dataesr/scientific-paragraphs-categorization huggingface dataset:
This research was supported by the European Metrology Research Programme (EMRP) within the framework of a Researcher Excellence Grant associated with the Joint Research Project "International Timescales with Optical Clocks" (SIB55 ITOC), as well as the Deutsche Forschungsgemeinschaft (DFG) within the Collaborative Research Centre 1128 "Relativistic Geodesy and Gravimetry with Quantum Sensors (geo-Q)", project C04. The EMRP is jointly funded by the EMRP participating countries within EURAMET and the European Union. We also thank the reviewers for their valuable comments, which helped to improve the manuscript significantly. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecomm ons. org/licenses/by/4. 0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Regarding the individual GNSS and levelling surveys at the four selected clock sites, very similar procedures were employed in all cases and so the focus here is on details for the PTB case, which serves as a typical example. On the PTB campus, several clock laboratories exist in different buildings, in which height markers were installed and observed by double-run geometric levelling in connection to three existing national levelling benchmarks. For the levelling observations, a digital automatic level Leica NA3003 (includes an automatic data control and storage system) with bar-code fibreglass staffs was employed; the uncertainty of the entire system, as specified by the manufacturer, is 1. 2 mm for 1 km double-run levelling with fibreglass staffs (0. 4 mm with invar staffs). All levelling discrepancies (loops and double-run results) were found to be less than 1 mm, and the official heights of the national benchmarks and the levelling observations showed differences between 0.
Based on these findings, the overall (relative) levelling height uncertainty within the internal network on the PTB campus is estimated to be better than 2-3 mm. Figure  3  shows as an example of some of the markers and measurements taken in the Kopfermann building, which hosts PTB's caesium fountains as well as an ytterbium ion optical clock; the building also has a GNSS antenna on the rooftop, which is part of the IGS network and is denoted as PTBB. The levelling observations at PTB were for the most part performed during three different days in 2011, with supplementary information and data being collected until mid-2014, while the GNSS observations were carried out in a campaign over four full days from 15 to 19 September 2011 (i. e. four full 24-h sessions). The GNSS observations were processed with the Bernese 5.
Methods We used a database of 4723 primary studies included in 306 meta-analysis articles. These studies associated a risk factor with a disease in three biomedical domains, psychiatry, neurology and four somatic diseases. They were classified into a lifestyle category (e. g. smoking) and a non-lifestyle category (e. g. genetic risk). Using the database Dow Jones Factiva, we investigated the newspaper coverage of each study. Their replication validity was assessed using a comparison with their corresponding meta-analyses.
Results Among the 5029 articles of our database, 156 primary studies (of which 63 were lifestyle studies) and 5 meta-analysis articles were reported in 1561 newspaper articles. The percentage of covered studies and the number of newspaper articles per study strongly increased with the impact factor of the journal that published each scientific study. Newspapers almost equally covered initial (5/39 12. 8%) and subsequent (58/600 9. 7%) lifestyle studies. In contrast, initial non-lifestyle studies were covered more often (48/366 13. 1%) than subsequent ones (45/3718 1. 2%). Newspapers never covered initial studies reporting null findings and rarely reported subsequent null observations. Only 48. 7% of the 156 studies reported by newspapers were confirmed by the corresponding meta-analyses. Initial non-lifestyle studies were less often confirmed (16/48) than subsequent ones (29/45) and than lifestyle studies (31/63).
Because this influence represents a parameter that might confound our analysis, we decided to focus on studies associating biomarkers and risk factors with diseases. This type of biomedical research is crucial for improving the understanding, diagnosis and prevention of diseases and is thus often reported by newspapers. We recently built a large database of 595 meta-analyses investigating biomarkers and risk factors associated with four psychiatric disorders, four neurological pathologies and four somatic diseases  [11]. Starting from the assumption that each meta-analysis provided the best estimate of the 'true' effect size, we showed that most effect sizes reported by initial studies were inflated. In the present study we investigated three questions. First, we quantified and characterized the newspaper coverage of the scientific publications included in our database.
Scientific studies and their classification The database described in our previous publication  [11]  included 663 meta-analyses investigating the association between biomarkers, risk factors or cognitive/behavioral observations and twelve pathologies belonging to three biomedical domains: psychiatry (Attention Deficit Hyperactivity Disorder (ADHD), autism, major depression, and schizophrenia), neurology (Alzheimer's and Parkinson's diseases, epilepsy, multiple sclerosis) and four somatic diseases (breast cancer, glaucoma, psoriasis and rheumatoid arthritis). In a preliminary step of the present study we observed that initial studies related to cognitive/behavioral observations were never covered by newspapers. Therefore, the 68 meta-analyses dealing with cognitive/behavioral observations were not considered in the present study.
The 595 meta-analyses considered here were reported by 306 meta-analysis articles and included primary observations reported in 4775 scientific articles. As described previously, initial studies were distinguished from subsequent ones on the basis of their publication date (their references are given in our previous study  [11] ). From this database we excluded primary articles published before 1981. Indeed, we used the Dow Jones Factiva database to test whether these primary articles were covered by newspapers and the Factiva database starts on January 2nd 1981. Thus our final database includes 5029 scientific articles: 405 initial studies, 4218 subsequent studies and 306 meta-analysis articles. In our previous study, these 595 meta-analyses were classified into three types: genetic studies, brain imaging studies and "other" studies.
Impact factor of the journals that published scientific studies We used the Journal Citation Reports from The Web of Science to determine the 2012 impact factor of the journals that published the 5029 articles of our database. In our previous study we observed that studies associating ADHD with a biomarker or a risk factor were rarely covered by newspapers if they were published in a scientific journal with an impact factor below five  [19]. Therefore, when testing whether each study of our database was echoed by newspapers, we limited our initial search to articles published in scientific journals with an impact factor equal or superior to five. We observed that among the 91 scientific articles reported by newspapers and related to other pathologies than breast cancer, the lowest impact factor was 6.
Newspaper coverage of scientific studies As previously described  [19] , we used the Dow Jones Factiva database to find newspaper articles echoing the scientific studies of our database. Briefly, the following keywords were used in each search: "study OR research Ã OR scientist Ã ". Moreover, each search also used specific keywords that included the name of the pathology, one or several keywords that characterized the study (e. g. "gene", "milk", "birth weight") and was restricted to one month after the publication date of the study. Some searches, particularly about genetic associations, led to many items. In those cases, we added the name of the authors of the study, or of the university to the keywords. We only considered newspaper articles written in English and published in the general press.
Articles published in the specialized press (e. g. Pharma Business Week), as well as articles published by any press agency (e. g. Associated Press), were not taken into account. Sometimes newspaper articles found by this search actually highlighted a biomedical finding that was not included in our database of 595 meta-analyses. For example, some newspaper articles underlined risk factors associated with colon cancer, but did not mention the association of the same risk factor with breast cancer although it was also reported in the corresponding scientific article. In these few cases this media coverage was not considered. This search was performed by one author (EDM). Another author (FG) independently checked the possible media coverage of 20% of scientific studies. Both authors always reached agreement on whether any scientific study was echoed or not.
Replication validity of scientific observations reported by newspapers We used our previously published database of 595 meta-analyses to check whether biomedical findings echoed by newspapers were confirmed, or not, by the corresponding meta-analyses. As previously described  [11] , a primary observation was considered in agreement with the corresponding meta-analysis if it satisfied two criteria: first, both observations were in nominal agreement (i. e. both reported a statistically significant effect in the same direction or the absence of a significant effect) and, second, when both reported a significant effect, the effect size of the primary observation must not be more than twice as large as that of the corresponding meta-analysis (i. e. effect size inflation below 100%). Sometimes single scientific articles reported several associations included in our 595 meta-analyses while newspaper articles that echoed them did not specify which association they highlighted.
Overview of scientific articles reported by newspapers Among the 4723 primary studies of our database 156 were echoed by at least one newspaper article (Table  1 ). These 156 studies were covered by 1475 newspaper articles and were related to ten pathologies (ADHD, autism, major depression, schizophrenia, Alzheimer's and Parkinson's diseases, multiple sclerosis, breast cancer, glaucoma and rheumatoid arthritis). The primary studies included in our database and related to epilepsy or psoriasis did not attract newspaper attention. Moreover, 86 newspaper articles covered five out of the 306 meta-analysis articles of our database. The references of these 156 studies and of the corresponding metaanalysis articles are given in Supporting Information (S1 Text). The raw data about the studies covered by newspapers and their corresponding meta-analyses are also given (S1 File).
Media coverage of "positive" versus null findings All 53 initial studies covered by newspapers reported a "positive" finding (i. e. a statistically significant effect). It follows that none of the 174 initial studies included in our database and reporting a null effect (i. e. 43% of all initial studies) attracted journalists' attention (Table  2 ). Among the 103 subsequent studies covered by newspapers, only 14 studies reported a null effect. Regarding the non-lifestyle associations, among the 45 subsequent studies covered by newspapers, only five reported a null finding. Moreover this media coverage was small (10 articles) compared to that of all non-lifestyle studies (742 newspaper articles). The 58 subsequent studies of the lifestyle category were echoed by 733 newspaper articles. Only nine subsequent studies reported the absence of a significant association and these null findings were reported by 65 newspaper articles.
Therefore, assuming that, in our database, the percentage of subsequent studies reporting a null finding is at least as large as that of initial studies (43%) we observed that newspapers under-reported them. Indeed, only 13. 6% of the subsequent studies covered by newspapers reported a null finding (Table  2 ). Moreover, the number of newspaper articles covering each null finding was small, particularly regarding non-lifestyle studies (Table  2 ). Finally, only one of the five meta-analysis articles covered by newspapers reported a null finding. Moreover, this null finding was echoed by only four newspaper articles whereas the four "positive" meta-analyses attracted 82 press articles.
In agreement with previous studies  [24, 25] , we observed that newspapers preferentially reported lifestyle association studies linking a pathology to a risk factor on which each reader can act. Non-lifestyle studies related to brain imaging, genetic factor or other inescapable risk factors were less often echoed. This preferential coverage further supports the view that the first journalists' aim is to attract readers' attention. We also observed that newspapers never covered initial findings reporting null findings and under-reported subsequent null findings, particularly regarding non-lifestyle studies. As a consequence, journalists rarely cover null findings that contradict previously echoed studies. Within our database we observed only one interesting exception to this general rule: four newspaper articles covered the meta-analysis by Risch and coworkers and informed the public that it disconfirmed Caspi's initial study, which received much larger coverage (50 articles).
Limitations Although single primary studies have often investigated several associations linking a biomarkers or risk factors to a pathology (e. g. several polymorphisms on the same gene), we restricted our replication analysis to only one association per scientific article. Because we selected the most credible association (i. e. with the highest degree of statistical significance) it is likely that the replication rates we reported here are overestimated. The 4723 primary articles of our database were included in meta-analyses thus enabling us to assess their replication validity. Whether the newspaper coverage of association studies included in meta-analyses is representative of the coverage of all association studies remains an open question. We only considered newspaper articles published within a month following the publication of each scientific study.
Second, they strongly favor initial findings over subsequent studies when they cover non-lifestyle associations, but they equally cover initial and subsequent lifestyle studies. Third, among non-lifestyle associations covered by newspapers, initial findings are less often confirmed by meta-analyses than subsequent studies. However, our number of observations is too small to know whether this third conclusion also holds true regarding lifestyle studies. Indeed, our database included only five initial lifestyle studies covered by newspapers. We only studied newspaper articles but ignored television reporting, and this although television is the main source of information on medical sciences for Europeans (European Commission, 2007). Indeed, television programs rarely explicitly report on specific scientific publications  [31].
Finally, we denote the spatial approximation of the solution as follows u(t) = (u i (t)) d i=1 ∈ R d , with approximations of both initial condition u 0 ∈ R d , and source term g(t, ξ) ∈ R d that will be specified later. We assume that the time integration scheme is accurate enough for the error due to this approximation to be neglected. Numerical experiments were conducted with an in-house code written in Matlab R.
In the sociology of small-to mid-sized (O(100) collaborators) experiments the issue of data collection and storage is sometimes felt as a residual problem for which well-established solutions are known. Still, the DAQ system can be one of the few forces that drive towards the integration of otherwise loosely coupled detector systems. As such it may be hard to complete with off-the-shelf components only. LabVIEW and ROOT are the (only) two software systems that were assumed to be familiar enough to all collaborators of the AEḡIS (AD6) experiment at CERN: working out of the GXML representation of LabVIEW Data types, a semantically equivalent representation as ROOT TTrees was developed for permanent storage and analysis. All data in the experiment is cast into this common format and can be produced and consumed on both systems and transferred over TCP and/or multicast over UDP for immediate sharing over the experiment LAN.
Turning to the computing side of the experiment, the main challenge is not in the size or rate of data to be acquired 1 , but rather in the lack of common technology familiar to the entire 1234567890 CHEP IOP Publishing IOP Conf. Series: Journal of Physics: Conf. Series 898 (2017) 032014 doi :10. 1088/1742-6596/898/3/032014 collaboration: groups accustomed to running small test-stands tend to run various versions of LabVIEW, saving data in local files of disparate formats; collaborators with data analysis experience at CERN tend to be trained in the use of the ROOT  [3]  analysis framework and the related forms of data object compressed storage; the integrated DAQ and data storage package most commonly used by the antimatter experiment community (and inherited by collaborators who are 'native' to the field) seems to be MIDAS  [4].
The first effort was therefore spent in reducing the technology palette to a minimal set that would gain the confidence of most, if not all, collaborators. We started by putting together a test stand based on MIDAS. We quickly realised that the most noticeable shortcoming of MIDAS was neither code obsolescence nor lack of maintenance or support, but rather the fact that the package comes as a tightly integrated system. Developing new components that fit and morph into the existing MIDAS interfaces is a complex task, but it is required in case part of the needed functionality (front-end module support, storage data[base] format, access tools) is not found in the integrated package. We then focused on identifying and designing a small set of simple building blocks built only around ROOT and LabVIEW, providing the needed data acquisition, logging and access functions.
The AEḡIS data atom and its serialisation One data object whose expressiveness is well understood by many people in the collaboration is the LabVIEW Cluster. We therefore chose to limit the allowable numeric formats to what LabVIEW can use and to model structured data types around the LabVIEW Array and Cluster types 2. Two additional requirements were imposed: (i) that all data that circulate in the DAQ system be identified by a unique name (string) and include a common format timestamp (see Table  1  for details). (ii) that the recursiveness in data Cluster definitions be limited to what can be unwound into human-readable ROOT TTree definitions. The final data format for long-term storage can be thought of as a ROOT TTree translation of a LabVIEW cluster. In short, we allow clusters to contain arrays of scalar values, but we do not allow arrays of clusters or clusters containing other clusters.
This leads to the definition of the AEḡIS data atom shown in Table  1. It represents all data objects handled by the DAQ for both apparatus monitoring and physics data. The issue of serialising data atoms for network transfer was addressed by scouting various options available within the LabVIEW software library. We looked for a format with no proprietary data content in order to preserve the interoperability with non-LabVIEW components. A text format was perceived as acceptable in the foreseen data throughput scale, so we examined the default "Flatten to/Unflatten from XML" 3  functions, but passed on them as the produced XML code was too verbose. We then settled on the GXML  [5]  reference library, that provided a reasonable balance between expressiveness and verbosity. An example of the GXML representation of a data atom with a cluster data payload is found in Figure  2.
The main advantage of relying on a reference library provided by National Instruments is a guarantee that any internal component needed to unwind and serialise ("flatten" in LabVIEW jargon) data  objects will be maintained and upgraded. The NI-supplied GXML helpers were supplemented with a (C++) library to code, decode, transfer, translate GXML on other platforms. They were also integrated into higher level LabVIEW Virtual Instruments (VIs). Both developments are described in the following sections.
C++ library and tools All DAQ components and processes that are not run by LabVIEW create data atoms as instances of the LVTClusterData C++ class. The class can include a scalar payload, but is also provided with container-like properties to represent arrays and clusters: it can therefore represent any LabVIEW data type. A glance at Figure  3  shows that: • All C++ classes developed for handling GXML data can inherit from a ROOT TObject, so they can be constructed and used in the interactive ROOT shell. as a vector of LVTClusterData. On the other hand, LVTArrayData uses the ordinary, contiguous memory area storage for arrays and can therefore be made to point to existing array or std::vector storage, avoiding unnecessary memory copies. For increased data transfer efficiency (see Section 7) LVTArrayData can be serialised and deserialised in a compressed text format, in addition to the standard GXML format.
TCP Network Transfer (multicast capable) UDP Network transfer The remainder of the C++ library is used to transfer LVTClusters across the network. It includes base transmitter and receiver classes and their specialisation for TCP and UDP transfer. The UDP classes support multicast transfer. All network code is IPv6 compliant. While LVTClusters can serialise themselves in GXML format, the de-serialisation code is kept in the network receiver class to avoid bringing in unnecessary dependencies from the external XML library used for XML parsing (see Section 7 for details). The receiver class is also able to cast received data into a semantically equivalent ROOT TTree format for long-term storage and subsequent data analysis. The main users of the C++ library are: (i) The core data logging executable.
LabVIEW library Turning to the LabVIEW side of the design, while the basic code to serialise and deserialise data in GXML format is provided and maintained by National Instruments  [5] , we needed to: • Add integrated VIs for network data transfer (a quick review of these is shown in Figure  4 ). Data can also be retrieved from the experiment long-term storage by querying a dedicated web server over HTTP. • Work around performance shortcomings of the 32-bit LabVIEW memory allocator by developing a few Windows DLLs and executables to accelerate data conversion. More details on this specific issue can be found in Section 7. One of the most noticeable practical advantages of building the DAQ design around the native LabVIEW data representation lies probably in the ease of sending data from any piece of code run by LabVIEW anywhere on the experiment network, including National Instrument embedded real-time processors running on the front-end.
As long as data is tagged by a unique name, it can simply be connected to the appropriate VI and sent to the applicable TCP or UDP port for either run-data taking or long-term, continuous experiment monitoring. An example of how data can be sent to the DAQ is shown in Figure  5. As the main run-control functions can also be driven from LabVIEW, the palette of experiment-specific VIs was completed with run start/stop/status blocks (interacting with the main DAQ control node over the network).