dataesr/scientific-paragraphs-categorization huggingface dataset:
They were measured together in an adiabatic demagnetization refrigerator. Fig. 3  shows the resistivity of one sample against field at various fixed temperatures. At the lowest temperatures, ρ increases sharply at the first metamagnetic transition (at applied field H 1 ), and decreases sharply at the second (H 2 ). Elevated resistivity between H 1 and H 2 is also seen in CeNiGe 3  [4] , YbNiSi 3  [8] , and CeRh 2 Si 2. [6]  Data from increasing-field and decreasing-field ramps are shown together in Fig. 4 ; magnet hysteresis is excluded by measuring the field with a Hall sensor placed near the sample. Hysteresis in the metamagnetic transition fields shows that the transitions are first-order. The magnitude of the hysteresis against temperature is shown in panel (b): it decreases steadily as the temperature is increased (proving that it is not an artefact of instrument hysteresis), disappearing within experimental resolution above ∼5 K for the first and above ∼3 K for the second transition.
Acknowledgements We are grateful to  Philip Lubin  for many useful discussions. We are in debt to the engineering staff at  INPE , in particular,  L. A. Reitano ,  M. C. Almeida ,  I. Geier  and  M. Desidera. I. S. F. thanks a CAPES and a  CNPq Fellowship. T. V. acknowledges  CNPq  Grants  305219 /2004Grants 305219/ -9, 303637/2007Grants 305219/ -2, 484378/2007Grants 305219/ -4, 308113/2010Grants 305219/ -1, 506269/2010Grants 305219/ -8, 502831/2008Grants 305219/ -1, 559197/2008Grants  305219/ -0  and  308876/2014-8-8 , and  FAPESP  Grant  00/06770-2. The corresponding author and DB acknowledge support from the  Fundação para a Ciência e a Tecnologia (FCT) the Portuguese national funding agency  ( SFRH/BD/76615/2011 ) and by  FCT/MEC  through national funds and when applicable co-funded by  FEDER -PT2020  partnership agreement under the Project  UID/EEA/50008/2013.
Funding I. S. F. thanks a CAPES and a  CNPq Fellowship. T. V. acknowledges CNPq Grants 305219/2004-9, 303637/2007-2, 484378/2007-4, 308113/2010-1, 506269/2010-8,  502831/2008-1, 559197/2008-0 and308876/2014-8 , andFAPESP Grant  00/06770-2. The corresponding author and DB acknowledge support from the  Fundação para a Ciência e a Tecnologia (FCT ) the  Portuguese National Funding Agency  ( SFRH/BD/76615/2011 ) and by  FCT/MEC  through national funds and when applicable co-funded by  FEDER -PT2020  partnership agreement under the Project  UID/EEA/50008/2013. DB acknowledges support from the  ENGAGE SKA national Research Infrastructure.
Although treatment using molecular stratifications has not been attempted systematically in CC, differential mutational and gene expression patterns are associated with squamous or adenocarcinoma CC histopathological types  (Schwarz et al, 2012; Scholl et al, 2013; Jimenez-Wences et al, 2014; Ojesina et al, 2014; Rusan et al, 2015; Spaans et al, 2015). In retrospective data sets of CC and head and neck tumours combined, three subgroups of pathway alterations were revealed in human papillomavirus (HPV)associated cancer: namely cell proliferation/survival, immune response/cell death, and cellular differentiation  (Rusan et al, 2015). BIO-RAIDs, a prospective study of patients with previously non-treated CC (stages IB1-IV), was initiated in seven European countries with the aim of analysing primary tumours at the molecular level along with patient outcome to stratify tumours into classes according to the underlying pathway activation and prognosis.
Preliminary bioinformatics analysis using ACSN (Atlas of Cancer Signalling Networks), which was based on TCGA, Ojesina  (Ojesina et al, 2014)  and the first exome sequencing data from the first 48 BIO-RAIDs patients, suggested that CC can be stratified into 5 classes with distinct molecular features (unpublished data). At the time of manuscript submission, BIO-RAIDs had recruited over 400 patients. The BIO-RAIDs protocol was published previously  (Ngo et al, 2015). Here we present the challenges for molecular assessment studies that were identified during this systematic biobanking and provide recommendations for conducting similar studies in the future. The identified challenges were related to: diversity of legal and ethical standards in different countries, lack of clinical trial resources at some centres, lack of experience in biobanking in many centres, and to the complexity integrating molecular data on multiple samples (Figure  1 ).
All errors which occurred were human errors, the most serious one being a mislabelling by the carrier company, sending a batch to the wrong country. The late recognition of errors (inversion of labels for blood and serum) proved labour-intensive to correct. A central integration platform in knowledge data integration (KDI) at Institut Curie continuously checks for incoherences in the data, sending out reports to all laboratories and clinical monitoring groups. Incoherence in sample ID is checked first. The EAN-13 barcoding scheme was used for sample labelling in the BIO-RAIDS study. Although this format includes a built-in error-checking capability, it could only code for numeric values. Thus, when these barcoded sample IDs are imported into spreadsheet software such as Libre Office or Microsoft Office or into the R statistical computing environment, the 13 digit barcodes are routinely recognised as huge numeric values and are transformed into floating point numbers (for instance, 9110351350145 may be converted to 9.
Yet this needs to be cautiously evaluated in terms of costs. Clinical data collection and monitoring tasks can be simplified if we adhere to the following principles. First, the data to be collected should be limited to data relevant for evaluating the study objectives. Second, the data should be captured in real time using a validated, user friendly eCRF to facilitate flawless data collection and integration. Third, the accuracy, completeness, and coherence of the entered data should be assured through eCRF internal controls. This allows a considerable reduction in on-site monitoring time and therefore a reduction in monitoring costs. The eCRF should include an interactive biobanking section that is accessible to and completed by the clinical centres as well as by the central molecular laboratories to ensure traceability and future use of samples in subsequent ancillary studies.
WEBTRIAL was used for the BIO-RAIDs study. Many other easy-to-use eCRF backbones are available today, such as Marvin, REDcap, which is a free, webbased, user friendly electronic data capture (EDC) tool for research studies. The monitoring strategy should ideally be described in the monitoring plan, and significant steps that are meant to reduce the cost of monitoring should be considered. It is currently estimated that the use of complete source data verification will not necessarily result in a substantial improvement in study data. Rather, a riskbased approach for QC, including on-site monitoring, should focus on the study procedures and data, which are relevant for patient safety and data integrity. The following suggestions by Messenger et al (2012) might be worth exploring further: (1) issue a QC report for the data; (2) have a set of internal quality-assurance controls embedded in the eCRF; and (3) institute a yearly data audit programme that includes biobanking information that can be applied to non-interventional studies.
WES was selected for use in the BIO-RAIDs study (n ¼ 100) to prospectively evaluate very diverse alterations, ranging from point mutations to whole chromosomal rearrangements, and to detect less frequent alterations that are relevant to patient outcome. BIO-RAIDs is developing targeted exome sequencing of a panel of genes that were chosen because they harbour hotspot mutations according to both our own WES data and according to the literature. Such a targeted panel offers several advantages; in particular, it is cost-and time-effective, and it has manageable bioinformatics and computational requirements  (Roychowdhury and Chinnaiyan, 2014; Dietel et al, 2015). Sequencing instruments, library preparation kits, sequence kits, and analytical tools are in constant development and are likely to change during the course of a multi-year study.
Second, bioinformatics pipelines need to be developed to make relevant biological information from the highthroughput molecular profiles of the patient rapidly available to clinicians. Third, the architecture must ensure the reproducibility of the results. All of these bioinformatics challenges were reviewed by  Servant et al (2014). At the Institut Curie, in the context of the RAIDs project, a information system termed KDI (for 'knowledge and data integration') was set-up to ensure the sharing of information between partners, cross-software interoperability, automatic data extraction, and secure data transfer. Other similar systems are available today, such as the tranSMART platform, which is an open-source, community-driven knowledge management platform for translational medicine  (Barash et al, 2015).
Although BIO-RAIDs is purely an analytical trial, it sets the stage for future clinical trials of specifically targeted drugs or drug cocktails. Ongoing work on cell lines that compares mutational and proteomics data with pharmacological profiling should help identify common targeted drugs and drug cocktails that benefit patients. In addition to these technical and clinical research aspects, the perception of patients and their feedback on biobanking is key. A recent study shows that patients are receptive to donate tissue samples if they are educated on the importance of specimen based research  (Braun et al, 2014) , an observation that seems to be shared in the BioRaids population.
ACKNOWLEDGEMENTS We would like to thank all of the RAIDs consortium members for their concerted efforts in conducting the  BIO-RAIDs trial. A special thank you to  Dr P Ewing-Graham  ( Department of Pathology, Rotterdam ) for her expert pathological review. This project received funding from the  European Union's Seventh Program for Research, Technological Development, and Demonstration  (grant no. 304810 ). This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution-NonCommercial-Share Alike 4. 0 Unported License.
Acknowledgements Special thanks are due to  Bill Keener  for making useful suggestions that improved his manuscript, to  Elena Papale  for her critical revision of a previous draft and to  Emilio Balletto  for his encouragement and advice in improving the structure and effectiveness of our scientific writing. Thanks also go to two anonymous reviewers for their helpful comments. We are indebted to  Dr. Ahmed Ouledi , coordinator of the  EU B. I. R. D. project, who helped in many ways during this study and with  Dr. Daniele Cagnazzi  for his useful logistic aid in our research activity. The results presented here are based on the outcome of the B. I. R. D. project, contract  FED/2009/217077  financed by  ACP Science and Technology Programme, a program  of the  ACP Group of States , with the financial assistance of the  European Union.
Acknowledging the need for appropriate data to remedy this state of affairs, the Migrations between Africa and Europe (MAFE) project collected rich retrospective biographical data in several locations in Africa and Europe  (Beauchemin 2012)   1. Here we use MAFE data to examine the drivers of Senegalese migration to Europe between 1976 and 2008. This migration system is an example of migration from Sub-Saharan Africa to advanced societies. Although comparatively modest in absolute numbers, this migration stream has developed quickly since the 1980s. In this study, we pay particular attention to the links between the micro-level factors that influence migration decisions and political, social and economic processes in both Senegal and Europe. Very few empirical studies examine how migration relates to historical social and economic transformations  (Sassen 1988; Portes 1997; Castles and Miller 2008).
Data and variables We The data are time-varying by nature, since they result from individual life-histories collected in biographical questionnaires. The questionnaire was designed to collect retrospective information on a yearly basis from birth until the time of survey (  2008 ), for each sampled individual, whatever his/her country of residence at the time of the survey. Information was collected about individuals' migration and work histories, as well as their family history (children, partnerships) and social networks. This type of data collection is susceptible to recall errors and omissions. Fortunately, omissions should be minimal for salient events, such as international migration or important family events (like births or marriage); but errors are likely to be more frequent for some categories of network members or in the case of complex job histories.
Independent variables include age, gender, number of children, and partnership status (individuals married or in a consensual union versus not being in a partnership). A time-varying indicator of the educational level has been constructed, based on answers regarding the main occupation of individuals and their educational attainment. The data distinguish periods when the individual was employed, unemployed, studying, or in other type of economic inactivity. The questionnaire provides information on the individual's job during each activity period (each change in occupation or occupational category implies a separate activity period). Information on professional occupations was coded using the ISCO-08 International Labor Office classification, and was subsequently collapsed into a simplified version of the Erikson and Goldthorpe's class categories  (1993).
We include two contextual variables in our analyses, based on the data presented in Figure  1. The first is the annual growth rate of the Gross Domestic Product. This data, expressed in constant 2000 US dollars, is obtained from the World Bank database (2012). The second variable, intended to proxy labor demand, is the annual employment growth rate in France, Italy and Spain, based on the Labor Force Surveys (OECD 2010) 20. These contextual variables were available starting from 1976, resulting in a reduction of 47 individuals included in the statistical analyses. Additionally, 72 observations were dropped from the original 1670 valid interviews, because they migrated at age 18 to other destination outside the three countries studied, or because they had already made a first migration before age 18.
base the empir2006)analyses on the survey «Migrations between Africa and Europe» (MAFE-Senegal)17. This transnational dataset results from the use of identical questionnaires and similar survey methods in Europe and in Senegal: 603 Senegalese migrants were surveyed in Europe, irrespective of the Senegalese region of residence prior to migration (about 200 each in France, Italy and Spain 18 ) and 1,067 persons were interviewed in the region of Dakar (including 197 returnees and 101 migrant's partners at the time of the survey in 2008). The MAFE Senegal survey's geographical strategy is astute for surveying Senegalese migrants. On one hand, France, Spain and Italy accounted for about 45 percent of the Senegalese diaspora, reported in the 2002 Senegal Census (Agence Nationale de la Statistique et de la Démographie 2006).
Acknowledgements The work has benefitted from useful suggestions and discussions with  Mathew Creighton ,  Mao-Mei Liu , and the participants to the workshop " The Decision to Emigrate. Agent-Based Modeling of International Migration " September  25 -27 ,  Rostock. The research reported here has been founded by the  Spanish Ministry of Science and Innovation, National Program of Research and Development  ( CSO2009-12816 ).
Further culture of this placental exudate material on HEp2 cells in vitro revealed the presence of Chlamydia-like organisms with the capacity to form cellular inclusions and induce cell lysis; a growth phenotype identical to that of the W. chondrophila strain ATCC VR-1470 that was used to challenge the animals. Visualisation of these inclusions at 24 h p. c. with the anti-W. chondrophila antisera identified the organisms as W. chondrophila (Fig. 5c ). Genotypic verification by whole genome sequencing unequivocally established these isolated organisms as being W. chondrophila strain ATCC VR-1470 (Whole Genome Shotgun project has been deposited at DDBJ/ENA/ GenBank under the accession LVEB00000000. The version described in this paper is version LVEB01000000).
Acknowledgements The authors thank  Biomathematics and Statistics Scotland (BioSS)  for statistical support and  Moredun Bioservices  for their valuable technical assistance and for the general care and well-being of the animals. This work was funded by the  Scottish Government Rural and Environment Science and Analytical Services Division  (RESAS, http://www. scotland. gov. uk) and through the  Industrial partnership award  awarded by the  Biotechnology and Biological Sciences Research Council  (BBSRC, http://www. bbsrc. ac. uk/) and  Zoetis  (grant No. BB/J015083/1 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data on factors hypothesised to have a direct impact on survival (proximal factors) such as tumour biology and treatment received are predominantly extracted from hospital records (patient notes, pathology reports, etc), using a standardised study proforma. Factors hypothesised to have an indirect impact on survival (distal factors) such as socioeconomic factors that influence diagnosis delays (figure  1 ) are obtained directly from the participants during the baseline interview. The full prediagnostic experience from first symptom recognition, every contact with caregivers, through to diagnosis are also obtained at baseline interview (figure  2 ). At three-monthly intervals after the baseline interview, the study RA telephones the woman, or when appropriate, her next-of-kin, to obtain data on vital status, treatment side effects, treatment compliance and health status and possible barriers to healthcare utilisation and treatment adherence.
Each RA's mobile phone downloads a 'to do' list daily with relevant information on which patients are due follow-up. Investigators are able to remotely track the progress of the fieldwork in real-time and keep in touch with the RAs on the ground, creating a closer motivated team. ▸ Data are encrypted and securely stored with access control codes defining levels of access (eg, local investigators have access to the data from their centre; while the study coordinator has access to all the data). The above app is being used with one exception, the South African site. Their breast clinic already had a successfully operational database which contains information for the clinical management of patients, and treatment, as well as questions on barriers to diagnosis. Fields for the follow-up have been added so as to implement the ABC-DO design.
Data analysis plan Overall survival analysis will be carried out using time since first presentation at the hospital as the relevant timescale. Absolute survival probabilities overall, and stratified by baseline characteristics, will be examined using Kaplan-Meier curves for sites and subsets of women of interest for example, by stage and for invasive breast carcinoma only. Relative survival estimates  29  will be used to compare overall survival between sites as background mortality rates differ greatly between countries, especially in HIV-endemic populations. To investigate determinants of survival within sites, Cox proportional hazard models will be used to estimate relative risks of survival time since baseline interview associated with explanatory variables including baseline factors (eg, age, ethnicity, tumour characteristics) as well as time-changing explanatory variables (eg, having received chemotherapy, number of chemotherapy rounds).
Effects will be compared across sites before pooling the data across sites and estimating effects in a single Cox model stratified by site to account for between-site differences in the baseline hazards of death. In doing this, power will be gained to examine less-common exposures (eg, the effect of non-adherence to treatment, or survival in less common stages). To investigate the indirect determinants of survival (eg, socioeconomic factors, distance to hospital), crude HRs will be compared with those adjusted for relevant clinical variables to identify confounding and/or the pathways through which these indirect factors operate. We will also investigate the duration of the prediagnostic journey and its determinants using linear regression models and logistic regression models for a diagnostic journey of over 6 months, and for late-stage diagnosis.
MATERIALS AND METHODS We applied a mixed methods approach throughout 18 consecutive months of fieldwork. The first and second authors lived in two Tsimane' villages from January 2012 to August 2013. We obtained consent to carry out the research from the Gran Consejo Tsimane' (the Tsimane's governing body). Subsequently, a community meeting was held in two villages (V1 and V2) along the Maniqui River (Fig. 1 ) to explain the project to villagers and to assess their willingness to participate. Once we obtained consent to live in the villages, all adults (16 years of age and older) were invited to participate in the research. Women and men were equally encouraged to participate, resulting in a sample of 68 women and 79 men, for a participation rate above 90 % in both villages. To minimize the risks of gender bias in data collection, we trained a team of male and female assistants and translators (see Morgen 1989 in  Pfeiffer and Butz 2005).
Qualitative methods Qualitative data were collected through participant observation in medicinal plant foraging expeditions and remedy preparation. In the first months of fieldwork, 12 semistructured interviews were conducted with key informants (six women and six men) on the Tsimane' healing system. In these interviews, we asked about the most common health concerns in the area and how informants responded to those ailments. To address perceptions and socio-cultural norms around access to and control over medicinal plants, biomedicines, gendered behaviours and responsibilities regarding health care giving, three focus group discussions were carried out. We invited women and men of different ages in each village to the focus groups, what resulted in two groups of ten and four women, respectively, and one group of seven men.
We used data from the medicinal plant knowledge survey to construct the following individual-level variables: (1) number of plants known, (2) number of illnesses treated with the plants known, and (3) average number of uses per medicinal plant known. We used a Kolmogorov-Smirnov test to test for normality of distribution in our data. As data were normally distributed, we then compared the values of such variables by sex and age groups using a t test of mean comparisons. By aggregating women's and men's medicinal plant knowledge, we can explore whether these two groups hold diverse types of knowledge that may affect knowledge system performance.
Exploring the functional redundancy of medicinal plant knowledge To explore functional redundancy in Tsimane' medicinal plant knowledge, we collected data on the use of medicinal plants through a health survey. We differentiate between knowledge and use of medicinal plants because previous research indicates that the two do not necessarily overlap and medicinal plant knowledge may not be an accurate proxy for the actual use of medicinal plants and vice versa (Reyes-  Garcı ´a et al. 2005; Wayland and Walker 2014). The health survey was administered up to six times over the course of 12 months to the sample of 68 women and 79 men, most of whom had also answered the knowledge survey. We asked about the ailments suffered in the 2 weeks prior to the interview and the treatments used. Adults self-reported and mothers (or the adult in charge) reported children's health.