br Acknowledgement br Data Descriptive values of physical

Acknowledgement
Data Descriptive values of physical capacity-related variables, NT-ProBNP, cardiopulmonary exercise capacity, PA levels and HRQoL of PAH patients involved in the RCT [2] (ClinicalTrials.gov ID: NCT02288442) conducted from January 2015 to June 2016 at the Hospital 12 de Octubre (Madrid, Spain) following the Consolidated Standards of Reporting Trials (CONSORT) guidelines [3] are shown in Table 1.
Experimental design, materials and methods 20 PH patients were involved in the intervention group of the RCT and 20 PH patients in the control group. The RCT intervention lasted 8 weeks and included 3 main components: aerobic and muscle resistance, and specific inspiratory muscle training. All sessions were supervised by experienced purchase pitavastatin instructors. Data outcome (by order of aquisition) were: i) blood sampling for NT-proBNP determination, PImax, 6MWD, muscle power, 5-STS, and distribution of HRQoL questionnaire (1st day); ii) cardiopulmonary exercise testing (after 2–3 days to allow recovery from power testing); and iii) seven days of accelerometry recording for objective PA determination. Please see Gonzalez-Saiz et al. (2017) [1] for detailed information.
Acknowledgments We acknowledge POWERbreathe International Ltd., through BIOCORP EUROPA S.L./ POWERbreathe Spain, for the kind donation of equipment for inspiratory muscle training. This study was funded by a grant from Cátedra Real Madrid-Universidad Europea (Grant number P2015/05RM) and L.G.S was supported by a research training scholarship from GSK to conduct the study. The research by AL is financed by Fondo de Investigaciones Sanitatrias and Fondos Feder (grant # PI15/00558).
Data The data presented include the clinical characteristics of the paradoxical embolized patients, the administrated treatment and the following up results (Table 1).
Experimental design, materials and methods Twelve patients diagnosed of paradoxical embolism in Fuwai Hospital from January 1994 to October 2015 were included. All clinical data related were collected from medical records, such as (1) demographic data; (2) case history and risk factors for thromboembolism; (3) clinical, laboratory and imaging findings of deep venous thrombosis, pulmonary embolism and systemic arterial embolic events; (4) imaging findings of an abnormal communication that allowed right to left shunt; (5) treatment and outcome; (6) follow-up data. Follow-up data were obtained from hospital records or by telephone interview with the patients or their family members. Follow-up ended on November 1st, 2015 [1].
Conflict of interest
Specifications Table
Value of the data
Data When a patient is diagnosed with cancer and selected for treatment with radiotherapy, a treatment plan has to be generated. This is based on a 3D Computer Tomography (CT) scan of the patient, containing delineations of the organs and the tumour. The treatment plan describes the personalised settings of the applied treatment unit, and contains a predicted patient dose distribution for these settings, projected on the CT-scan. The aim is to deliver sufficient dose to the tumour for curation, while keeping the dose to healthy organs as low as possible to minimise the probability of developing radiation-induced treatment related complications. The data provided allows to investigate two applications: (1) For a chosen problem definition, the performance and accuracy for mathematical solvers can be evaluated, irregardless of the clinical interpretation of the result (see [1]). (2) For multi-criteria optimisation and decision-making (MCDM), different clinical trade-offs can be investigated, irregardless of the performance of the mathematical solver (see [2]). More information on the technical background of radiotherapy treatment planning can be found in [3,4], and on the use of the data can be found in [1,2,5].
Experimental design, materials and methods This dataset contains data required for radiotherapy treatment plan optimisation for 120 patients which were treated previously at the Erasmus University Medical Center Rotterdam, The Netherlands. The patients belong to different groups of tumour locations, tumour types and types of treatment, and were included randomly in the original studies, see Table 1. For the Head-and-Neck patients, we included an alternative set for the same 15 patients with a more accurate dose model. This results in denser matrices, and thus a heavier problem from a numerical perspective. Because the problem complexity between the two sets is comparable, this allows evaluating the impact on the numerical performance of mathematical solvers.