dataesr/scientific-paragraphs-categorization huggingface dataset:
We examine this by looking at the formation of international co-authorship ties of South African academics from 2005 to 2012. We first differentiate among the ways local scientists can acquire foreign research collaborators. The type best documented in the literature, of an individual developing unique ties to a foreign collaborator, occurs in about a fifth of the cases in our dataset. The bulk of the international scientific collaborations that we observe, though, results either from repeated collaborations with foreigners, from foreign ties acquired jointly by locals, or via referrals from local co-authors or colleagues. For each type of IRC formation, we attempt to identify causal effects notably of (i) the focal scientist's international social capital, (ii) her local peers' international social capital, and (iii) the joint involvement of local peers in newly created IRCs.
This also holds true for developing economies that rely particularly on IRC to access funding and other resources needed for research that are not available in the home country  (Pouris and Ho, 2014). Barnard et al. (2012)  analyze a dataset on South African scientists similar to the data we use and find that internationally renowned scientists are well connected both internationally and to local scientists through (direct and indirect) co-authorship links. If international collaboration is more beneficial than local collaboration, in particular in resource-poor environments, why then are international and local collaboration strongly positively correlated? Explanations, put forward but never tested, are that scientific excellence and productivity cause collaboration in general (domestic and international), that individuals have a general taste for collaboration (e.
Sample The following paragraphs present what we consider the main features of our sample. More detailed discussions on various aspects of the data are provided in Appendix 1. 1. Our focus is on how scientists active in the South African academic community establish coauthor links to foreign scientists. The scientists rated by the South African National Research Foundation (NRF) form our sample. The NRF is mandated with the support of research within South Africa, in particular, but not only, within the university system. Since the mid-1980s, one of its activities has been the "rating" of researchers at universities and other public research institutions. The rating system has first been applied in Science, Engineering, and Technology (SET) where it widely diffused within 10 years, reaching saturation in the mid-1990s  (NRF, 2005, p.
5 NRF ratings involve a rigorous evaluation process and so can be used as an effective measure or indicator of the quality of a researcher's output. There are strong incentives for participation in the process both for the individual researcher and for her institution  (Pouris, 2007). Therefore, the vast majority of (academic) researchers in South Africa do apply for ratings, so the coverage of the dataset is very good if the population in question is research-active scientists. It seems likely however that our dataset covers SET research somewhat better than SSH research, not only due to the relatively late introduction of ratings in SSH but also due to relatively low funding needs in SSH compared to SET. Therefore, we focus in the following on SET scientists, generally referred to as "scientists" in the following.
6  Our sample is based on digital files of the NRF established in the period from 2002 to 2015. During that period, 2762 SET scientists applied for at least one NRF rating. 7 Some scientists did not receive a valid rating for several reasons-mostly due to ineligibility or incomplete application or because the rating was in process at the time of the data snapshot. A valid rating decision, including an unsuccessful rating, was obtained by 2696 scientists. Table  1  describes the basic features of our sample of scientists with at least one valid rating decision. Scientists are characterized by basic social variables (constant), scientific field (constant), higher education degrees (constant), employment (dynamic), rating (dynamic), and their scientific output in terms of papers (dynamic). Co-author networks are measured on the scientific publications entered into the NRF database by South African scientists for various programs run by the NRF, most notably their rating applications.
Removing entries with missing data and disambiguating names in the publication data leave 163,591 authors (individuals). Co-authors of NRF-rated scientists are identified as being local or foreign based on (i) additional information in the NRF dataset (employment in SA or corresponding e-mail in SA), (ii) a complementary dataset on publications of South African authors 5 From the NRF website: A -Leading international researchers; B -Internationally acclaimed researchers; C -Established researchers; P -Prestigious Awards; Y -Promising young researchers; L -Latecomers to Academia.
The network is constructed on peer-reviewed journal publications. We exclude conference proceedings to avoid double counting of scientific collaborations from one year to another. Furthermore, each scientist enters her scientific output up to the time of filing for a rating (or a project) application, implying that the same article appears in the dataset twice if two different scientists file the same, co-authored paper. 8  The potential duplication of co-author links prompts us to work with unweighted, binary networks. With these data at hand, we construct an unbalanced panel spanning the period from 1996 to 2014. The panel traces individual scientists from the first year of South African employment after obtaining a PhD until one year before their last observed rating. Individuals for which this information is missing cannot be included in the panel.
We consider this a repeated tie, whatever the involvement of a focal scientist's peer j. So what we focus on here is i's deepening of existing foreign relations (the k's). If not repeated, it could be a "referral" from a peer, i. e. there is no prior tie between the focal scientist i and the foreign scientist k, but a prior tie between focal's peer j and k, where the 9 Note that in our dataset, we do not observe ties among foreign scientists. 10 A network motif in general is an isomorphism class defined on a subgraph, the simplest is an undirected edge, the most prominent a closed triangle (see  Holland and Leinhardt, 1976). Temporal network motifs are isomorphism classes on temporal networks taking into account time information on edges and potentially nodes. The simplest and most prominent temporal network motif is a repeated tie, that is the introduction of a tie in time t that already has been created at some time before.
Peers' foreign social capital counts the total number of foreign co-authors accessible to an individual i in exactly two steps through her South African social networks (see Figure  1 ). It is a count of the number of foreign collaborators in my neighbors' (cumulated) networks who are not in my own. There are two sources: my co-authorship neighbors (N s,a i,t ) and my faculty neighbors (N f ,a i,t ). 12 3. 2. 3. 4 Scientific quality is measured by the NRF rating. Researchers in our data undergo an NRF rating roughly every 5 years (see Section 3. 1), and we use the ratings current at the time of the publication as a measure of quality (q i,t ). A scientist in a panel year before her first rating is assumed to be of the quality of her first (future) rating.
We suggest that an exclusive focus on the individual and her human capital tells only a small part of the overall story. In this paper, we show the potentially helpful effect of not simply human but also social capital and the role of the scientific community in which the scientist finds herself in enabling global collaborations. Using a robust dataset from South Africa, we advance the literature on international scientific collaborations in countries with smaller and less-developed science systems. Differentiating between the different ways in which foreign ties are acquired allows us to advance a systemic understanding of how science advances. Individual tie acquisition of new foreign collaborators does happen, but it represents only about 20% of all collaborations. A focus on an individual and her attributes, including her international exposure, seems less helpful, given that the great majority of foreign tie acquisitions happen jointly, through repeated ties or even via referrals.
But our work also suggests the value of a more thorough engagement with the systemic and system-wide functioning of science, also at the local level, to serve not just the global project, but particularly development. (suggested e. g. by  NRF, 2005, p. 18, Fig. :  total number of rated researchers in  SET: 1985 SET: -2003)). In 2002, the rating system was expanded to include social sciences and humanities (SSH). In our dataset, in that first year around 207 SSH researchers obtained their first NRF rating compared to 32 SET researchers. In the subsequent three years, until 2005, further catching-up happens. The ratio of SSH to SET in terms of first ratings then normalizes to around 1:2. On the other hand, the panel that we construct from the rating data displays each year (from 1996 to 2014) that same ratio of 1:2 in terms of active researchers.
Thus, the relatively late inclusion of SSH does not lead to a shifting composition of the scientific domain in our panel. There is no strong pattern of missing information over disciplines, except for arts with 18% missing. Also, foreign and local graduates have similarly few missing observations. Missing information is particularly high for older scientists, born in the 1940s and 1950s, and those scientists with a last rating in the year  2002, 2003, or 2004. It seems possible that in particular older scientists with some reputation managed to sidestep a complete digital filing in the earlier years of the NRF data platform, perhaps with a reference to earlier dossiers in paper format. All individuals in the sample have (by sample construction) at least one valid rating during the period 1990-2015, with seven being the maximum number of ratings observed for one individual.
Network sample Co-author networks are used not only to count foreign ties of our focal scientists but also to measure the network context in which foreign ties are acquired. Therefore, we aim at an exhaustive description of the network, covering South African scientific collaboration as much as possible, by including in particular papers of rated scientists that are not part of the main sample. The data creation process itself has implications for network construction. Scientists entered their (past) publications during a period from 2002 and 2014 for various programs run by the NRF. They enter their scientific output up to the time of filing for a rating (or a project) application. Thus, a unique article enters the data twice in case two different scientists file the same, co-authored paper.
27 The potential duplication of co-author links prompts us to work with unweighted, binary networks. On the other hand, papers are missed if the paper is published after the last filing of a scientist. This leads us to drop focal scientists from the panel at the year before their last filing because foreign ties are observed only partly or not at all thereafter. A second effect is a potential measurement error on the collaboration network among South African scientists. Publication data fields include among other things publication year, publication type, (co-)authors, title, and journal. After some initial cleaning, 28 we obtain 308,412 publication entries with publication dates between 1990 and 2014; 235,712 peer-reviewed journal articles and 72,700 peer-reviewed conference proceedings. The main analysis is based on peerreviewed journal articles.
This avoids duplication of collaboration ties when a conference paper is subsequently published in a journal. Disambiguation of co-authors is based on information within the NRF dataset. We proceed sequentially. In a first step, the scientist filing a paper is identified by name (surname and initials). In a second step, co-author names are matched to a list of 5,255 scientists rated by NRF between 1984 and 2017. In a third step, remaining names are matched to non-rated NRF users. These 27 Because there is no unique paper identifier available, we do not know the exact number of unique papers. Titles, journal names, etc. in the data are strings entered by the applicants in various ways and hence cannot serve as identifiers without further cleaning.
Finally, for all author names not found in the NRF dataset, a one-to-one correspondence between author name and individual is assumed. True, this final decision may conflate two individuals with the same name. We checked on the whole sample that this issue is not too severe. In addition, we construct networks only within disciplines such that different individuals from different disciplines are not conflated. 29  Our disambiguation procedure leads to the following numbers: the 235,712 peer-reviewed journal articles give rise to 866,024 author name-paper relations ('author-papers'). Around 25% of author papers are associated with the postulating rated scientist, 15% can be attributed to nonpostulating rated scientists, 17% are found among non-rated scientists registered in the NRF system, and for the remaining 41% we take the name as the individual.
At the right it can be seen a zoom of the Spanish East coast displaying the difference between the predicted (gray lines) and the postoccultation shadow path (with the same colour code as in the left panel), which results into 10 mas. to-noise ratio (S/N) for Bienor was ∼ 60 and 63 for the first and second runs, respectively. We carried out astrometric measurements using our own codes written in Python that select the different stars within the images and compare them with the Gaia DR2 catalog  (Gaia Collaboration et al. 2018) , obtaining the astrometric position of the target related to the stars. This astrometric position is then used to update the prediction of the stellar occultation by using the methodology explained in  Ortiz et al. (2020b). We obtained astrometric data with a precision of 9 milliarcseconds (mas) in both runs, which includes the plate solution and the centroid uncertainty.
Data reduction and results from the stellar occultation Images were bias and flat-field calibrated using standard procedures as described in Section 2. In the case of the 40-cm telescope at La Hita and Constancia, the observations were recorded in video format. We converted the video into FITS prior to the analysis using Tangra v3. 6. 18 2. We performed synthetic aperture photometry in order to obtain the flux of the occulted star blended with the centaur relative to a set of reference stars. We used our own routines written in IDL, which are based on daophot package  (Stetson 1987 ). These routines are specifically designed to measure the flux from the combination of the occulted star plus the small body. In contrast to other software routines, that obtain the centroid within the aperture to extract the flux, our routine places the centroid at a fixed position relative to the reference stars calculating the photo-center of the star prior to the occultation, thus avoiding errors due to the wandering of the photo-center when the star is occulted and the signal drops to nearly zero.
Rotational light curve: Observations, data reduction, and results In order to determine Bienor's rotational phase at the time of the stellar occultation, we carried out an observational campaign during the second semester of 2019 with the goal of obtaining its rotational light curve. We observed Bienor during seven different nights using two telescopes as described below. On September 23 and 24, we used the 1. 23-m telescope of Calar Alto  Observatory, in Almería (Spain). This telescope has a 4k×4k CCD with a FoV of 21. 5 × 21. 5 and a pixel scale of 0. 315 /pixel. Thanks to the large FoV, we were able to aim the telescope at the same coordinates during both nights. This is convenient as it allowed us to use the same set of stars to perform relative photometry, avoiding systematic errors associated with absolute photometric techniques.
The average seeing in this run was 1. 86. Additionally, two runs were carried out with the 1. 5-m telescope at Sierra Nevada Observatory, in Granada (Spain). The first run was on October 3, 24, and 25, and the second run was on November 24, and 25. This telescope has a 2k × 2k CCD with a FoV of 7. 92 × 7. 92 and an pixel scale of 0. 232 /pixel. This FoV is not large enough to aim the telescope at the same coordinates over the whole run. However, the duration of the observations each night was long enough to cover more than 50%   01:03:58. 2 ± 0. 7 01:04:04. 9 ± 0  of the rotational light curve most of the nights (i. e. , between 2 and 5. 1 hours per night), which allows adding an offset to put all data at the same level. The average seeing of each run was 1. 83 and 2. 18 , for the first and second run, respectively.
All runs were executed using binning 2×2, and without filter, in order to maximize the S/N. We took bias and flat-field frames at the beginning of each observation night. We calculated a median bias that was subtracted to the flat-field frames; after bias subtraction, a normalized-median flat-field was also calculated (these procedures were carried out using the software AstroIm-ageJ;  Collins et al. 2017). Then, we subtracted the median bias and divided by the normalized-median flat-field each science image using specific routines written in Interactive Data Language (IDL). These routines also contain the specific code to perform the synthetic aperture photometry. We applied the same photometric techniques as in  Fernández-Valenzuela et al. (2016). All the stars used in the photometry were brighter than Bienor so that the main source of errors comes from Bienor's flux.
In order to maximize the quality of the photometry, we studied the data dispersion versus aperture radii for each night, selecting those radii that minimize the dispersion. Before calculating the rotational phase at the time of the occultation, we refined the rotation period of Bienor by merging these data with those from previous campaigns taken in 2014 and 2015. Results from these campaigns were previously published in  Fernández-Valenzuela et al. (2017) , where the relative photometric measurements are available online. All data were corrected from light-travel time. We used two different techniques to search for the rotation period in unevenly sampled data. The first one is the Lomb technique (Lomb 1976), which is a modified version of the Fourier spectra analysis as implemented in  Press et al.
g. ,  Vara-Lubiano et al. 2022; Santos-Sanz et al. 2022). We performed a weighed linear fit to the centers of all the chords, and then shifted all the chords so that their centers would lay on that linear fit. The shifts applied to the chords for this second scenario are included in Table  3. As stated above (see Section 5), Bienor is expected to have a triaxial shape at least to a first approximation. Therefore, we fitted the extremities of the chords to an ellipse. To evaluate the effect of the uncertainties of the ingress and egress times, we used a Monte Carlo method: we repeated the fit 10, 000 times changing the extremities of the chords according to their uncertainties and fitting the best ellipse using the curve_fit function of Python SciPy, which minimizes the sum of the squares of the geometric distances r between the fitted ellipse and the extremities following the direction of the chords: S = i r 2 i , (5) with i = 1, 2,.
The work leading to these results has received funding from the European Research Council under the European Community's H2020 2014-2021 ERC Grant Agreement no. 669416 "Lucky Star". E. F. -V. acknowledges financial support by the Space Research Initiative from State of Florida. P. S. -S. acknowledges financial support by the Spanish grant AYA-RTI2018-098657-J-I00 "LEO-SBNAF" (MCIU/AEI/FEDER, UE). A. P. acknowledges financial support of the Hungarian National Research, Development and Innovation Office (NK-FIH) Grant K-138962. G. B. -. R acknowledges CAPES-PRINT/UNESP Process 88887. 310463/2018-00, Project 88887. 571156/2020-00. M. A. acknowledges financial support from CNPq grants with numbers 427700/2018-3, 310683/2017-3, 473002/2013-2, and FAPERJ grant n • E-26/111. 488/2013. F. B. -R. acknowledges financial support from CNPq grant n • 314772/2020-0.
J. I. B. C. acknowledges financial support from CNPq grants with numbers 308150/2016-3 and 305917/2019-6. R. V. -M. acknowledges financial support from CNPq grants with numbers 304544/2017-5, and 401903/2016-8. B. M. acknowledges financial support from CNPq grant n • 150612/2020-6. A. R. -G. -J. acknowledges financial support from FAPESP grant n • 2018/11239-8. J. M. T. -R. research was supported by the research Grant No. PGC2018-097374-B-I00, which is funded by FEDER/Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación.
Acknowledgements. The work leading to these results has received funding from the European Research Council under the European Community's H2020 2014-2021 ERC Grant Agreement no. 669416 "Lucky Star". E. F. -V. acknowledges financial support by the Space Research Initiative from State of Florida. P. S. -S. acknowledges financial support by the Spanish grant AYA-RTI2018-098657-J-I00 "LEO-SBNAF" (MCIU/AEI/FEDER, UE). A. P. acknowledges financial support of the Hungarian National Research, Development and Innovation Office (NK-FIH) Grant K-138962. G. B. -. R acknowledges CAPES-PRINT/UNESP Process 88887. 310463/2018-00, Project 88887. 571156/2020-00. M. A. acknowledges financial support from CNPq grants with numbers 427700/2018-3, 310683/2017-3, 473002/2013-2, and FAPERJ grant n • E-26/111. 488/2013.
F. B. -R. acknowledges financial support from CNPq grant n • 314772/2020-0. J. I. B. C. acknowledges financial support from CNPq grants with numbers 308150/2016-3 and 305917/2019-6. R. V. -M. acknowledges financial support from CNPq grants with numbers 304544/2017-5, and 401903/2016-8. B. M. acknowledges financial support from CNPq grant n • 150612/2020-6. A. R. -G. -J. acknowledges financial support from FAPESP grant n • 2018/11239-8. J. M. T. -R. research was supported by the research Grant No. PGC2018-097374-B-I00, which is funded by FEDER/Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación.
For this purpose we selected the sample of the Large Programme DSHARP  (Andrews et al. 2018 ) , which consists of twenty discs imaged at 0. 05' resolution, since it constitutes the largest homogeneous high-resolution surv e y of protoplanetary discs. We aim to determine in which cases this technique is successful in gauging the disc thickness, and, whenever possible, to place meaningful constraints on the disc scale heights. This paper is structured as follows: in Section 2 , we discuss the basic assumptions of our model and describe the steps of the analysis we perform to match DSHARP data. A description of the data sample is presented in Section 3. Section 4 presents the main results of the analysis, while Section 5 contains a discussion on the implications of our findings. Conclusions are given in Section 6.