dataesr/scientific-paragraphs-categorization huggingface dataset:
The H. antecessor molar sample comprises 19 specimens, 11 mandibular and eight maxillary elements representing all molar classes  [38]. In this study, we excluded two teeth belonging to individual H3  [53] , its upper right M1 because of its more advanced wear degree than the antimere and its upper left M3 due to the too early stage of development (the crown is not fully formed). Therefore, we analysed 17 molars, 11 mandibular and six maxillary, belonging to H. antecessor (Table  1 ). Moreover, and for comparative purposes, the study included 355 molars belonging to extinct and extant populations of the genus Homo of African, Asian and European origin. Part of the comparative material and associated data (measurements) used for the 2D and 3D (complete molar crown) enamel thickness was extracted from the literature (Table  1 ).
However, data on both 3D complete crown and lateral enamel thickness is limited. Therefore, we also included molars (n = 88; original data) belonging to H. erectus from Sangiran (n = 4), North African Homo from Tighenif (n = 3), East African Homo from Eritrea (n = 1), European Middle Pleistocene Homo from Fontana Ranuccio and Visogliano (n = 2), Neanderthals (n = 26) and modern humans (n = 52) from different origins (see Table  1 ). The Neanderthal sample, including the specimens from Regourdou, Krapina, Abri Suard and Abri Bourgeois-Delaunay, is available online in the NESPOS database. Microtomographic scanning (mCT) of the samples was performed at the CENIEH facilities. Isolated fossil teeth were scanned with Scanco Medical Micro-CT80 system, using the following parameters: 70 kV and 114 mA, 0.
Acknowledgments The authors acknowledge all members of the  Atapuerca Research Team  for their dedication and effort. To  Pilar Fernandez Colo  ´n and  Elena Lacasa Marquina , from the  CENIEH Conservation and Restoration Laboratory  for their work with the conservation of the specimens. The authors are grateful to  Dr. Priscilla Bayle  ( PACEA Lab. Universitede Bordeaux, France ) for her valuable comments and scientific discussion that greatly improved the study and to  Dr. Santos  ( PACEA Lab. Universitede Bordeaux, France ) for his advice and discussion for the statistic analysis. Moreover, the authors are grateful to  Dr. Tanya Smith  ( Griffith University, Australia ) for kindly providing part of the modern human data. We also acknowledge  The Leakey Foundation  through the personal support of  Gordon Getty  (2013) and  Dub Crook  ( 2014-2017 ) to one of the authors (MM-T).
This study was part of the  Ministerio de Economı ´a y Competitividad (MINECO) , project  CGL2015-65387-C3-3-P (JMBdC ). This study received financial support from the  British Academy  ( International Partnership and Mobility Scheme   PM160019 ), the  French State in the frame of the "Investments for the future" Programme cuspal enamel ). Future studies on European Middle Pleistocene populations will provide more insights into the evolutionary trajectory of the typical Neanderthal dental structural organization. IdEx Bordeaux , reference  ANR-10-IDEX-03-02  (LM-F) and the  Leakey Foundation  through the personal support of  Gordon Getty  (2013) and  Dub Crook  ( 2014-2017 ) (MM-T). LM-F is beneficiary of an  IdEx Post-Doctoral Research Grant , MMdP is beneficiary of a  Fundacion Atapuerca Post-Doctoral Research Grant.
fMRI data analysis Processing of the fMRI data involved Statistical Parametric Mapping software (SPM 5, Welcome Department of Imaging Neuroscience, London UK; http://www. fil. ion. ucl. ac. uk/spm) running under Matlab (The Mathworks, Inc. , Natick, MA, USA). The first five scans of each run (i. e. , the first 12. 5 s and hence the first two or three sequences) were discarded to eliminate non-equilibrium effects of magnetization and warming up effects in the participants. For preprocessing, the functional images were realigned with respect to the first functional image for motion correction and were corrected for slice acquisition timing in reference to the middle slice in each scan. The resulting volumes were spatially normalized to fit to an EPI template in MNI (Montreal Neurological Institute) space.
This is because if the transponder's antennas are exposed to, for example, strong winds, then any minor alterations in their orientation (even in sub-mm level) may lead to erroneous observations. Several solutions for the transponder housing had been evaluated given certain constraints regarding construction material, operating environment, etc. In the end, it has been decided that the transponder be protected by a sliding roof that will move and leave the instrument free of any signal interference during calibrations (Figure  13 ). In summary, the CDN1 Cal/Val site has been fully operational as of September 2015. It provides external, independent post-launch calibration services for all Ku-band altimeters that fly over the area. Processing of this network data from those permanent GNSS sites is carried out using three different types of scientific software: GAMIT [35], Bernese  [36] , and GIPSY  [37].
This DORIS site had been installed on 27 September 2003, as  Processing of this network data from those permanent GNSS sites is carried out using three different types of scientific software: GAMIT [35], Bernese  [36] , and GIPSY  [37]. The coordinates and their velocities for each GNSS site are estimated along with the ionospheric and tropospheric signal delays. These atmospheric signal delays are also used to provide ground truth verification for the radiometer on board the satellite altimeter. Tables  2  and 3  show the latest results for the coordinates, velocities, and their processing uncertainties of 11 such GNSS stations in the ITRF 2008 reference system. Independent positioning systems and processing techniques have been applied to achieve FRM standards for the determination of geodetic coordinates and results.
For example, an inter-comparison and cross-examination of tropospheric delays, as determined by GPS [called "GVD0"] in Gavdos but also with a collocated DORIS beacon (less than 50 m away, called "GAVB") (Figure  16 ), have been carried out  [38]. This DORIS site had been installed on 27 September 2003, as Processing of this network data from those permanent GNSS sites is carried out using three different types of scientific software: GAMIT [35], Bernese  [36] , and GIPSY  [37]. The coordinates and their velocities for each GNSS site are estimated along with the ionospheric and tropospheric signal delays. These atmospheric signal delays are also used to provide ground truth verification for the radiometer on board the satellite altimeter. Tables  2  and 3  show the latest results for the coordinates, velocities, and their processing uncertainties of 11 such GNSS stations in the ITRF 2008 reference system.
3. The transponder at CDN1 Cal/Val facility has produced a range bias for Jason-2 of -1. 2 cm for Cycles 267-303 with SGDR-D and Level-2 data products. The associated SSH bias of Jason-2 along Pass No. 18 for the same cycles amounts to +1. 7 cm based on Gavdos Cal/Val facility. Biases derived using transponder and sea surface techniques exhibited absolute values of about the same magnitude but with opposite signs. These results back each other up as they are determined by diverse calibration techniques, different locations, settings, and instrumentation; 4. Jason-3 demonstrated a bias at determining sea-surface heights of -0. 50 cm along the ascending orbit and -0. 74 cm along its descending orbit, as determined by the Gavdos Cal/Val site. Results were based on GDR-D products and its first 80 cycles of Jason-3.
Acknowledgments: Part of this work has been supported and funded by the  European Union , the  European Space Agency , and the  Centre National d'Etudes Spatiales, France. We greatly thank  Joana Fernandes  and  Telmo Vieira ,  University of Porto, Portugal , for helping us better understand the wet troposphere delays and for verifying the produced results. We also thank  Mingsen Lin  and  Hailong Peng , the  National Ocean Satellite Application Center, China , for providing help and support for the HY-2A satellite. Ole Andersen  of the  Danish Space Center has  provided mean sea surface models for the region.
Such information could lead to a better understanding of the epidemiology of these diseases in adolescents and young adults and improvements in healthcare organization. Furthermore, these youths must face the challenges of this period related to physiological, psychological, and socio-economic changes, as all adolescents. Conducting studies comparing youths with or without a chronic disease could help identify the role of the chronic condition in healthcare use versus that of entry into adulthood. The French national health insurance database is one of the largest databases in the world and has been extensively used to guide public health policies in France, as these data allow systematic follow-up of all medical care received in France, including that of the low-income population  [18] [19] [20].
With the Universal Health Coverage Act (opening up the right to statutory health insurance coverage on the basis of residence in France), data on almost all French residents, i. e. , people living in France (with or without French nationality), are registered in this database. In addition, a list of major groups of chronic diseases, for which the severity and/or chronic nature requires prolonged treatment and costly therapy, has been established by decree as "Affections de Longue Durée" (long-term diseases, LTD) to allow patients to receive full reimbursement for expenditures related to the LTD. This provided the opportunity to estimate the prevalence of adolescents with a LTD diagnosed before 14 years of age at the national level and to describe the mortality and short-stay hospitalization rates between youths from 14 to 21 years of age, with or without a LTD.
The EGB The French national health insurance offers access to a large ongoing random sampling of 801,047 (at the time of this study) for research purposes, the Echantillon Généraliste des Bénéficiaires (EGB)[General Beneficiaries Sample]  [18, 21, 22]. The EGB includes exhaustive records of hospitalizations in short-stay units (medical, surgical, obstetrical) from the national hospital discharge database, from 2005 to the end of 2015, and all data on LTD and mortality for subjects insured under the French general health insurance schemes, representing 80% of the population. The other types of hospitalizations (i. e. , hospitalizations in psychiatric units, homehospitalizations, and hospitalization in long-stay units) were not exhaustively recorded in the database during the study period and were thus not analyzed.
Study design We selected a retrospective cohort using data of youths between the ages of 14 and 21 years to compare mortality and hospitalization rates between adolescents with or without a LTD. This age range, from one year before healthcare adulthood (in France, patients can be managed in adult care from the age of 15 years) to three years after legal adulthood (18 years old), thus covered the period of transition to young adulthood.
Outcomes We studied two types of outcomes: 1) all-cause mortality, using data from the national death registry and 2) hospitalizations in short-stay units. Two categories of hospitalizations were considered: unplanned, defined as an admission through an emergency department (ED) and planned, without admission through an ED. We also explored hospitalizations for an external cause, as a proxy of exposure to high-risk situations, such as injuries and road accidents, which are major causes of death in youths. An external cause was defined as a main diagnosis from the ICD-10 chapter "Injury, poisoning, and certain other consequences of external causes", excluding complications of medical or surgical care, allergies, or intoxication by non-psychotropic drugs, which may be related to the LTD.
Scientific data on this topic are inconsistent  [31] [32] [33]. We could not explore this issue, as data on the specialty of the department of hospitalization was not available in the French national health insurance database during the study period. However, access to care could be studied in a future analysis by considering data on outpatient care and consultations with private physicians, which are available in the EGB. This could help to determine whether there is a discontinuity in patient care or a transition from inpatient care to outpatient care when the patients become young adults.
Background. High-throughput technologies are rapidly generating large amounts of diverse omics data. Although this offers a great opportunity, it also poses great challenges as data analysis becomes more complex. The purpose of this study was to identify the main challenges researchers face in analyzing data, and how academic libraries can support them in this endeavor. Methods. A multimodal needs assessment analysis combined an online survey sent to 860 Yale-affiliated researchers (176 responded) and 15 in-depth one-on-one semi-structured interviews. Interviews were recorded, transcribed, and analyzed using NVivo 10 software according to the thematic analysis approach. Results. The survey response rate was 20%. Most respondents (78%) identified lack of adequate data analysis training (e. g.
, R, Python) as a main challenge, in addition to not having the proper database or software (54%) to expedite analysis. Two main themes emerged from the interviews: personnel and training needs. Researchers feel they could improve data analyses practices by having better access to the appropriate bioinformatics expertise, and/or training in data analyses tools. They also reported lack of time to acquire expertise in using bioinformatics tools and poor understanding of the resources available to facilitate analysis. Conclusions. The main challenges identified by our study are: lack of adequate training for data analysis (including need to learn scripting language), need for more personnel at the University to provide data analysis and training, and inadequate communication between bioinformaticians and researchers.
It remained open until March 15, 2016, and contained the following sections: (1) Demographics, (2) Information/Data-seeking assessment, (3) Databases, software and tools, (4) Training and (5) Collaborative work and networking events. A copy of the survey instrument is available online: https://works. bepress. com/rolando_garciamilian/25/. In addition, fifteen in-person interviews were arranged with volunteers who responded to the survey and were selected from the main three respondent groups of the survey: five faculties, five postdocs, and five graduate students. Interviews were conducted at a time and location chosen by the participant and were digitally audio-recorded (authors RGM/DH/FD). Each interview lasted about 30 minutes. All participants were asked the same questions while retaining the flexibility to explore participants' responses in further detail where necessary.
Total responses Analysis of high-throughput data (e. g. , microarray data, RNA-seq) When asked about main challenges with data analysis (Q. 6), most respondents (78%; n = 130) identified lack of adequate training as the main challenge they face in their research. The next most noted challenge was not having the proper database or software (54%; n = 130) to expedite analysis. Under ''Other,'' respondents mentioned: training beyond the basics of command-line, and inadequate Information Technology (IT) support. However, not having the proper training is perceived as a bigger obstacle for graduate students and postdocs (94% and 86% of respondents) than for faculty (62%). In contrast, the main challenge for faculty is not having the adequate software, databases or tools (73%). Postdocs also identified this as a problem (62%), however not having the adequate software, databases or tools was not the main concern of graduate students (28%) (Figs.
3A 3B ). Respondents indistinctively replied affirmatively (52%; n = 129) or negatively (48%; n = 129) to the question: Does finding, retrieving, analyzing high-throughput omicsrelated, or other type of data represent a bottleneck for your research project? (Q. 7). However, when asked: Do you have data that you have not been able to analyze? Only twenty-seven percent ( n = 128) responded affirmatively that this is due to the lack of proper software (Q. 8) while 47% (n = 128) attributed it to the lack of training (Q. 10). When asked about the type of training sessions needed, programming with R, Python, Perl, Unix, and Linux for the analysis of high-throughput data (e. g. , microarray, RNA-sequencing) were mentioned the most. Others included Galaxy, MatLab, SPSS, and statistics for biologists (Q.
Interviews We used Nvivo 11 Pro for Windows software (QSR International Pty Ltd) for qualitative analysis of the text to identify main challenges for data analysis reported in the interviews. While researchers enumerated various challenges related to data analysis, two main themes emerged from the interviews; personnel and training. Researchers feel they could improve data analyses practices if they had better access to the appropriate expertise for data analyses, and/or training in data analyses tools and resources. In both cases, interviewees noted time as an underlying barrier. Researchers consistently commented on the time it takes for them or others to analyze data; difficulty in finding time to acquire expertise in using data analyses tools; and a lack of time to investigate and evaluate the resources available to help them analyze their data.
Training challenges Training is also a significant challenge to successfully analyze data. Among the 15 interviewees, 10 commented on problems related to the lack of training that would allow them to conduct their own data analysis. In order to efficiently use the preponderance of new tools and resources, a certain level of expertise in data analysis is required. Unfortunately, researchers recognize that they do not always have a strong background in this type of work. These researchers want to be able to do their own analysis and not rely on other staff. Many would like access to commercial bioinformatic software. Their training needs vary from learning how to use specific commercial tools such as Ingenuity Pathway Analysis (IPA), to how to do bioinformatics analysis using a high-performance computer cluster, code with R, or a desire for more general training in statistics.
, 2015; Oliver, 2017). Those who responded to our survey specified training needs such as programming or scripting language (e. g. , R, Python, Perl, Linux) for the analysis of high-throughput data as the top desired type of training (Survey Question 11). As biomedical research has become more data-intensive, some believe that the ability to design and write computer programs is among the most crucial skills that a contemporary biologist should acquire and develop  (Ekmekci, McAnany & Mura, 2016). However, it is important to notice that the researcher's perceived need to learn programming and command-line may ignore the consequent investment of time needed to learn these skills. Our study also identified one paradox, where researchers asked for training, but also reported the lack of time for attending training sessions.
One study describes this disconnect as the result of the absence of statisticians from the planning stage of the original research at which point any concerns or questions about the analyses could be resolved proactively  (Carvalho & Rustici, 2013). It is important to mention that previous studies have identified researchers' concerns regarding hardware, data storage and space to analyze data  (Norton et al. , 2016). Our survey results identified this as a problem only for the 30% of respondents. Interviewees have not indicated concerns with data storage nor with computing storage. At Yale University, the primary and secondary stages of the data lifecycle, where big/raw data files are generated, usually takes place in core units (e. g. , Yale Center for Genomic Analysis). In addition, the Yale Center for Research Computing offers different tiers for storage and sharing data services in a secured manner (https://research.
These types of sessions can promote awareness of applicable tools and how to use them which researchers can choose to purchase if the library cannot. Another strategy followed has been to partner with other centers and organizations on campus to provide training opportunities. Creating partnerships with other campus units and individuals (e. g. , bioinformaticians, faculty) allows libraries to broaden and package data analysis services, including training opportunities. When training needs exceed library capacity, partnering with intramural and extramural units will be crucial in library support health sciences bioinformatics research  (Oliver, 2017). As discussed above, there is a perceived need to learn R and scripting language. Many large academic institutions including Yale University have statistical consulting services, with staff who can provide coding classes in languages such as R, Unix, or Python.
Therefore, they are better able to tailor the instruction material to the attendees' current needs which can result in better learning outcomes  (Yu et al. , 2011). Lastly, academic libraries can make it a strategic priority to build up their own bioinformatics programs. To do this successfully, library administration would need to devote more funding for commercial bioinformatics software to assist with routine analysis, and to hire librarians with backgrounds in bioinformatics who can identify and manage these resources and develop workshops and training sessions tailored to their researchers' needs  (Li, Chen & Clintworth, 2013). This investment, however, would give libraries a new relevancy to constituents who may not feel connected to the library as a service provider. As this manuscript was being written, a newly created Yale Center for Biomedical Data Science was inaugurated with the goals of ''build critical computational & informatics strength in a range of fields'', and ''to connect experts and non-experts''.
Observation and reanalysis data sets We used two observational temperature data sets, based on in situ measurements from meteorological stations: HadCRUT4 (45) and the GISTEMP  (46). The latter data set has been interpolated over a scale of 1200 km to fill spatial gaps between stations, which is the reason for the very uniform changes in the Arctic in fig. S8   (46). Because these data sets report temperature as standardized anomalies, we did not calculate absolute changes in variability, but only the direction of change as Kendall t values. The GISTEMP data set has been obtained as version 5 from the GISS, https://data. giss. nasa. gov/gistemp/. The HadCRUT data set (version 4. 6. 0. 0) has been obtained from https://crudata. uea. ac. uk/cru/data/ temperature/. Both data sets have been downloaded on 31 January 2018.
Acknowledgments: We are grateful to two anonymous reviewers for constructive comments;  M. Flach ,  E. van Nes , and  B. van der Bolt  for valuable feedback; and  P. Cox  for his advice and inspiring discussions about the surface energy balance, climate variability, and the smell of rain. Funding: S. B. and M. S. acknowledge support by the  Netherlands Earth System Science Centre , financed by the  Ministry of Education, Culture , and Science. T. M. L. was supported by a  Royal Society Wolfson Research Merit Award , the  Natural Environment Research Council  ( NE/P007880/1 ), and the  European Union Seventh Framework Programme   FP7/2007-2013  under grant agreement no. 603864  [ High-End cLimate Impacts  and eXtremes (HELIX)]. V. D. was supported by an  Adaptation to Changing Environments ETH (Eidgenössische Technische Hochschule Zürich)  fellowship.