dataesr/scientific-paragraphs-categorization huggingface dataset:
Data pooling and standardisation Overall, diagnostic definitions have changed over the years. Cut-offs for diagnostic test results depend on laboratory conditions, techniques and equipment used (e. g. the reference range for ciliary beat frequency is dependent on the temperature at which analyses are made). Definitions of many clinical manifestations also vary between countries and centres. We observed considerable heterogeneity in content and format between the delivered datasets. For this reason, and to obtain comparable data for pooled analyses, we developed a standardised dataset. We discussed the content and format of this dataset with all collaborators, who include experts on PCD diagnostics and clinical care, and with PCD patient representatives, participating at BESTCILIA meetings.
What information is collected The iPCD Cohort includes retrospectively collected patient data on the following 11 thematic categories (table  1 ): 1) general information, 2) results of diagnostic tests, 3) baseline characteristics, 4) growth and lung function, 5) clinical manifestations, 6) therapy, 7) microbiology, 8) imaging, 9) surgical interventions, 10) neonatal period, and 11) family history. Details on all variables included in the standardised dataset and information on the coding of variables are included in online supplementary table  S1.
Infrastructure for data delivery and data rights The iPCD Cohort is hosted at the Institute of Social and Preventive Medicine at the University of Bern, Switzerland. Research is performed in close collaboration with all data contributors. We have set up a safe information technology platform (Sharepoint) for uploading already available anonymised datasets. We organised telephone conferences to offer extra support during the initial steps of the data delivery, and to discuss questions and comments from the contributing partners. To further facilitate data delivery in the standardised dataset for new partners we created a web-based platform, using the software Research Electronic Data Capture (REDCap) developed at Vanderbilt University (Nashville, TN, USA), which is widely used in the academic research community  [30] , and allows data entry and extraction in various formats.
Detailed instructions explain how data can be entered. The REDCap environment is completely secure and each contributor only has access to the data of their own centre. We collaborated closely with centres that already had national registries and large retrospective datasets, to help them recode the data into the standardised format, and we performed quality controls of the delivered data (e. g. checks for coding errors and plausibility of entered values and dates). Each partner was responsible for ensuring that the delivered data were anonymous and in accordance with the national/local data protection laws. More information on data protection can be found in the online supplementary material under Data protection/ Ethics. We drafted agreements for data delivery and publication (see online supplementary material), which leave all rights with data contributors.
Data analysis For each planned analysis, all eligible data are validated, cleaned and standardised. We identify outliers and implausible values, and if necessary we contact contributors to resolve any issues encountered. Based on the research question, we use a one-or two-stage random effect individual patient data meta-analysis approach. As PCD diagnostics has evolved quickly and not all patients have diagnostic information that is up to current standards, all analyses will be stratified by level of diagnostic certainty, using the diagnostic subgroups described previously. Whenever relevant, we will perform additional sensitivity analysis including only patients with definite PCD, based on the recent guidelines of the ERS PCD Diagnostics Task Force  [11] , and will compare these results with the overall analysis results.
Funding The setting up of the iPCD Cohort (salaries, consumables and equipment) was funded by the EU FP7 project BESTCILIA (http://bestcilia. eu) and several Swiss funding bodies, including the Lung Leagues of Bern, St Gallen, Vaud, Ticino and Valais and the Milena Carvajal Pro-Kartagener Foundation. Data collection and management at each site was funded according to local arrangements. Most participating researchers and data contributors participate in the COST Action BEAT-PCD: Better Evidence to Advance Therapeutic options for PCD (BM 1407; www. beatpcd. org). Infrastructure is provided for free by the University of Bern, where the data are pooled and stored.
Current dataset The iPCD Cohort is an ongoing effort where new centres can still join and participating centres can add data for new patients. As of April 2016, data from 21 single centres or consortia from 18 countries had been contributed. The pooled dataset contained data on 3013 patients (figure  1 ). Some countries which have a national reference centre for all paediatric and adult PCD patients (Cyprus, Denmark and France) or a national PCD registry (Italy and Switzerland) contributed their national dataset. Other countries contributed data from their consortia of several centres (Israel, USA and Canada) or datasets from single-centre studies (all remaining centres). The number of patients per centre ranged from 10 to 436 (table  2 ). Two paediatric centres contributed datasets only with patients <18 years and one with adult patients (⩾20 years old).
Five paediatric centres additionally included a few young adults. The remaining 13 centres submitted datasets that included both paediatric and adult patients with an age range from 0 to 77 years. The percentage of males ranged from 38% to 68% among datasets. General information, results of diagnostic tests and baseline characteristics (Modules 1-3) are in the basic mandatory dataset, and thus available from all centres and for all 3013 patients or the majority of them (tables  1  and 3 ). In addition to these baseline data, several centres have also contributed data to the other modules (table  3 ). Data on growth are currently available for more than 1500 patients and on lung function for more than 1000 patients. Additional data, such as microbiology or perinatal history, have been delivered by several groups (table  1  and figure  1 ) and for several hundreds of patients (table  3 ).
The percentage of patients in the remaining age groups had a declining trend from 10% for patients aged 30-39 years to 4% for patients aged >60 years. source for research, especially in the field of rare diseases where even large national reference centres may follow a small number of patients. In addition to the basic characteristics and diagnostic test results, which are available from all centres and for more than 3000 patients, the iPCD Cohort includes data from over 1000 patients on neonatal symptoms, growth, lung function and clinical characteristics, and from several hundred patients on other topics such as microbiology, imaging and therapy (e. g. growth, lung function, clinical manifestations) from a number of centres; these are higher numbers of patients than were ever previously included in publications on these topics.
What is important is that most centres have included almost complete datasets in the different thematic modules, in cases where they have contributed data to them. Another strength is the multinational nature of the iPCD Cohort. It offers the possibility, unique for rare diseases, to assess differences in PCD characteristics between countries and ethnic groups, and variations in healthcare (diagnostics and treatment). The iPCD Cohort includes patients of all age groups and although the paediatric population is larger, this offers the opportunity to study the course of the disease through life. Preliminary analyses on growth, nutritional status and lung function from the iPCD Cohort have been presented at international conferences, illustrating the quality of the dataset and the feasibility of future studies using the iPCD Cohort.
The user-friendly and safe environment of REDCap for the standardised entry of the data is an additional strength. Data contributors can easily type in their data following simple instructions and access it any time with password-protected accounts (box 1). In addition, they can easily export their dataset for their own analyses. Some limitations are inherent to the retrospective nature of the iPCD Cohort. For instance, there remains some heterogeneity in definitions among centres. This is not an issue for standardised measurements, such as height, weight, spirometry and microbiology/radiology test results, but is more relevant for clinical signs. Reference ranges of diagnostic test results vary, and depend greatly on the expertise, conditions, equipment and protocol used at each centre. For standardisation with comparable values, we used categorical coding for numerical values (e.
g. frequency of ciliary beating has been coded as normal/decreased/increased). All the items collected in the cohort and their coding were extensively discussed and agreed among collaborators during the development period, taking into account existing national registries and the prospective international PCD registry. This ensures that all important data resources could be pooled and analysed together in future collaborative studies. Another issue is the heterogeneity of the PCD patient population concerning the diagnostic evaluation; diagnostic results can be normal or very subtle in some patients who almost certainly have PCD. Therefore, we have grouped the patients into three subgroups based on the recent diagnostics guidelines from the ERS Task Force  [11]  and are planning to take the level of diagnostic certainty into account in all analyses.
Participating centres will be encouraged to follow these guidelines for new patients and to re-evaluate the diagnosis of patients in cases where not all testing has been concluded. Although the iPCD Cohort is an important data source for statistically meaningful research, it is not truly representative of the PCD patient population. Countries that have not developed diagnostic facilities yet are under-represented. PCD is underdiagnosed in adults, and most centres follow mainly children and younger adults. Thus, adult patients, especially those of older age, are under-represented in the Cohort. Most published studies on PCD include a small number of patients. The largest published study on PCD was in the framework of the previous ERS PCD Task Force, which included 1192 patients from 26 countries  [7].
In that study the researchers collected basic data from questionnaires addressed to the clinical centres of participating countries. They found a slightly higher proportion of male patients and 85% of the patients were aged <20 years. Recently, data on 201 patients from the international PCD registry have been described  [26]. The authors observed 55% males and almost 50% patients aged <18 years. 22% of the included patients have only a clinical diagnosis compared with 35% in the iPCD Cohort. As these two datasets were both built in the framework of BESTCILIA there is an overlap in the patients they include. The international PCD registry aims to collect prospective data of PCD patients in a standardised way to use for future studies, while the iPCD Cohort combines available retrospective datasets of PCD patients in pooled analyses to answer important questions on PCD in a large study population.
Although well-designed prospective studies have obvious strengths, it is of great importance to use all available data resources for research in rare diseases such as PCD, where there is still little evidence and management of patients is primarily based on experience derived from other diseases, such as cystic fibrosis. The iPCD Cohort is a valuable resource for epidemiological studies in PCD and it can be further enriched and used in the framework of the BEAT-PCD EU COST Action. BEAT-PCD aims to set up a global, Europe-led multidisciplinary network of PCD clinicians and researchers, and the aims of the epidemiological work group include using this dataset for projects on PCD epidemiology. More centres have expressed an interest in contributing data to the cohort. Some groups had originally contributed a basic cross-sectional dataset and are now adding retrospectively collected repeated measurements (longitudinal data) or they are adding new patients to their datasets.
Ongoing studies analyse growth, lung function, diagnostic tests, clinical manifestations, neonatal symptoms and the role of lobectomy in PCD, using the iPCD Cohort dataset. The iPCD Cohort also contains data on treatments, imaging and microbiology, which can be used and to identify patients for nested studies on PCD. This dataset is now available to be further exploited and offers a unique opportunity to study PCD in a large international patient-based cohort with sufficient statistical power. Results will help to improve survival, care and quality of life of PCD patients.
Acknowledgements We thank all the patients with PCD in the cohort and their families, and especially the PCD patient organisations for their close collaboration. We also thank all the researchers in the participating centres who were involved in data collection and data entry, and worked closely with us through the whole process of participating to the iPCD Cohort. We acknowledge  Jingying Wang  ( Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland ) for her help and technical support in building the REDCap dataset.
Discussion The iPCD Cohort is the largest PCD dataset available to date. It includes data from 3013 PCD patients from 18 countries; more than half of them have a definite PCD diagnosis using current standards. The main strength of the iPCD Cohort is the large number of patients it includes. This makes it a valuable  BOX 1 Contributing to and accessing data in the international primary ciliary dyskinesia cohort (iPCD Cohort) How to contribute data Centres that wish to participate to the project and contribute data can contact the iPCD Cohort to sign a data delivery agreement. They then will receive a password to access the online software REDCap and they will be able to enter their data directly. They can also upload follow-up data or add additional patients at a later time point. In case of large standardised datasets, it is possible to upload them directly.
How to access data Centres that have entered their data using REDCap will keep constant access to their datasets and can export them directly in various formats for local analyses. We have developed detailed extraction instructions to simplify the procedure. Researchers wanting to use the iPCD Cohort dataset can propose a topic and a concept sheet describing the planned analyses and publication. All concept sheets have to be approved by all centres contributing data to the proposed analysis under question. After the participating centres agree to contribute their data and sign a publication agreement, we will prepare a partial dataset for the proposed analysis and will work closely with the lead researchers offering methodological input and support. In case additional data is collected to complete the partial dataset for a specific project, this will be added to the iPCD Cohort to enrich it after each project.
Simulated datasets consisted of phenotypes for 13,000 animals, including 1540 animals genotyped for 45,000 SNP. Scenarios with 5, 100, and 500 simulated quantitative trait loci (QTL) were considered. The 5 new procedures for SNP weighting were: (1) u 2 plus a constant equal to the weight of the top SNP; (2) from a heavy-tailed distribution (similar to BayesA); (3) for every 20 SNP in a window along the whole genome, the largest effect ( u 2 ) among them; (4) the mean effect of every 20 SNP; and (5) the summation of every 20 SNP. Those methods were compared to the default WssGBLUP, GBLUP, BayesB, and BayesC. WssGBLUP methods were evaluated over 10 iterations. The accuracy of predicting GEBV was the correlation between true and estimated genomic breeding values for 300 genotyped animals from the last generation.
5 × 10 -5 was assumed for markers and QTL per generation per locus. The simulated population originated from a historical population with 1000 generations of random mating to create linkage disequilibrium (LD) and establish mutation-drift balance. After that, a recent population was established from 200 males and 2600 females out of the historical population. The simulation was carried out for 205 generations. In every generation, the above number of males and females were selected to mate and produce 1 offspring, forming a population with an effective size (Ne) of 743. For all the analyses, generations 200-204 were treated as a training population and generation 205 as a validation population, with 1240 and 300 genotyped animals respectively. The complete dataset contained 18,400 individuals in the pedigree, of which 13,000 were phenotyped and 1540 were genotyped.
Models and Computation Quality control of genomic data retained SNP with call rates >0. 9, minor allele frequencies >0. 05, and departures from Hardy-Weinberg equilibrium (difference between expected and observed frequency of heterozygous) <0. 15. An average of 36,000 SNP remained after quality control, and the average LD (r2 = correlation between loci pair) in the last generation was about 0. 29. For WssGBLUP, the model included population mean, animal effect, and random residual error. All phenotypes (except for validation animals), pedigree, and genotypes were accounted for. GEBV was obtained by using the software BLUPF90  (Misztal et al. , 2002)  with the simulated variance components; SNP effect was obtained by postGSf90  (Aguilar et al. , 2010; Wang et al. , 2012). A total of 10 iterations of BLUPF90 combined with postGSf90 were used for the default WssGBLUP and WssGBLUP with new weighting methods.
Each run of postGSf90 updated weights for SNP, whereas each run of BLUPF90 used the updated weights to constructed G matrices and, consequently, to improve GEBV estimates. For GBLUP, the model was exactly as the one used in WssGBLUP, however, only genotypes and phenotypes for genotyped animals (except validation animals) were used together with the simulated variance components. The software BLUPF90  (Misztal et al. , 2002)  was used assuming the animals were unrelated through the pedigree. The model for BayesB and BayesC had similar effects to the model used for WssGBLUP, but the animal effect was replaced by SNP effects. The phenotypic information was estimated breeding value (EBV) de-regressed (EBV DP ) following the approach described in  Garrick et al. (2009) , assuming that 0. 05 of the genetic variation was not explained by the markers.
The marker effects were assumed to follow a normal distribution with u i ∼ N(0, σ 2 u i ), and σ 2 u i the variance of the i th SNP. The proportion of SNP having no effect was set to 50%, 90% or 99%. Priors for σ 2 u i were calculated as σ 2 a 2 p i (1-p i ) , where σ 2 a is the total simulated genetic variance. Degrees of freedom for the SNP and residual variances were set to 4 and 10, respectively. The software GenSel  (Fernando and Garrick, 2009)  was used to calculate SNP effects under the BayesB and BayesC approaches. A Monte Carlo Markov Chain (MCMC) was run for 41,000-50,000 and 1000-10,000 iterations were discarded as burn-in. For BayesB, 10 Metropolis-Hasting iterations were run per MCMC. Estimates of SNP effects were based on the posterior means of the MCMC. For WssGBLUP, the output for genotyped animals in the validation population was GEBV that contained DGV, PA, PP; whereas for GBLUP, GEBV contained only DGV.
Another practical aspect of the g-functions is that these functions need to be computed numerically, which is a time-consuming and computationally-intensive task. Hence, these functions are precomputed for various borehole geometries and configurations and then stored as databases in building energy analysis software. The SBM has been implemented in several stand-alone programs and simulation software including Earth Energy Designer (EED) and the Ground Loop Heat Exchanger Program (GLHEPro), among others. Both design tools determine the long-term response of the BHE to the monthly heating and cooling loads using the above-discussed g-function approach. Short-term response due to the peak loads is calculated differently in each program however. Building energy simulation software with the SBM implemented includes HVACSIM+, eQuest, EnergyPlus, TRNSYS and Polysun.
The first and second solutions are determined numerically using the finite-difference method, while the third solution is determined analytically. The DST model was originally distributed as a FORTRAN program, which is still available. Later, it was implemented in TRNSYS  [41]  and is now distributed commercially as Type 557  [42]. A stand-alone TRNSED application of the DST model, called PILESIM2 also exists and is available commercially (see also Section 2. 4. 3) [43]. Despite its complex mathematical formulation, software A significant limitation of the model lies in its inability to model irregular borehole-field configurations and non-uniform borehole lengths.
Short-Time Analysis Temperature response functions (g-functions) have been extended to short time-scales  [44]. As with the SBM's g-function approach, the temperature response functions of  [44]  also needed to be pre-computed for individual cases and then stored in design and simulation software as databases. Using a radial numerical model  [45]  also developed temperature response functions for short timescales. The model accounts for thermal properties of circulating fluid, pipe, grout, and the surrounding ground and is a major improvement on existing analytical models. A purely analytical method to determine borehole fluid temperature for time scales from minutes to decades was obtained by combining the short-and long-time temperature response functions  [45]. The method uses superposition of heat pulses in a similar manner to the SBM; however, it removes the important limitation of having to use a constant or steady state resistance.
The software PILESIM and implementation of the DST model includes the effects of groundwater by making two simple two-dimensional approximations. The first approximation is to the global problem of heat transfer into and out of the heat store, where the maximum convective energy is then limited to that which would be released by changing the temperature of the heat store to that of the undisturbed groundwater temperature. PILESIM then also implements increased heat transfer due to convection at the local level surrounding each duct in the store by using the steady state heat transfer for a cylinder at given temperature in a saturated porous media  [51]. It must be stressed however that although both these groundwater approximations are included within the software,  [42]  indicates that the accuracy of the underlying assumptions has not been checked.
Analysis methods The only commercially available tool for pile foundation thermal analysis is the software PILESIM  [46]. This tool is based on the DST Model described in Section 2. 3. 5, and assumes a large number of identical piles installed in a regular array to form a cylindrical store. It is unclear however what errors result from smaller or less regular pile group arrangements that are more representative of typical foundation layouts. Other analysis methods are mostly based on the g-function approach. [72]  propose a solid cylinder model which differs from the hollow cylinder model by assuming heat flow both into the pile and into the ground from the circumference of a cylinder. Numerically derived pile g-functions have been developed that provide upper and lower bound solutions for typical PHE geometries and fall between the line-source and the solid cylinder model in the short term, Figure 5  [62].