dataesr/scientific-paragraphs-categorization huggingface dataset:
To put this in perspective, other highly normative MeSH categories are "health policy" with 8. 7% and "biomedical ethics" with 6. 2% editorial material. Most diseasebased categories like tuberculosis and neoplasms come in at between 1 and 2 percent editorial material. Using a more inclusive definition of the term, WoS categorizes 30% of the world/global health publications that it covers as editorial. 4 That means that in addition to the 13. 5 % that PubMed classifies as editorial there is another 17% or so whose categorization produces classificatory disagreement. This is not entirely surprising given the normative and advocacy orientation of so much of this literature. All these articles appeared in well over 1000 journals, with over 800 journals publishing five or more of the papers in the database.
Mapping platform In order to analyze the GH database, we used the software platform CorTexT (www. cortext. fr), which comprises algorithms designed to process bibliographic data and to perform several types of scientometric network analyses  (Rule et al 2015; Cointet et al 2012; Jones et al 2011). To display these links CorTexT applies a dynamic positioning algorithm that optimizes the location of all the nodes by minimizing the overall strain in the network. CorTexT also uses an automatic clustering algorithm to define (and color-code) clusters, i. e. cohesive subsets of the network that provide a high-level, fully bottom-up description of the network. To facilitate interpretation, CorTexT color-codes and adds circles around each cluster. The process of mapping was followed by a detailed, manual inspection of the content of individual clusters and their relationships.
The University of Washington, flush with Gates funding, has 615 mentions. The University of Toronto is not too far behind with 589. Among governmental institutions, authors associated role in the GH literature. They have exerted influence in other ways as well. This small cadre of authors has received 27% of all citations in the highly cited articles (10 or more citations) in our database. In other words, they are highly cited in the most cited articles. In order to examine co-authorship patterns, we produced a comprehensive set of cumulative co-authorship maps displaying the collaborative links between authors who published at least 12 papers. The first map, with only a couple of authors, goes back to 1988, and each subsequent map adds one year. This approach (as compared to simply producing maps for a given year or specified period) has the advantage of showing, when one moves from one map to the next, the animation-like concretion of a core-set of authors who will contribute to the definition of the domain.
There seem to be at least three different patterns of co-authorship. Harvard. Other clusters that emerged early and that remain visibly dense are tuberculosis and infant and child health. There are also small clusters that are visible early in the century but which disappear from view (e. g. climate and health, disability) as more and more co-authored publications are added to the data base. 2. Authors linked by advocacy. Individuals have increasingly come together in groups and consortia in order to advocate for one strategy or another or register complaints about GH politics. In the early 2000s, for instance, specialists in different disciplines co-signed pieces about the problems of the WHO  (Binka et al 2002)  or advocating a more intense response to AIDS  (Stover et al 2002). As GH gained in popularity and interest, more and more of these collective articles appeared, spearheaded by such groups as the Lancet NCD [non-communicable diseases] Action Group, the NCD Alliance and the Disease Control Priorities Project (a joint project of the Fogarty International Center of the US National Institutes of Health, the WHO, and The World Bank, launched in 2001 to deal with policy change).
Sometimes authors with rather different policy agendas come together for a specific purpose while at other times they co-sign articles with authors who have also worked with differently oriented groups. That is why the relatively dense cluster at the top center of the final map has remarkably little thematic coherence. It is held together by the existence of numerous multi-authored works on a variety of topics. 3. One final and critical source of co-authorship are collaborations for metric purposes. The Global Burden of Disease team has not just become denser with time; it has actively sought collaboration with other specialty clusters for whom its data are relevant. This advances both the role and credibility of GBD within the GH field but also Christopher Murray's apparently insatiable thirst for more and better data.
Can we say the same of the intellectual worlds in which they function, as understood through co-citation patterns? The Cognitive Landscape: Co-citations To get a sense of the cognitive landscape guiding the work of our core GH authors we examined the publications they most frequently cited. We limited ourselves to works cited at least 30 times by all the authors in our database. This gave us 203 cited works. Those most cited by our authors were several early reports and articles on the Global Burden of Disease, followed by the World Bank's influential World Development Report of 1993. 6 Unsurprisingly, the by-now numerous GBD-linked studies are cited frequently since they provide an ongoing source of data useful to many authors. The same is true to a lesser degree for the annual World Health Reports published by the WHO.
Funding Research funding has attracted increasing attention in recent years, but analyzing it remains a highly problematic exercise  (Grassano et al 2016). Nonetheless, our database can provide us with a first impression of GH research funding. WoS began systematically collecting information about funding in 2008. 8 While utilizing statements of funding sources and acknowledgments in its data, WoS complicates matters by including information contained in statements of conflict of interests; this results in mention of organizations, usually pharmaceutical companies, that paid researchers in the past, through grants, consulting or lecturing fees, editorial aid, and a variety of other functions and perks. This has been noted by at least one group of scientometric researchers  (Lewison and Sullivan 2015)  who calculated that such non-direct funding may constitute as much as 50% of WoS funding hits in some domains.
We initially considered cleaning up this data but eventually came to the conclusion that this effort was misdirected. Research funding is a complex phenomenon and is not just the result of targeted grants. It results from a dense web of previous grants, relationships with funders, non-specific or even non-financial benefits that allow researchers to interact, publish frequently, and collect yet more grants. One could call this configuration of direct funding, fees, salaries and perks the "financial ecology" of research. Consequently, we shall include in our analysis all information that WoS lists as funding, and seek, within the limitations of our data, to make sense of the "financial ecology" of GH publishing. We identified 4134 funding institutions in our full database. These appeared in 2177 different articles, in the majority of cases only once or twice.
Things look slightly different if we take account of the WoS ESI (Essential Science Indicators) collection of highly cited papers derived from a more complex series of indicators than mere citation numbers. 9 Simply put, ESI takes into account differences in citation behavior and numbers between different domains. While this is a very reasonable approach, it can be questionable in the case of GH, which is not a WoS recognized research area but, rather, an assemblage of articles from different areas. ESI lists the 550 most cited articles in our database. Since it limits itself to the last 10 years (starting in 2006), it has a much higher proportion of reported funding source -more than half of the articles listed -then the highly cited articles in our own database. Aforementioned caveats aside, the results are provocative.
(For a recent but hardly unique example see  McGoey 2015). Our data does not allow for overall judgments about the Foundation or its strategic choices, and different metric criteria may well produce somewhat different rankings of institutions that fund highly cited articles. But what is clear is that the overrepresentation of BMGF among ESI highly cited articles is due to its major role in funding large-scale quantitative research which, we know from our maps, has become central to the GH publication enterprise and its most cited component. Its strategic influence is compounded by its relative generosity. Articles funded by BMGF have fewer co-funders than those supported by other institutions. The 220 articles in which BMGF was involved contain 1075 mentions of funding translating into an average 4.
But it turns out that more than labels hold together this fuzzy intellectual domain. There is in the first instance a core group of authors who have for some time been co-producing papers and reports with one another, and whose publications are highly cited within this literary domain. There is in the second instance a number of central journals, and most notably The Lancet, that transcend the narrow specialization characteristic of scientific fields and that serve as major sounding boards for authors seeking a wide audience. Finally, there is a growing body of large-data metrics, most prominently the GBD that, whatever its origins in World Bank development strategies, and whatever the critiques that continue to be raised against it, now produces data that numerous different groups use for their own distinctive purposes.
High rapidity electron channel In this channel, the rapidity range of the measurement is extended by selecting one central electron and one forward electron. Forward electrons are defined as having pseudorapidities in the range 2. 5 < |η e | < 4. 9, reconstructed by the endcap or forward calorimeters. The data were collected using two singleelectron triggers in the central calorimeter region with E e T > 24 GeV or E e T > 60 GeV. The lower-threshold trigger has additional criteria for the shower shape and energy deposited in the vicinity of the electron candidate. The reconstructed central electrons are required to have E e T > 25 GeV, |η e | < 2. 4, and must satisfy the "tight" identification criteria. Electrons in the calorimeter transition regions 1. 37 < |η e | < 1. 52 are rejected. Leptons produced in the Drell-Yan process are expected to be well isolated from other particles not associated with the lepton.
The influence of residual misalignments is estimated from two sources. The first arises from the statistical uncertainty of the alignment corrections derived using Z → µ + µ -data and is considered uncorrelated. This component is propagated to the cross section using the pseudo-experiment method, and is separated into 84 uncorrelated components. The second source accounts for biases in the correction method, and is defined as the difference between the corrections derived for data and simulation in bins of η µ. This uncertainty is separated into 40 correlated components. After propagating this correlated source to the cross section using the pseudo-experiment method, the resulting uncertainty is found to be about 0. 2%, increasing significantly with | cos θ * | at large |y µµ |.
Compatibility with published data The cross-section measurements in the central electron and muon channels partially overlap with published DY measurements from ATLAS using the same data set. They are differential measurements of the Z boson transverse momentum spectrum  [16]  and of the high-mass DY cross section for m > 116 GeV  [17]. The compatibility of the data presented here with these two published measurements has been tested in identical fiducial regions, separately for the electron and muon channels. The measurements are in good agreement with each other. The reader is referred to  [16]  where the most precise measurements of integrated and p T -differential Z cross sections were made in the fiducial region p T > 20 GeV and |η | < 2. 4. For cross sections differential in m and |y | in the region m > 116 GeV, see the results presented in reference  [17].
This facilitates data analysis by healthcare workers, providing an easyto-use display of the results. Importantly, the integration of this lateral flow readout avoids the inherent ambiguity of fluorescence outputs, supporting the deployment of these diagnostic devices in rural communities (where there is a requirement that results can be readily and accurately interpreted; Fig. 1 ). The performance of the device was evaluated in humans in primary schools in St. Kizito and Mayuge Districts, Uganda, on 67 patients. Its effectiveness was benchmarked against two standard field-based techniques, RDT and light microscopy run at the same time, as well as against a laboratory-based, real-time PCR assay, carried out retrospectively. The sample-processing steps performed by this paper-based microfluidic device included DNA extraction, LAMP isothermal amplification, and lateral flow antibody-based detection of DNA amplicons.
Individual results, with ages and sexes of the children, are available in SI Appendix (including individual Ct values; see Dataset S1). The regions where the tests were carried out are endemic for malaria, and, out of the 67 samples tested, only three were negative for the reference real-time PCR test. The extremely low number of negative samples does not allow the specificity of the device to be assessed. We note that one negative sample tested as being positive by the paper-based microfluidic device. To validate the capability of the device to detect negative samples in a clinical setting, we tested 20 blood samples from healthy patients as a separate study (SI Appendix, Fig. S2 ), validated with PCR. The paper-based tests correctly identified all samples (as the experiments were performed in the laboratory, these results are not included in our analysis of the performance of the devices in the field; Tables  1  and 2 ).
We also evaluated the capability of the device to be used with minimal power requirements. For eight samples (see rows 66-73 in Dataset S1), we also performed the LAMP reaction on a local cooking stove, using a water pan to heat the device. The temperature of the water was controlled visually using a thermometer (SI Appendix, photo ESI-7), varying around 63 °C ± 4 °C. Using this method, the paper origami device correctly detected four positive samples (also positive by PCR) and two negative samples (also negative by PCR). One of these positive samples was negative by both RDT and microscopy. One additional positive sample was not detected by the paper device, but this sample was also not detected by any of the other field-based methods. One sample tested positive by the paper device and negative for all other techniques.
School children between the ages of 6 y and 14 y were recruited as a cohort (balanced for sex) under the supervision of their class teachers. Each child was assigned a unique identification number (ID), and their age, sex, name, parents' name (to ascertain consent), and school class were recorded. Details without names were computerized and anonymized using the ID numbers. No personal data were revealed to the investigators. Written informed consent of the parents and the head teacher were also obtained. The parents/guardians were informed of the activities by the head teacher at community meetings, and their verbal and written assent for their children's participation was obtained. Where parents were unable to write, their fingerprint was used as a signature (see SI Appendix. photos ESI-1 and ESI-2).
As stated, all samples were anonymized with an ID. Microfluidic testing, Giemsa staining, and microscopy as well as diagnostic assessment with the RDT were all carried out within the same school classrooms in parallel to testing on the paper-based microfluidic device without sample storage. Testing was double-blinded between on-site and reference tests. After testing at the school, all used paper devices and small plastic consumables were incinerated by burning on a bonfire, while glass slides and RDTs were stored in a biohazard container for safe disposal at the Vector Control Division, Kampala (SI Appendix, photo ESI-6). We also provide data from a separate study of 20 negative samples, in SI Appendix, Fig. S2 , obtained in September 2018 from Adicon clinical laboratories (Hangzhou, China) and validated on PCR.
Five milliliters of whole blood was obtained from patients using routine phlebotomy in a vacutainer, coated with heparin. The blood was stored in a fridge until use (for less than 8 h). Five microliters of the sample was taken out of the tube and used on the paper device, while 200 μL was processed for PCR (see Real-Time PCR). This latter work was performed in a laboratory and in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. All procedures were carried out in accordance with Measures on Ethical Review over Biomedical Research Involving Human Subjects  (49). All of the samples were collected from patients after obtaining informed consent, and the data from these patients were used in this study only.
ACKNOWLEDGMENTS. We thank  Dr. Poppy Lamberton  for help with ethical clearance and facilitation; Epigem Ltd. for help with device manufacturing;  Yujie Wang  and  Laurine Leriche  for device assembly;  Hang Zhao  for performing the confirmatory real-time PCRs;  Niall McDonald  for help with preparing artwork; and  Diana Ajambo ,  Andrew Nuwamanya , and  Alon Atuhire  for helping perform field diagnostics in Uganda. The study was supported by the  UK Global Challenges Research Fund , the  Scottish Funding Council , and  Engineering and Physical Sciences Research Council (EPSRC) Institutional Support Fund  (Grant  EP/R512813/1 ), as well by  EPSRC  (Grants  EP/I017887/1  and  EP/R01437X/ 1  also supported by the  National Institute for Health Research)  and the  European Research Council  (Grant  340117 ).
We present a study of photometric redshift performance for galaxies and active galactic nuclei detected in deep radio continuum surveys. Using two multi-wavelength datasets, over the NOAO Deep Wide Field Survey Boötes and COSMOS fields, we assess photometric redshift (photo-z) performance for a sample of ∼ 4, 500 radio continuum sources with spectroscopic redshifts relative to those of ∼ 63, 000 non radio-detected sources in the same fields. We investigate the performance of three photometric redshift template sets as a function of redshift, radio luminosity and infrared/X-ray properties. We find that no single template library is able to provide the best performance across all subsets of the radio detected population, with variation in the optimum template set both between subsets and between fields.
Specifically, we want to understand how the photometric redshift accuracy of radio sources varies as a function of radio luminosity and redshift. Do the current methods and optimization strategies developed for 'normal' galaxies or other AGN populations in optical surveys extend to radio selected galaxies? Finally, based on the results of these tests, we wish to construct an optimised method which can then be applied successfully to other survey fields in preparation for the next generations of radio continuum surveys (e. g. LOFAR/MIGHTEE: Rottgering 2010; Jarvis 2012) and the millions of radio sources they will detect  (Shimwell et al. 2017). The paper is structured as follows: Section 2 outlines the multi-wavelength datasets used in this analysis, including details of optical data used for the photometric redshift estimates and the corresponding radio continuum and spectroscopic redshift datasets.
DATA To maximise the parameter space explored in this analysis, we make use of two complementary datasets. Firstly, we make use of the extensive multi-wavelength data over the large ∼ 9 deg 2 NOAO Deep Wide Field Survey in Boötes (NDWFS  : Jannuzi & Dey 1999). Secondly, we also include data from the COSMOS field which extends to significantly fainter depths across all wavelengths but over a smaller ∼ 2 deg 2 area. 2. 1 Wide' field -Boötes Field.
2013) , covering the full NDWFS observational footprint. Finally, IRAC observations  (Fazio et al. 2004 ) at 3. 6, 4. 5, 5. 8 and 8 µm are provided by the Spitzer Deep Wide Field Survey (SDWFS,  Ashby et al. 2009). Although the available GALEX NUV data cover a significant fraction of the NDWFS field and reach depths comparable to the NOAO BW data, the large point-spread function (PSF) with full-width half maximum (FWHM) equal to ∼ 4. 9 could result in significantly increased source confusion relative to the other bands used in the catalog. As such, the NUV data were not included for the purposes of photometric redshift estimation. 1  Finally, we also include the u, g, r, i and z imaging from SDSS  (Alam et al. 2015). Although the limiting magnitudes reached by the SDSS photometry are not as faint as the NDWFS optical dataset at comparable wavelengths, the different central wavelengths of the SDSS filters provide valuable additional colour information for bright sources and are therefore worth including.
Optical photometry The optical/near-IR data used in the COSMOS field are taken from the C OSMOS2015 catalog presented in  Laigle et al. (2016). Laigle et al. (2016)  outline fully the details of the optical dataset, including data homogenisation, source detection and extraction. We therefore refer the interested reader to said paper for further detail. For the analysis in this paper, we use the seven optical broad bands (B, V , g, r, i, z + /z ++ ), 12 medium bands (IA427, IA464, IA484, IA505, IA527, IA574, IA624, IA679, IA709, IA738, IA767, and IA827) and two narrow bands (N B711,N B816) taken with the Subaru Suprime-Cam  (Capak et al. 2007; Taniguchi et al. 2015). Also included at optical wavelengths are the u * -band data from the Canada-France-Hawaii Telescope (CFHT/MegaCam) as well as Y-band data taken with the Subaru Hyper-Suprime-Cam.
Spectroscopic redshifts Spectroscopic redshifts for the COSMOS field were taken from the sample of redshifts compiled by and for the Herschel Extragalactic Legacy Project (HELP; Vaccari 2016, PI: S. Oliver). 2  The compilation includes the large number of publicly available redshifts in the field (see  Laigle et al. 2016 , and references therein) and a small number of currently unpublished samples. In total, the sample comprises 44,875 sources extending to z > 6 and with ∼ 12, 000 sources at z > 1. Thanks to the optical depths probed by both the photometric and spectroscopic data available in the COSMOS field, the 'Deep' spectroscopic samples a range of galaxy types magnitudes, redshifts which may be missing from the 'Wide' sample. Despite this, the subset of radio detected galaxies with available spectroscopic redshifts may still be biased against towards brighter sources and populations with higher spectroscopic success rates.
2014; Carrasco Kind & Brunner 2014b). Furthermore, because the computationally expensive training step only occurs once these methods can also be significantly faster than template fitting when applied to very large datasets. However, the drawback of training sample methods is that they are very dependent on the parameter space covered by the training sample and its overall representativeness of the sample being fitted  (Beck et al. 2017). While template-fitting methods do benefit from additional optimisation through spectroscopic training samples (Section 3. 2, see also  Hildebrandt et al. 2010; Dahlen et al. 2013) , they can be applied effectively with no prior redshift knowledge and tested without spectroscopic samples for comparison  (Quadri & Williams 2010). Fully representative training samples for the rare sources of interest are not yet readily available for many different fields.