dataesr/scientific-paragraphs-categorization huggingface dataset:
Individual exposures are reduced and stacked by the HFF team directly, using the standard Pyraf/STSDAS pipeline. Additionally, the ACS mosaics are further corrected with a "self-calibration" technique to eliminate Charge Transfer Inefficiency (CTI) effects and low-level pixel noise 3  (J. Anderson, in prep). The full reduction procedure is described in the A370 HFF data archive 4. After combining all exposures, total integration times are  51400, 25316, 130643, 2335, 12894, and 9647  seconds for the F435W, F606W, F814W, F105W, F140W, and F160W bands, respectively. This is equivalent to 20, 10, 52, 1, 5, and 4 HST orbits. Collectively, this represents one of the deepest, highest resolution imaging of A370 ever taken, and corresponds to average limiting magnitudes of 29. 3 in the optical bands and 27.
MUSE Spectroscopy MUSE observations of A370 were taken on UT 2014 November 20, as part of the Guaranteed Time Observing (GTO) Program 094. A-0115(A) (PI:Richard). In total, we observed four 30-minute exposures in WFM-NOAO-N mode, centered at (α = 2 h 39 m 53. s 111, δ = -1 • 34 ′ 55. ′′ 77). We applied a small (∼ 0. 5 ′′ ) dither pattern between exposures to average out systematics on the detector, and we alternated taking exposures between PA = 0 • and PA = 8 • to reduce the systematic striping pattern caused by the IFU image slicers. The full observational footprint can be seen in Figure  1. Typical seeing during the observations was 0. 75 ′′ , as measured by stars in the field. The raw data were reduced through the MUSE pipeline  (Weilbacher et al. 2012 (Weilbacher et al. , 2014) )  provided by ESO (version 1.
REDSHIFT MEASUREMENT AND SPECTROSCOPIC CATALOG After reducing the data, we probe the MUSE cube for redshifts using two complementary techniques: 1. ) an automated emission line detection program, and 2. ) a customized, interactive data visualization tool designed to extract MUSE aperture spectra by hand. The automated program, MUSELET 5  , is included as part of the Muse Python Data Analysis Framework (MPDAF 6  ) version 2. 0. It first creates narrow-band images of the data, averaging the flux over a narrow emission wavelength range and then subtracting a local continuum. For the A370 cube, we choose an emission window of 6. 25 Å (spanning five MUSE wavelength slices), with each window centered on one of the original wavelength planes. The continuum is created by averaging two 25 Å slices, immediately blue-and red-ward of the emission window.
The tool, a custom-made Python script, collapses the data cube along the wavelength axis, creating a "white-light" image of the field. A second panel, matched to the WCS of the cube, shows a corresponding HST image to help identify the target galaxy. Once a galaxy has been identified, a user can draw a (circular or rectangular) aperture around the target object, which will extract both a 2D and 1D spectrum from the cube. From these spectra, the user can then interactively fit a redshift to the galaxy, using the same set of lines as the automatic line-finder. An example of this process can be seen in Figure  2. As an additional check, we run AutoZ  (Baldry et al. 2014 ) on the extracted 1D spectrum. In all cases, we find the difference between the manual and AutoZ fits to be less than δz = 0.
In some cases, the best-fit model predicts additional counterimages for these objects  (Images 8. 3, 11. 3, and 13. 3 ), but we again find no obvious counterparts in the HFF data, so we do not use them as model constraints. This is because predicted positions without redshift have significantly larger uncertainties. However, this will be revisited in future work with a larger MUSE mosaic. After adding and removing objects as described above, we are left with 21 unique systems with a total of 66 individual image constraints. To optimize the model we feed all parameters and constraints into the LENSTOOL 7   (Kneib et al. 1996; Jullo et al. 2007; Jullo & Kneib 2009 ) software program, which probes the full parameter space with a Bayesian Markov Chain Monte Carlo (MCMC) sampler. To evaluate a given model, LENSTOOL reconstructs the parametrized mass distribution and transforms the constraint coordinates from the image (observed) plane to the source (undeflected) plane.
ACKNOWLEDGMENTS DJL, JR, BC, GM, VP and JM acknowledge support from the  ERC  starting grant CALENDS. This work has been carried out thanks to the support of the  ANR FOGHAR  ( ANR-13-BS05-0010-02 ) and the  OCEVU Labex  ( ANR-11-LABX-0060 ). LW acknowledges support by the  Competitive Fund of the Leibniz Association  through grant  SAW-2015-AIP-2.
The Static User Equilibrium is a powerful framework for the theoretical study of traffic. Despite the restricting assumption of stationary flows that intuitively limit its application to real traffic systems, many operational models implementing it are still used without an empirical validation of the existence of the equilibrium. We investigate its existence on a traffic dataset of three months for the region of Paris, FR. The implementation of an application for interactive spatio-temporal data exploration allows to hypothesize a high spatial and temporal heterogeneity, and to guide further quantitative work. The assumption of locally stationary flows is invalidated in a first approximation by empirical results, as shown by a strong spatial and temporal variability in shortest paths and in network topological measures such as betweenness centrality.
An example of empirical investigation of classical assumptions is given in  Zhu and Levinson (2010) , in which revealed route choices are studied. Their conclusions question "Wardrops first principle" implying that users choose among a well-known set of alternatives. In the same spirit, we investigate the possible existence of the equilibrium in practice. More precisely, SUE assumes a stationary distribution of flows over the whole network. This assumption stays valid in the case of local stationarity, as soon as time scale for parameter evolution is considerably greater than typical time scales for travel. The second case which is more plausible and furthermore compatible with dynamical theoretical frameworks, is here tested empirically. The rest of the paper is organized as follows : data collection procedure and dataset are described ; we present then an interactive application for the interactive exploration of the dataset aimed to give intuitive insights into data patterns ; we present then results of various quantitative analyses that give convergent evidence for the non-stationarity of traffic flows ; we finally discuss implications of these results and possible developments.
Dataset Construction We propose to work on the case study of Parisian Metropolitan Region. An open dataset was constructed for highway links within the region, collecting public real-time open data for travel times (available at www. sytadin. fr). As stated by  Bouteiller and Berjoan (2013) , the availability of open datasets for transportation is far to be the rule, and we contribute thus to a data opening by the construction of our dataset. Our data collection procedure consists in the following simple steps, executed each two minutes by a python script : • fetch raw webpage giving traffic information • parse html code to retrieve traffic links id and their corresponding travel time • insert all links in a sqlite database with the current timestamp. The automatized data collection script continues to enrich the database as time passes, allowing future extensions of this work on a larger dataset and a potential reuse by scientists or planners.
Data Summary A time granularity of 2 minutes was obtained for a three months period (February 2016 to April 2016 included). Spatial granularity is in average 10km, as travel times are provided for major links. The dataset contains 101 links. Raw data we use is effective travel time, from which we can construct travel speed and relative travel speed, defined as the ratio between optimal travel time (travel time without congestion, taken as minimal travel times on all time steps) and effective travel time. Congestion is constructed by inversion of a simple BPR function with exponent 1, i. e. we take c i = 1 -t i,min t i with t i travel time in link i and t i,min minimal travel time.
Such a variability is indeed observed on a non-negligible number of paths on each day of the dataset. We show in Figure  2  an example of extreme spatial variation of shortest path for a particular Origin-Destination pair. The systematic exploration of travel time variability across the whole dataset, and associated travel distance, confirms, as described in Figure  3 , that travel time absolute variability has often high values of its maximum across OD pairs, up to 25 minutes with a temporal local mean around 10min. Corresponding spatial variability produces detours up to 35km.
This index is more relevant to our purpose than other measures of centrality such as closeness centrality that does not include potential congestion as betweenness centrality does. Fig. 4. Temporal stability of maximal betweenness centrality. We plot in time the normalized derivative of maximal betweenness centrality, that expresses its relative variations at each time step. The maximal value up to 25% correspond to very strong network disruption on the concerned link, as it means that at least this proportion of travelers assumed to take this link in previous conditions should take a totally different path. We show in Figure  4  the relative absolute variation of maximal betweenness centrality for the same time window than previous empirical indicators. More precisely we plot the value of ∆b(t) = |max i (b i (t + ∆t)) -max i (b i (t))| max i (b i (t)) (2) where ∆t is the time step of the dataset (the smallest time window on which we can capture variability).
Theoretical and practical implications of empirical conclusions We argue that the theoretical implications of our empirical findings do not imply in a total discarding of the Static User Equilibrium framework, but unveil more a need of stronger connections between theoretical literature and empirical studies. If each newly introduced theoretical framework is generally tested on one on more case study, there are no systematic comparisons of each on large and different datasets and on various objectives (prediction of traffic, reproduction of stylized facts, etc. ) as systematic reviews are the rule in therapeutic evaluation for example. This imply however broader data and model sharing practices than the current ones. The precise knowledge of application potentialities for a given framework may induce unexpected developments such as its integration into larger models.
Towards explanative interpretations of non-stationarity An assumption we formulate regarding the origin of non-stationarity of network flows, in view of data exploration and quantitative analysis of the database, is that the network is at least half of the time highly congested and in a critical state. The off-peak hours are the larger potential time windows of spatial and temporal stationarity, but consist in less Fig. 5. Spatial auto-correlations for relative travel speed on two weeks. We plot for varying value of decay parameter (1,10km) values of autocorrelation index in time. Intermediate values of decay parameter yield a rather continuous deformation between the two curves. Points are smoothed with a 2h span to ease reading. Vertical dotted lines correspond to midnight each day. Purple curve is relative speed fitted at scale to have a correspondence between auto-correlation variations and peak hours.
g. Tordeux and Lassarre (2016)  that study the stability of speed regulation models at the microscopic scale to avoid traffic jams. Other research directions may consist in the test of other assumptions of static user equilibrium (as the rational shortest path choice, which would be however difficult to test on such an aggregated dataset, implying the use of simulation models calibrated and cross-validated on the dataset to compare assumptions, without necessarily a direct clear validation or invalidation of the assumption), or the empirical computation of parameters in stochastic or dynamical user equilibrium frameworks.
Conclusion We have described an empirical study aimed at a simple but from our point of view necessary investigation of the existence of the static user equilibrium, more precisely of its stationarity in space and time on a metropolitan highway network. We constructed by data collection a traffic congestion dataset for the highway network of Greater Paris on 3 months with two minutes temporal granularity. The interactive exploration of the dataset with a web application allowing spatio-temporal data visualization helped to guide quantitative studies. Spatio-temporal variability of shortest paths and of network topology, in particular betweenness centrality, revealed that stationarity assumptions do not hold in general, what was confirmed by the study of spatial autocorrelation of network congestion.
It consists of a card index in which each of the many thousands of cards contains provenance information for a single Phillipps manuscript. This index was discontinued some years ago, and there are no plans to publish it or to make it available in digital form. The Institut de recherche et d'histoire des textes in Paris also has an unpublished list of current locations for Phillipps manuscripts, compiled by Edith Brayer. These resources have obvious limitations: they are available only in a single physical location, they are no longer being updated, and none of them is comprehensive, though their content overlaps. More seriously, the evidence is in a form where it cannot readily be analyzed or used for any but the most specific research questions. The potential for using new digital approaches to overcome these limitations has been the focus of a project funded by the European Union under its Marie Curie Fellowship scheme between 2014 and 2016.
Sources of Data The annotated printed catalogs and card indexes already mentioned are only one of the numerous sources of data relating to the history of the Phillipps manuscripts. These are summarized in Table  1. The most important digital source, which focuses on provenance events, is the Schoenberg Database of Manuscripts, developed and managed by the Schoenberg Institute for Manuscript Studies in the University of Pennsylvania Library: https://sdbm. library. upenn. edu/. It contains something like twenty thousand entries relating to about six thousand of the Phillipps manuscripts. Most of its data consist of lot descriptions from sale and auction catalogs, which are each given individual records in the database. In the first iteration of the database, entries relating to the same manuscript could be manually linked, but there was no overarching "master record" for a manuscript.
A new data model, currently in beta testing, does create such a record, to which each of the records that identifies provenance events can be linked. 4 Apart from a specialist source like the Schoenberg Database of Manuscripts, the main sources of evidence are the many catalogs of individual institutions and some aggregated products derived from them, such as Digital Scriptorium, a North American consortial database with records from forty contributing partners: http:// bancroft. berkeley. edu/digitalscriptorium /. Those libraries and museums with large manuscript holdings-and sufficient funds-have usually produced a printed catalog of their holdings. 5 These catalogs are normally very detailed, accurate, and thorough, though their coverage may be increasingly out of date. In the digital environment, institutional cataloging practices vary greatly.
Some institutions have transferred all the information from their printed manuscript catalogs or card indexes into their online catalogs; others still maintain separate manuscript databases or finding aids. Some-especially art museums-do not make their full collections database available over the web and only offer selected digital highlights of their collection. Where manuscripts are described in catalogs and databases, the treatment of provenance can be inconsistent. Some catalogs have no provenance information at all, while other institutions provide detailed and thorough provenance histories. These are often located in a "notes" field, however, which may not be searchable. Only a few institutions provide a specific access point for former owners like Sir Thomas Phillipps, thereby enabling all former Phillipps manuscripts to be identified through an "author" search.
Saving and exporting catalog records can also be difficult, even with the most thorough and comprehensive online catalogs. For most library databases, relevant records can be exported only individually or in small batches, and often in a limited range of bibliographic formats, which may not include the crucial provenance notes. While this may partly reflect the limitations and inconsistencies endemic in manuscript cataloging practices, the main reasons are the limitations of the specific brand of software involved and the choices made in configuring that software for use. The Schoenberg Database of Manuscripts, in contrast, provides all its data as downloadable Excel or CSV files. These can then be analyzed, filtered, and imported into other environments. Other sources of evidence include printed and handwritten lists of the Phillipps manuscripts, but using these to construct a master list against which to check institutional holdings is not a straightforward task.
8 his narrative approach to recording manuscript provenance is reflected in the Text Encoding Initiative (TEI). The TEI's <msDesc> (Manuscript Description) section contains a <history> element for encoding information from a catalog entry about the life cycle of a manuscript. 9 Within this, the <acquisition> and <provenance> elements are similar to the note fields in a MARC record. The problem with these approaches is that the provenance information is too loosely structured for computational purposes. The museum sector, which also has a strong interest in provenance (particularly in relation to authentication), is grappling with the same problem. In museum databases, provenance information for an individual object is usually given in the form of a chronological listing of owners. The American Association of Museums, in the AAM Guide to Provenance Research, provides a guideline for structuring these notes.
S44 The History and Provenance of Manuscripts Speculum 92/S1 (October 2017) complex model, but its focus on events is very relevant to the problem of representing manuscript provenance. Christian-Emil Ore has examined how manuscript provenance can be represented in CIDOC-CRM, using the Kringla Leaf as his example. 14 hile this produces a dauntingly complicated series of diagrams, it helps to identify various types of provenance events that can serve as the basis for a new data model. The data model developed for the Phillipps Collection project draws together elements from CIDOC-CRM's event-based ontology, the Carnegie Museum's chronological framework for transfers of ownership, and the evidence-based approach of the Schoenberg Database of Manuscripts. It is very much a work in progress, sufficient to meet the initial requirements of that project; it does not aim to be exhaustive, but is designed to be hospitable to further expansion and elaboration.
A Digital Environment for Provenance Research Once a data model has been developed, it needs to be implemented in a suitable software environment. For the Phillipps Collection project, this implementation has been carried out using nodegoat. 16 Developed in the Netherlands by the Lab1100 group, nodegoat is described as a web-based data management, network analysis, and visualization environment. It was chosen for this project for its flexibility in data modeling, its ability to import data through CSV files, and its capacity to produce geographical and network visualizations of the aggregated data. The data structure used in nodegoat consists of objects with subobjects attached to them. These subobjects can embed relationships to other objects, as well as containing properties like time and place.
The generic data model for manuscript provenance described above was mapped onto this framework. A "manuscript" object can have subobjects for transactions, such as "sold" and "owned," and these subobjects can contain links to the "persons" and "organizations" participating in each transaction, as well as the times and places where the transaction occurred. Table  3  shows the basic form of the data model as mapped to the nodegoat structure. The subobjects attached to each manuscript provide a summary of the known events in its provenance history. Fig. 1  shows a typical nodegoat record, for Phillipps manuscript 12264. The "object" record, which contains a physical description of the manuscript, is accompanied by a series of "sub-objects" that show events in its history: "produced," "owned," and "sold.
The History and Provenance of Manuscripts The vocabularies used for specific elements within the nodegoat database are partly imposed by nodegoat itself and partly chosen by the creator of the database. The Geonames ontology for place names (http://www. geonames. org/ontology /documentation. html) is incorporated into nodegoat; it makes geographical visualizations of the data relatively easy, but also causes some problems by recognizing only contemporary countries and cities. Personal names in nodegoat can be matched against DBPedia (http://wiki. dbpedia. org/) and the Virtual International Authority File (VIAF, https://viaf. org/). Other types of classifications can be created manually, including languages, currencies, and materials. The vocabulary used to describe subobjects can be derived from CIDOC-CRM properties, with some additions.
The major gap is the lack of any standard method for identifying individual manuscripts, other than their Phillipps numbers or their current institutional shelf mark; for the purposes of the project, Phillipps numbers have been used as identifiers and as the basis for creating Uniform Resource Identifiers (URIs). Data can be entered directly into nodegoat using web forms created from the data model designed by the user. But it is generally more efficient to import data in bulk, in the form of CSV (Comma Separated Values) files-simple text files used to store tabular data, which can be viewed and edited in standard spreadsheet software like  Excel. These files can be either keyed in or derived from library catalogs or other database exports. The Schoenberg Database is exemplary in making its data available for download as a CSV file, which can then be mapped to the nodegoat data model.
An "import template" must be created for each unique CSV format by mapping the CSV columns to objects and subobjects in nodegoat. The import routine consists of loading the CSV file to nodegoat, choosing the appropriate import template, and running the import process. Various parameters can be set for this process, including whether to create new objects (such as manuscript records) automatically as part of the data load or to hold each object for manual review against possible matches. Fig. 3  shows the import template for a CSV file derived from the Bodleian Library's list of numbered Phillipps manuscripts in the Phillipps-Robinson collection.
The History and Provenance of Manuscripts The major difficulties in the import process are likely to arise from mapping the source data to the nodegoat data model. This is especially so when multiple pieces of information are conflated into a single column in the CSV file, or into a single field in the source database. A person's dates of birth and death may be included in the same column as his or her name, for example, or the names of multiple previous owners may be included in a single provenance field. Alternatively, a field may be given in a narrative or note format containing multiple pieces of information. In these cases, the source data are likely to require extensive editing with a tool like OpenRefine. 17.