dataesr/scientific-paragraphs-categorization huggingface dataset:
The CARP dataset compliance with such standards should be more extensively checked, whichwould help interoperability  [38] , in particular if considering other European past railway datasets. For instance, Linear referencing (km attribute), and Network interconnections ( =gares-same node "tolerance"), are explicit terms of INSPIRE Transport Networks. Moreover, adding time constraints, as happens with a historical railway network, should also be treated more carefully: so far, only an "end" (validTo) date is provided, when a gare is no longer active. We indirectly use the standardization efforts of the UIC (Section 2. 4): today railway ontology in Wikipedia benefits from it for keepinghomogeneity in representation, visualization and associated tools (e. g. , Infobox/BS-tables). However, toponymy is much less standardized: changes in names and boundaries of administrative objects is accelerating in France, without real retrospective help for data reconstruction (e.
g. , simple list of communes merging, see Section 2. 3). Overall, interoperability should be improved with a better compliance with INSPIRE, especially if a European collaboration is foresighted. Data visualization: has been limited to the use of geojson, which can be displayed on line, by many websites, and which is easily read as an open source. Examples are displayed with OpenLayers, on top of OpenStreetMap tiles. Issues in the development of tools: the code is in JavaScript, well suited to direct interaction with the web, with web-workers (what allows some parallelism if the computer supports this), but the maximum number of actually possible Internet requests is rapidly reached on a personal computer. Any perspective of development would imply working on a stronger architecture more adapted to Big Data.
Data Quantity; Computation Intensivity These two issues are met all along this work: we are working with about 1000 lignes 10,000 gares, 35,000 communes and a gare-commune join requires 350 M possibly complex tests. For instance, when seeking for insee codes of the gares, the process (point-in-polygon) should, and can, be parallelized for 90% of data, because most gares of a same ligne are in the same area (5 or 6 large parts of France), excepting a few very long lignes (e. g. , Paris-Nice). The technology of web-workers has been used successfully on a personal computer with 4 processors. In the Wikipedia realm, it is rather easy to obtain gares from a ligne, then posting asynchronous queries about coordinates of these gares, which is computationally difficult beyond a dozen concurrent gares: see above comment about better Big Data access (Section 5.
1. 1). In the crowd of VGI, where the most sophisticated structure isoften a list of names, the rule is to work "schemaless", as proposed in  [39] , possibly after a text analysis looking for words ligne and gare (in French, or other language of course), and a "text pattern" identifying a list, then deriving for instance a ligne-gare relationship. Important work is yet to be done in the future, in particular if aiming at handling more European countries. Most standard quality metadata components are concerned and have been used, and a posteriori quality control is largely automated by the systematic constraint checking. Version control should be better taken into account, in order to track: the evolution of the CARP dataset: a Git-type solution would improve it; -the lineage of data used in setting attribute values.
Some retrospective information could be retrieved, partially, for that lineage mixes software and non-reproducible manual steps. 5. 1. 4. Data Availability; Data Access; Security These issues are not really addressed in this work. We are considering sources only from public datasets (excepting an author copy of a dataset that is no longer on-line), or Internet accessible open data, and the outcome is archived on a public portal. Together with version control, these aspects should be given more attention.
A Dataset for Further Social Sciences Studies The CARP resulting dataset is open, accessible on a public website, and is intended as a tool, for geographers and transportation specialists to further investigate how long-term trends form and evolve. The present version (January 2021) is about 80% complete (?), but compared to the previous public datasets, already shows an increase in all total counts: nodes: +56%, lignes ( =num): +30%, communes ( =insee): +39%. One typical question used as an example of the opportunity to reconstruct this past network, was: Does a commune possess a gare? Today? A century ago? The CARP dataset can list, and count the number of such gares (Table  17 ). To date, 20% of communes, have been checked as having been connected to the railway network, at least by a simple stop for some time between 1920 and now.
This is already significant material for demographic, socio-economicand even electoral studies. Almost 4000 additional nodes have been geocoded and quality checked. Also in CARP: every national border crossing with neighbor countries (68 such places), showing how European rail liaisons, from or to France have evolved since WWII (Figure  25 ). This CARP dataset, can also confirm broadly to some local patterns mentioned in the literature  [2] [3] 29, 40] , for instance the lignes that have been closed just before WWII in Region Bretagne, or in the late 1990s. These observations are intended to be studied further in collaboration with transport geographers, including those at a European level. Already cited papers  [2, 7]  demonstrate that there is an interest in developing such research in Europe.
In particular, surprisingly, some French gares are still in operation only because they are served by foreign trains from Luxembourg (Audun-le-Tiche, Volmerange), or Germany (SaarBahn to Sarreguemines) or Switzerland (Line 10 of the Basel tramway). This CARP dataset, can also confirm broadly to some local patterns mentioned in the literature  [2, 3, 29, 40] , for instance the lignes that have been closed just before WWII in Region Bretagne, or in the late 1990s. These observations are intended to be studied further in collaboration with transport geographers, including those at a European level. Already cited papers  [2, 7]  demonstrate that there is an interest in developing such research in Europe.
Further action is needed particularly in road transport, the highest contributor to transport emissions, as well as aviation and shipping …", whichwe can read as: rail is the only transportation mode able to significantly decrease CO2 and greenhouse gas (GHG) emissions. The first sentence of the EU report's executive summary states: " The fact that only limited data are available and that the impacts of most of the initiatives cannot yet be observed do not allow the proper assessment of the effectiveness of the measures In Belgium, some lignes, near the border with France, have been processed directly with wikiInfoBox. , e. g. , https://fr. wikipedia. org/wiki/Ligne_73_(Infrabel). In Germany routemap tables are used instead, instead of BS-tables, e. g. ,: https://de. wikipedia. org/wiki/Stuttgart_Hauptbahnhof, uses Infobox_Bahnhof a similar template, whose Strecken entry (=list of lignes), has a different syntax.
Further action is needed particularly in road transport, the highest contributor to transport emissions, as well as aviation and shipping. ", whichwe can read as: rail is the only transportation mode able to significantly decrease CO 2 and greenhouse gas (GHG) emissions. The first sentence of the EU report's executive summary states: " The fact that only limited data are available and that the impacts of most of the initiatives cannot yet be observed do not allow the proper assessment of the effectiveness of the measures adopted so far and their contribution to reaching the goals ". Therefore, better long-term data could help the EU to better measure the adoption of its recommendations by the member states, and better understand what hurdles exist on the route. const closestNode /* gets: closest node to f (f,old,new) */ const breaks = [ D1, D2, D3, D4, Infinity]; /* thresholds */ const pos = ["AP","PP","und","und","SN"], neg = ["SP","und","und","PN","AN"]; const dd = /* geographic "greatCircle distance" in meters */ const tt = /* starts with "und", then update tag[i] depending on: similar label and rank in breaks[i-1] */ const fusionTags /* returns {fuse: tt; dist: dd; closest: closestNode } */ const garesA = fetch("A_dataset"), garesB = fetch("B_dataset"); garesB.
f[i]. geometry = {"type":"Point", "coordinates":[lon,lat]}; (c) for each node with geometry (Figure 23), check nom and insee, check km strict order check label uniqueness in CARP dataset, check geometry deviation if a gare of the same label exists already. Report control result in info: {f[i]. nom, f[i]. insee} = getCommuneFromPoint(f[i]. geometry?. coordinates); f[i]. info += " (commune_ok | commune_inconsistent) (km_checked) …";.
), check nom and insee, check km strict order, check label uniqueness in CARP dataset, check geometry deviation if a gare of SPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 25 of 38 4. 2. 2. Addition of New Lignesand Gares, from VGI A gare exists only if a ligne exists to which that gare belongs. Most of the 'secondary lignes are not recorded in public datasets, and have no official num code. VGI has recorded many of them. Depending on what can be found, various routines may apply. Figures 22 and 23 sketch the three steps of a possible full software chain:.
4. 2. 2. Addition of New Lignesand Gares, from VGIA gare exists only if a ligne exists to which that gare belongs. Most of the 'secondary lignes are not recorded in public datasets, and have no official num code. VGI has recorded many of them. Depending on what can be found, various routines may apply. Figures22 and 23sketch the three steps of a possible full software chain:.
Acknowledgments: Work initiated with the help of the sophomores 2018 at  IUT-Marne-la-Vallée , and pursued with the encouraging comments from IGN (seminar on patrimonial data), SNCF-Réseau and several VGI contributors. First version of the manuscript submitted in August 2020, extensively rewrote thanks to the acute, thorough and highly relevant comments of two rounds of reviewers, who deserve heartfelt thanks.
num: matches SNCF*|Lignes:code_ligne, primary key for lignes. Mandatory; • Target. label: matches SNCF1:nom, and SNCF2*:libelle; • Target. km: matches SNCF2*:pk. Mandatory. Order must reflect the strict order of the nodes on the ligne; • Target. uic: matches SNCF2*:code_uic. In SNCF1, uses Target. label instead; • couples SNCF1:(code_ligne, nom) and SNCF2:(code_ligne, pk), are used as primary key for nodes what requires to check its uniqueness; • Target. capa: derived from SNCF:nature, SNCF*:fret,voyageurs or Lignes:mnemo, whichever available; • Target. geometry: matches SNCF1:(latitude,longitude), SNCF2*:geometry:point, • Target. insee: present in Insee-geo or Insee-new datasets, is primary key for com- munes, and must be retrieved for a gare, by identifying which commune verifies Point_in_Polygon(geometry(gare), geometry(commune)); • Target.
1 , D 2 ] or [D 3 , D 4 ] respectively:The last fusion step is to make a single "fused" node from a node pair, that is choosing appropriate values for every attribute, e. g. , best coordinates?, or to convert into the attribute schema of the other dataset. ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 12 of 38 2. 4. 3. Fusion Combination, Integration, Revision and Conclusion The fusion-combination of 2014 attributes concerns: - nature: which can be translated into a capa value (though losing some specificity). - geometry: the maximal distance being D2, use 2017 coordinates; - info: add approximation info about geometry (depending on dist). The integration of 2017 nodesis straightforward. The integration of 2014 nodesis more difficult, concerning: - geometry: straightforward, use 2014 coordinates; -nature: translate into a capa value (rather easy); km: seek for the closest 2 nodeson the same ligne, then interpolate a value; uic: build a unique code using numand km; info: add uncertainty info about km and uic.
), check nom and insee, check km strict order, check label uniqueness in CARP dataset, check geometry deviation if a gare of SPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 25 of 38 4. 2. 2. Addition of New Lignesand Gares, from VGI A gare exists only if a ligne exists to which that gare belongs. Most of the 'secondary lignes are not recorded in public datasets, and have no official num code. VGI has recorded many of them. Depending on what can be found, various routines may apply. Figures 22 and 23 sketch the three steps of a possible full software chain: the same label exists already. Report control result in info: {f[i]. nom, f[i]. insee} = getCommuneFromPoint(f[i]. geometry?. coordinates); f[i]. info += " (commune_ok | commune_inconsistent) (km_checked). "; The dataset built at this step is named "CARP-VGI".
However, at present, there are no experimental data on 72 Ge(n, γ ) covering the entire astrophysical energy range. Experimental data on 72 Ge +n reactions include transmission data obtained for natural germanium by Harvey and Hockaday  [14] , providing total cross sections over a wide energy range from 6 eV to 178 keV. Maletski et al. [15]  performed transmission and capture measurements on 72 Ge and identified 14 resonances up to 30 keV neutron energy, but radiative widths γ are only known for three resonances up to 4 keV. Consequently, MACS values used in stellar models are exclusively based on theoretical predictions or evaluations taking into account the experimental information available at lower neutron energies. The latest version of the Karlsruhe Astrophysical Database KADoNiS-v1.
The detectors have been specially optimized for neutron capture measurements to achieve a low sensitivity to background from neutrons scattered into the detector  [30, 31]. The detectors were installed 7. 7 cm upstream from the sample resulting in a position corresponding to an effective angle of 125 • relative to the neutron beam. The capture sample, with a mass of 2. 68 g, consisted of 96. 59% isotopically enriched 72 GeO 2 powder, which was pressed into a cylindrical pellet of 2 cm diameter. In addition, data were taken with a metallic Ge sample of natural composition which was used to identify resonances due to other Ge isotopes present in the sample, and an Au sample which was used to normalize the capture yield (both cylindrical with 2 cm diameter). All samples were glued on to a sample holder with 6 μm Mylar backing.
They are placed into the neutron beam upstream of the capture sample and are chosen to be thick enough to block out all neutrons at certain resonance energies. Any counts measured in the filter dips consequently come from background of scattered neutrons. The background was determined from the filter dips due to strong resonances in Al around 35, 90, and 160 keV neutron energy and is at the level of 5-15 % compared to the sample spectrum in the region from 43 to 300 keV. The yield in the resolved resonance region was analyzed with the multilevel, multichannel R-matrix software SAMMY  [39]. SAMMY fits resonance shapes taking into account all experimental effects such as resolution broadening, selfshielding, multiple interactions, and sample impurities (e. g. , other Ge isotopes). Resonances due to impurities are identified by comparing the capture yield of the enriched 72 Ge sample with the spectrum recorded with germanium of natural isotopic composition (background due to oxygen is negligible due to small neutron cross sections).
Resonances were fitted using the Reich-Moore approximation and assuming a constant background. Capture data do not usually allow one to reliably determine all individual resonance parameters (gamma and neutron widths, energy, spin, and parity; neutron   (13)  238  (7)  orbital momentum). Thus we report in Tables I and II only well determined observables for each resonance: resonance energy and capture kernel k, defined as k = g γ n γ + n , with g = 2J + 1 (2I + 1)(2s + 1) , (2) with J, I, and s being the resonance, target and neutron spin, respectively. In total, 93 resonance kernels were determined, with 77 resonances not listed in any database. Figure  2  shows examples of SAMMY fits in the keV neutron energy region. Systematic uncertainties of capture kernels (not included in uncertainties in Tables  I  and II ) are due to the neutron flux (2% below 10 keV and 5% between 10 and 43 keV), the sample enrichment (1%), the normalization (1%), and the pulse height weighting technique (2%)  [35] , resulting in total systematic uncertainties of 3.
Assuming that the kernel for resonances below 20 keV with J = 1/2 cannot be higher than 300 meV, i. e. , γ γ + 2. 5σ γ , we observe four strong (with n γ ) p-wave resonances with J = 3/2. Their presence imposes a limit of S 1 0. 7 × 10 -4 , the most probable S 1 value being about two times higher. Using these neutron strength functions we arrived at an s-wave resonance spacing of D 0 = 1800(300) eV. The available literature values of 1190(290)  [41]  and 2070(290) eV  [42]  are inconsistent due to the lack of experimental data. Examples of resonance fits of the neutron capture yield using the R-matrix code SAMMY  [39]. The data are compared to the yield calculated from resonance parameters listed in the ENDF/B-VIII. 0 evaluation  [33]. The bottom panel shows an example of resonances measured at n_TOF for the first time.
9-9. 5 %), multiple interaction and self-shielding corrections (1. 3%), and sample impurity corrections (1. 3%), in addition to the 5. 6% or 3. 2% systematic uncertainty also applying to the capture kernel (see above). Our data are on average 15-17 % systematically lower than the ENDF ones, e. g. , 16% on average from 200 to 300 keV. Likewise, TENDL overestimated the cross section by 27% over the whole URR neutron energy range, on average by about 29% from 50 to 100 keV, and up to 25% from 200 to 300 keV. Tabulated data will be provided to the IAEA EXFOR database  [45]. FIG. 3. Averaged cross sections with statistical uncertainties from 43 to 300 keV neutron energy compared to ENDF/B-VIII. 0 [33]  and TENDL-2017  [17, 44]. See text for details.
ACKNOWLEDGMENTS This work was supported by the  Adolf Messer Foundation , the  Austrian Science Fund FWF  ( J 3503 ), the  Science and Technology Facilities Council UK  ( ST/M006085/1 ), the  European Research Council   ERC-2015-STG No. 677497 , the  Croatian Science Foundation  under the project  8570 , the  MSMT of the Czech Republic , the  Charles University   UNCE/SCI/013  project, and by the funding agencies of the participating institutes.
(e) Statistical analysis We assessed whether the probability of bats flying inside the forest (PFIF) differed according to the distance to the light, and whether this relationship differed between spectra (i. e. dark control, red and white lights). The relationship between the PFIF and the distance to the light allowed us to define the Flight-Path Switch Distance (FPSD) as the distance at which bats on average flew as much inside as outside the forest. We performed Generalized Linear Mixed Models (GLMM, R package TMB), using the PFIF as a binomial response variable where zero corresponded to positions located in the open habitat, and where one corresponded to positions located inside the forest (Fig. 1 ). We used as explanatory variables the distance to the light, the spectrum type, and the interaction between them to assess the effect of spectra on FPSD.
x < 4 m) (Fig. 1 ). All GLMMs exhibited much smaller Akaike Information Criteria (AIC) than null models. We assessed for each model the goodness of fit by computing the percentage of variance explained by models using the r2 function (R package sjstats). We checked residual plots of models using the R package DHARMa. All analyses were performed using a significance threshold of 5% in the R statistical software  [36].
Acknowledgments We thank  Staatsbosbeheer, Natuurmonumenten , the  Dutch Ministry of Defence, Het Drentse Landschap and the municipality of Ede  for the use of their terrain. We are grateful towards  Julie Pauwels  and  Charlotte Roemer  which recently help to improve R script allowing to reconstruct 3D bat positions. We also thank  Ray van Dijk  for his help with the nightly field work.
Data sharing offers the opportunity to further increase the statistical power of longitudinal NHP studies by increasing the cohort size. The NHP neuroscience community has recently started to share neuroimaging datasets  (Milham, 2018 (Milham, , 2020)). While only cross-sectional datasets have been shared so far, the potential of longitudinal dataset sharing is even greater. Indeed, by focusing on within-subject effects, sharing of longitudinal data is less prone to problems linked to differences in data quality and scanning parameters between sites. An approach especially promising is the sharing of control group data in interventional studies to increase the sample size in developmental and ageing studies of healthy individuals. Another potential source of longitudinal neuroimaging datasets is the routinely acquired scans used to assess the health of NHPs  (Basso et al.
Specific strength: Developmental studies Longitudinal MRI in NHPs is of considerable interest for investigating brain development from birth to adulthood or even prior to birth, thanks to foetal imaging. One major objective of such studies is to establish developmental trajectories in NHP species and to compare them across NHP species and with humans. Another goal is to develop experimental NHP models of human neurodevelopmental pathologies, thanks to either genetic mapping or pharmacological, environmental, or behavioural interventional approaches. Several NHP longitudinal neurodevelopmental MRI databases have recently emerged (see Table  1 ). For instance, several rhesus macaque longitudinal MRI databases from postnatal to early adulthood can be found from different primate facilities mostly located in the USA  (Scott et al.
To our knowledge, no such longitudinal neurodevelopmental database exists for squirrel monkeys (Saimiri sp. ) or mouse lemurs (Microcebus sp. ), two genera of increasing relevance to the field of neuroscience  (Fritz et al. , 2020; Royo et al. , 2021). (Majka et al. , 2016)  and anatomical sections  (Paxinos et al. , 2012). There is clearly a need for expansion of such atlases, covering longer macaque age ranges, and including other species such as baboons, squirrel monkeys, or mouse lemurs. Using MRI in NHPs to study brain development provides critical benefits over human MRI datasets. First, while developmental trajectories in humans are well documented for healthy children aged 4 years old and up  (Giedd et al. , 1999; Sowell et al. , 2004; Raznahan et al. 2012) , it has been extremely challenging to collect high quality motion-free images from children below 4 years old.
Specific strength: Ageing studies The impact of ageing on brain structure and function is a topic of great interest which has been investigated mainly by a cross-sectional approach in humans and NHPs. Taking advantage of huge datasets and the development of algorithms to automatically analyse thousands of MRI scans, recent studies in humans have documented age-related brain changes consistent across subjects  (Cole et al. , 2018; Kaufmann et al. , 2019; Madan and Kensinger, 2018; Walhovd et al. , 2011; Wang et al. , 2016). However, cross-sectional studies of ageing suffer from two main limitations which can be overcome by using a longitudinal approach. First, cross-sectional studies do not allow distinguishing true ageing effects from cohort effects. Because cross-sectional ageing studies compare brain scans from groups of subjects born in different years (different cohorts), they cannot distinguish true ageing effects from brain differences induced by factors unrelated to ageing that changed between cohorts' birth years (e.