requirements survey (eSurvey) as a continuation of prior work in the Electronic Health Records to Electronic Data Capture systems (EHR2EDC) project  [21].
One aspect of the EU-PEARL work plan is to examine the eligibility criteria being proposed for the four disease area master protocols in order to undertake some feasibility queries across the eight university hospitals that are part of the project consortium, and which are members of the European University Hospital Alliance (EUHA) (https://www. euh alliance. eu/). These pilot eligibility criteria will be modelled using the OHDSI tools (with support from the IMI EHDEN project  [36] ) and executed on exported data from each of the hospitals represented according to the OMOP CDM  [19, 37, 38, 39]. The OMOP CDM has been developed to do observational healthcare research at a large scale, from drug utilisation to comparative effectiveness studies. In Europe, the.
What this paper adds The EU Patient-cEntric clinicAl tRial pLatforms (EU-PEARL) project has developed a readiness survey for the selection of clinical sites and trial feasibility criteria as interoperable queries to obtain the number of potential participants at those sites. adoption of the OMOP CDM has been driven by the IMI EHDEN project consortium with 166 data partners from 27 countries using the OMOP CDM. To support research using the OMOP CDM, there is a collection of open-source software maintained by the OHDSI community  [20]. The use of hospital EHRs described here focuses on the design of a trial, in particular a platform trial. However, there has been additional work to demonstrate that the data within the EHR of a patient who has given consent to participate in a trial and given consent for the reuse of their data can be soundly transferred from EHR to electronic data capture (EDC) systems used in clinical trials.
Moreover, ISO standards have together informed the readiness survey (ISO 13606:2019 Electronic Health Record communication; ISO 18308: 2017 Requirements for an Electronic Health Record Architecture; ISO 13940: 2015 System of concepts to support continuity of care; ISO 21090: 2011 Harmonised data types for information interchange; ISO 27789: 2021 Audit trials for electronic health records). Two independent researchers screened all the criteria for duplicity from the referenced articles. This resulted in a set of 354 unique criteria. The criteria, the corresponding questions, and the defining of the subdomains were achieved in cocreation with all relevant stakeholders (public and private). In total 27 individuals (EHR, Clinical Trial, ICT, technical experts, data experts from hospitals, and sponsors) selected the criteria and the corresponding questions.
Challenge #2: Trial eligibility criteria It is essential to compare, and adjust if necessary, information contained in these records and the eligibility criteria, a procedure that may have severe limitations as not all eligibility criteria could always be reliably captured in the EHRs causing inconsistencies between a given trial and potentially eligible trial participants  [45]. The EHR-driven phenotyping is based on the selection of trial participants through specific criteria  [46] , and the presence of missing data might further increase the risk of bias. Even within one site, there is huge heterogeneity in completeness, depending on patient handling; inpatients will have a lot more information than outpatients will, and the selection of participants through querying healthcare datasets can be associated with an over-representation of "less-healthy individuals"  [47].
This replacement would help avoid possible human errors, save time, and significantly improve overall efficiency, This is a complex challenge, primarily because hospitals have implemented their EHRs in customised ways and using different EHR solutions (i. e. , developers and vendors). In this situation, the best solution is to have the warehouse data of the eligible sites (selected through the use of the EHR readiness survey) translated to some CDM. In EU-PEARL, the OMOP CDM has been selected to facilitate interoperability between sites. OMOP is created for observational studies; thus, an innovative aspect here is to leverage this data model in the case of interventional studies. This data standard can also be used in the EU-PEARL context of site selection and clinical trial feasibility. As described in the introduction, we focus on the calculation of the number of potential patient (NPP) counts matching the lists of I/E criteria for the four diseases under study, defined by the clinicians and corresponding to their IRP needs.
Even in the case of all sites using OMOP CDM, we have made several local adjustments due to the complexity and heterogeneity of the EHR structures on each clinical site. This challenging situation prompted us to adopt an incremental strategy to assess the feasibility of any of the disease-specific protocols to build, export, execute, and validate the queries across multiple hospitals (see Fig. 1 ). We used the ATLAS cohort definition module from OHDSI  [21]  to create the trial feasibility eligibility I/E criteria as executable queries which can be shared between sites. ATLAS facilitated the creation of cohorts by integrating vocabulary search terms in structured data such as medical conditions or exposure to medicines to define cohorts. Sites using the OMOP CDM can execute these queries directly on their data to retrieve patient counts.
In addition to ATLAS, we evaluated Cohort Diagnostics  [50] , a software package written in the R platform  [51]  for combining results for all cohort definitions from different sites. Cohort Diagnostics is fully interoperable with ATLAS cohort definitions and data in the OMOP CDM. When lacking OMOP databases at any site, alternative queries may be constructed in Structured Query Language (SQL) or other computational languages compatible with local data warehouses, and aligned, to the extent possible, with those queries performed in the ATLAS tool. However, even OMOP-structured databases might require a modification of the query scripts. The current heterogeneity of the databases at the different sites usually forces the development of specific additional "ad-hoc" tools or scripts to align the querying strategies.
Informaticians from different areas (data architects, software developers, bioinformaticians) must have a constant dialogue to supervise these tasks. To reach this objective we introduced a previous step for all queries, constructing "pre-code" texts in collaboration with medical experts in plain language and understandable to the informatics actors that have direct contact with their own EHR system. We followed a similar sequence of statements and prioritisations of rules and lapse times, and we called them the "harmonised pre-code queries" (to be published), which may serve as guidance for future users. Another important decision we made in the construction of the queries is that, in some cases, there is not sufficient or complete information in the warehouse databases to extract the NPP.
g. , SQL and JavaScript Object Notation (JSON)) which are interoperable with other sites using the OMOP CDM and ATLAS tools. Future publications with details on the creation and execution of queries, and results, are currently under production. All the cohort definitions created in the EU-PEARL project will be shared in a publicly accessible repository, GitHub. GitHub (https://gith ub. com) is a web service where version-controlled open-source software repositories can be hosted for collaboration. It can be freely downloaded for future users, who may use and change them to particular needs on their own sites. As discussed, eligibility criteria are often documented in unstructured text, making it challenging for an automated cohort definition process. Translation of trial eligibility criteria into EHR phenotypes is a complex and time-consuming process  [45].
Lastly, this query may be exported to ATLAS which has a user-friendly web-based interface where manual review and editing can be performed prior to executing the query to obtain patient feasibility counts. Although the Criteria2Query approach is interesting, there are some limitations, including information extraction errors which would need a larger training data set for further improvements. The WP3/EU-PEARL work will guide future studies in leveraging EHRs in clinical research. However, one of the limitations of the eSurvey is the difficulty in answering all the questions, as highlighted by several hospitals during a closing teleconference. This issue should be further investigated. In addition, there was no possibility to assess the sensitivity of the eSurvey and thus determine whether it can yield the desired patient cohort.
An additional value of this manuscript is the elucidation of the methods and description of the newly developed tools. As future Close dialogue with informaticians or data scientist were necessary for Main or Alternative procedures. Main: in OHDSI-OMOP schema, we used the ATLAS Demo Site to derive JSON source code. Then, this JSON query was sent by email and was executed at VHIR and EMC respective OMOP databases, finally, each NPP was retrieved. An optimal reuse scheme is to have JSON queries at a repository open to the research community according to FAIR principles. Alternative: in a non-OMOP database, we had several rounds of discussions with local informaticians, and an "informatics-friendly" list of requirements was derived. Then, they executed their SQL code, highly dependent on the local EHR system.
On the other hand, some criteria need to be added to generate results that are reflective of the number of potential candidates in a given time frame (i. e. , how many patients currently treated with this condition meet these additional criteria), and thus, to estimate the most accurate number of eligible trial participants. Finally, some key criteria might not be available for querying the hospital EHRs. Then the protocol criteria should be used to define some surrogate "proxy" criteria based on data more likely to be available in the EHRs. Ideally, the query should include some specific conditions allowing the count of patients actively seen at the institution during a specific period of time. EU-PEARL developed queries using I/E (or approximated I/E) criteria to extract the count number of potential participants in the complex trials.
Acknowledgment "EU-PEARL has received funding from the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement No  853966-2. This Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation programme  and  EFPIA  and  CHILDREN'S TUMOR FOUNDATION ,  GLOBAL ALLIANCE FOR TB DRUG DEVELOP-MENT NON PROFIT ORGANISATION ,  SPRINGWORKS THERAPEUTICS INC. ".
2015). Along with the Anatolian Neolithic ancestry, those populations showcase sporadic evidence of steppe-related ancestry (specimens in Varna I and Smyadovo cemeteries in Bulgaria), but also a consistent huntergatherers related ancestry (some resilient native Mesolithic groups from the target area), which indicates a complex population structure and admixture, with several genetic components  (Mathieson et al. 2018). The last decade has seen major advances in developing theoretical, analytical, and methodological instruments, concerning the understanding of demographic change out of large datasets of archaeological radiocarbon ( 14 C) dates, in different parts of the world and encompassing large temporal spans of the Late Pleistocene and Holocene  (Shennan et al. 2013; Downey et al. 2014; Timpson et al.
DATA In the current study 440 radiocarbon dates from both Romania and Bulgaria ascribed to the KGK-VI were used (Figure  1 ). The data were acquired from publications, gray literature, unpublished sources, and from on-line databases  (Reingruber and Thissen 2017; Weninger et al. 2020 ) (e. g. , http://www. 14sea. org/index. html and https://www. academia. edu/40774947/ CalPal_Holocene_Palaeolithic_14C_Database). The available radiocarbon dates from the above-mentioned sources were compiled in a database (n = 257 from Romania and n = 183 from Bulgaria), grouped into 135 bins for analysis (see explanation below of binning), recovered from 59 sites (n = 32 in Romania and n = 27 in Bulgaria). The database includes contextual information such as site name, site id, site recovery context, "culture" and phase (where available), region, country, laboratory number, the uncalibrated date and uncertainty, and geographical coordinates (longitude and latitude) (Supplementary Table  1 ).
The dates selected to use in the current investigation were obtained from samples from various materials including, wood, charcoal, seeds, and bones (herbivores and humans). The dates based on shell, fish and carnivore samples have not been included to avoid potential reservoir effects issues. We also applied a cleaning protocol and excluded all dates with large uncertainties in order to remove any potential spurious dates. However, most of our dates have very low uncertainties, with a mean of 43 and a median of 37 years. Recent studies, dedicated to similar research agenda, have also shown that an overly strict data cleaning and the exclusive use of dates with very low uncertainties might potentially be just as damaging for this kind of analysis just as an uncritical data collection  (Timpson et al.
METHODS Our analysis was carried out with the R environment for statistical analysis  (R Core Team 2020) , and the rcarbon R-package for date calibration and SPD modeling  (Bevan et al. 2020; Crema and Bevan 2021) , using the Northern Hemisphere calibration curve (IntCal20)  (Reimer et al. 2020) , along with gstat  (Pebesma and Graeler 2021) , sp  (Pebesma et al. 2022) , and other R packages mentioned in the rmarkdown script that accompanies the study. Dataset, supplemental figures and R Markdown scripts needed for reproducing the results of our analysis are available at: https://zenodo. org/record/7587242#. Y9hI9S8Ro4c. The methods that we used in our analysis are based on previously developed quantitative analysis of SPDs by  Shennan et al. (2013) , further refined in several other recent studies  (Downey et al.
Dates Binning, Calibration, and SPDs Production To mitigate potential issues due to the differences in intensity of sampling, radiocarbon dates are combined in 100-year bins within each archaeological context (e. g. , horizontal and vertical provenience units) so that the intensively sampled sites/areas are not overrepresented and cause artificial spikes in the observed SPDs. When multiple dates are present from a single site, they are aggregated within each archaeological context and when their distance in 14 C years is less than 100 years. The procedure applies a hierarchical cluster analysis using the complete linkage method and a cut-off value of 100 years to separate the observations. Although our selection of 100 years for binning is arbitrary, we performed a bin sensitivity analysis (Supplementary Figure  1 ), which shows this choice has no negative impact on the accuracy of results (all bin sizes fit within the 95% confidence simulated envelope-gray area) and is also well above the median error of 37 years in the dataset (our protocol follows those already applied in the literature; for more details  (Bevan et al.
2017; Riris 2018; Crema and Bevan 2021). Dates were calibrated using the Northern Hemisphere Radiocarbon Age Calibration Curve (IntCal20)  (Reimer et al. 2020 ) and the rcarbon package  (Bevan et al. 2020). Multiple dates within a bin are calibrated and summed "inside" the bin and subsequently divided by the number of dates so that each archaeological context contributes a single date distribution to the overall SPD. The probability distributions of the calibrated dates were summed over the entire KGK-VI period to produce empirically based SPDs using the entire data set, as well as for subsets for the two regions north and south of the Danube. Following detailed discussions in recent works  (Weninger et al. 2015 ; see also details in  Bevan et al. 2017) , we have not normalized the post-calibration distribution of each date (that ensures it sums to 1 under the curve) before summation of multiple dates.
2017). We also deployed a more recent alternative to SPDs, Composite Kernel Density Estimates (CKDEs)  (Brown 2017; McLaughlin 2019) , which has the advantage of minimizing calibration artificial spikes, as well as, providing estimates of sampling and calibration-derived uncertainty over time (Figure  5 ). Based on the qualitative inspection of the SPDs, the CKDE curve and formal AIC test, we decided to also fit a suite of four composite models (see Rmarkdown document in the Zenodo repository) to the summed calibrated probability distribution for KGK-VI, representing potential demographic models (see  Goldberg et al. 2016 ; Arroyo-Kalin and Riris 2021; de Souza and Riris 2021). Assessing for the goodness of fit of these models was achieved following the same procedure as outlined above. Permutation Tests-Regional Comparison We are obviously interested in empirically testing the variation between the northern and southern regions of the KGK-VI (Danube River is used as a geographical divide).