dataesr/scientific-paragraphs-categorization huggingface dataset:
Atlantic and Pacific first modes The leading mode of the Pacific dataset explains 47 % of the overall variance. The associated EOF, φ 1 , features a common region of coherence west of the Bōsō peninsula, south of Japan, where the Kuroshio separates from the coast (Fig. 5a , circular markers). The Atlantic leading mode explains 60 % of the variance, and, in a similar way, φ 1 features greater amplitudes south of the separation point at Cape Hatteras, decreasing northward from there (Fig. 5b ). On the southern coastline of Japan (130-141 • E), the amplitude is on average 2. 3 cm (excluding the easternmost virtual station located on the Bōsō peninsula; φ 1 = 0. 7 cm). Towards the north, east of Honshū and Hokkaidō, the mode amplitude is reduced by a factor of 3 and equals 0. 7 cm on average (this time including the virtual station on the Bōsō peninsula).
Atlantic and Pacific second modes While similar patterns emerge in both the Atlantic and Pacific leading modes, the same is not true for the second modes. The second EOFs φ 2 of the Pacific and Atlantic tide gauges are presented in Fig. 6a  and b , respectively. The EOF of the Pacific dataset is dominated by the tide gauges on the shores of the Tōkai district. This pattern corresponds to the group of correlation from Uragami (TG 17) to Yokosuka (TG 23) presented in Fig. 3a. The averaged amplitude of the mode in the region is 2. 5 cm, but it rises to 3. 0 cm when only the two virtual stations east of the Kii peninsula are considered. This indicates that, in the region south of Tōkai, the second mode is larger in magnitude than the leading mode. The second EOF of the Atlantic is dominated by the tide gauges north of Cape Hatteras, with an amplitude of 1.
Finally, although the causal link between the upstream sea level and the meridional shifts of WBC extensions is not yet completely understood, our results suggest that upstream inshore tide gauges, such as Key West (available from 1913 in the PSMSL RLR database), Fernandina Beach (1897) or Hosojima  (1930) , could be used as proxies for the extension meridional shifts and, by extension, the forcing responsible for such meridional shifts. In the Pacific, tide gauges in the region west of Kii, where the sea level is less affected during large meander periods, should be preferred. That tide gauges can be used to infer past open-ocean dynamics is a result in line with some recent studies which have reconstructed openocean sea level (and hence ocean dynamics) using the global tide gauge network and advanced statistical methods  (Ezer and Dangendorf, 2020; Dangendorf et al.
Also, β 1 is preceded by a minus (-) because a rise of local atmospheric pressure makes sea-level fall and vice versa. To determine the best model for each tide gauge, 95 % confidence intervals are computed for each regression coefficient within the MATLAB built-in function. The regression models that feature one (or several) coefficient confidence interval(s) crossing zero are excluded. Then, the best regression is defined as the one with the highest adjusted coefficient of determination R 2 Adj , which is the proportion of the variance in the gauge record that is predictable from the atmospheric predictors, adjusted for their number (see Eq. A2): R 2 Adj = 1 - m i=1 ζ i -Y i 2 m i=1 ζ i -ζ 2 m -1 m -n r -1 , ( A2 ) where m is the number of time steps, n r is the number of regressors for that particular model (n r ∈ [1, 2, 3]) and Y the sum of the obtained atmospheric contributions.
2. 1) are used. The 1981-2010 objectively analysed mean temperature at 200 m depth field from the World Ocean Atlas 2018 (Locarnini et al. , 2018, WOA18) is used to derive the climatological position of the Gulf Stream and Kuroshio extensions between 75 and 55 • W and between 141 and 161 • E, respectively. The climatological extension positions are defined as the whole-number isotherm roughly corresponding to the surface velocity maximum, which is 17 • C in the Atlantic and 16 • C in the Pacific. The two datasets were downloaded on 8 September 2020. Derivation of jet latitudinal position indices is detailed in Sect. 2. 3.
Acknowledgements. Samuel Tiéfolo Diabaté would like to thank his colleagues of the A4 team as well as  Benoit Meyssignac ,  Simon Michel ,  Juliette Mignot  and  David Pugh  their for valuable advice and suggestions. Norihisa Usui ,  Magdalena Andres ,  Christopher Piecuch ,  Arnoldo Valle-Levinson ,  Dudley Chelton ,  Thomas Frederikse ,  Karen Simon ,  Philip Woodworth  and  David Smeed  are also thanked for their mail correspondence, as is  Jeanne Auboiron  for her preliminary work. Financial support. Gerard D. McCarthy  and  Samuel Tiéfolo Diabaté  work as part of the  A4   Project (Aigéin, Aeráid, agus Athrú Atlantaigh -Oceans, Climate, and Atlantic Change ; grant-aid agreement no. PBA/CC/18/01 ), which is carried out with the support of the  Irish Marine Institute  under the  Marine Research Programme  funded by the  Irish Government , co-financed by the  European Regional Development Fund.
Gerard D. McCarthy  is further supported by the  ROADMAP  project (grant-aid agreement no. PBA/CC/20/01 ) supported by the  Marine Institute  and funded by the  Irish Government  under the 2019  JPI Climate and JPI Oceans Joint Call. Didier Swingedouw  received support from the  Blue-Action (European Union 's  Horizon 2020 research and innovation programme , grant number:  727852 ) and  EUCP (European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 776613 ) projects. Joël Jean-Marie Hirschi  acknowledges funding from the  Newton Fund CSSP China  project  DYVA  and from the  NERC  project  ACSIS  ( NE/N018044/1 ).
36 [0. 26-1. 02] ng m -2 s -1 ). Analysis of water samples from the ASITs with gas chromatography (see Methods) confirms the presence of BTEX compounds in the sampled seawater, that were in close agreement to levels measured via workboat sampling in the surrounding ocean. Although relatively few data points (18. 5% of data points) were obtained during daytime, there were no significant difference between daytime (0. 18 ± 0. 13 ng m -2 s -1 e. g. , benzene flux) and nighttime (0. 17 ± 0. 14 ng m -2 s -1 for benzene flux) emission fluxes. There was a consistent, strong linear relationship between the fluxes of B and the sum of E + X across the three experiments (Slope = 0. 67, r 2 = 0. 94) and both B and E + X were correlated with the fluxes of the known biogenic compounds DMS (r² = 0. 86 and 0.
2). Shipborne observations of BTEX in the atmosphere over the remote Southern Oceans. Ambient mixing ratios of benzene, toluene, and the sum of ethylbenzene + xylenes were determined for remote open ocean air masses East of New Zealand measured during the Sea2Cloud campaign, and carefully filtered for ship stack contamination and/or terrestrial influence (see Methods). BTEX concentrations were found above the detection limit of the instrument for 26. 1, 47. 6, and 41. 5% of the time, with average concentrations (and standard deviation) of 0. 026 ± 0. 023 ppb, 0. 038 ± 0. 042 ppb, and 0. 026 ± 0. 041 ppb for benzene, toluene, and xylenes, respectively. Ambient fluxes of BTEX were calculated from the Sea2Cloud data via the nocturnal accumulation method  21, 22  , which assumes negligible nighttime photochemical losses and overnight accumulation of sea surface emission within the well-mixed marine boundary, with horizontally homogeneous fluxes and steady surface layer height (see Methods).
highest ambient atmospheric concentrations of benzene and toluene were observed over a chlorophyll enriched upwelling zone IIa (30-40 °S) in the Southern Indian Ocean during the MANCHOT campaign  23  with averaged concentrations of toluene and benzene of 0. 278 ± 0. 075 and 0. 085 ± 0. 036 ppb. Besides levels of BTEX observed in ambient air during the Sea2Cloud campaign were of the same order of magnitude as those reported from previous ship campaigns in the Southern Ocean for which the datasets were revisited in the framework of the present study. The MANCHOT campaign took place during southern hemispheric summer (December), in contrast to the other four voyages (Drake Passage in 2009;  24  SOAP in 2012;  25   S. 4) reported here which took place during the autumn season (March-April), and the higher concentrations observed during MANCHOT might point to a seasonal variation in phytoplankton type and abundance and subsequent BTEX emissions.
Identification and filtering of data impacted by ship exhaust. BTEX compounds are enhanced in ship exhausts from diesel combustion, waste incineration, and kitchens and must be carefully screened to ensure the quality of atmospheric data collected from marine research vessels. PTR-MS data from the four voyages reported in this study employed different exhaust screening methods as follows: Sea2Cloud-Air masses impacted by ship and kitchen exhausts were identified using SO 2 and the PTR-MS signal at m/z 57. When a spike of these compounds was observed in ambient and bypass air, data in the ASITs were excluded during the peak period and the following 2 h (3 x residence time in the ASIT's headspace). In the ambient air, only spikes of high SO 2 data were excluded, after it was checked that the BTEX concentrations decreased at the same rate as SO 2 , showing that no slow inlet desorption was occurring after ship stack contamination.
SOAP-a "baseline wind switch" was employed in the SOAP campaign which switched the PTR-MS sample to zero air when wind conditions made ingestion of ship exhaust emission likely. The data were further screened using CO 2 , aerosol, and VOC exhaust tracers to ensure complete removal of exhaust impacted air masses  21. MANCHOT-a CO filter correction was applied to the ambient PTR-MS data with time periods flagged for removal when a peak > 65 ppb of CO appeared. CAPRICORN-the exhaust data filter described in Humphries et al. 48 was applied to the PTR-MS data from CAPRICORN. The data were further screened using VOC exhaust tracers to ensure complete removal of exhaust impacted air masses. A Stir Bar Sorptive Extraction (SBSE) technique was used to characterize VOC concentrations in seawater samples.
its functions in the central nervous system are poorly characterized. Through international collaborations  [7] [8] [9]  conforming to local ethical guidelines and the declaration of Helsinki, we identified 42 individuals with a rare (likely) pathogenic variant in SATB1 (GenBank: NM_001131010. 4), a gene under constraint against loss-of-function and missense variation (pLoF: o/e ¼ 0. 15 [0. 08-0. 29]; missense: o/e ¼ 0. 46 [0. 41-0. 52]; gnomAD v2. 1. 1). 10  Twenty-eight of the SATB1 variants occurred de novo, three were inherited from an affected parent, and five resulted from (suspected) parental mosaicism (Figure  S1 ). Reduced penetrance is suggested by two variants inherited from unaffected parents (identified in individuals 2 and 12; Table  S1A ), consistent with recent predictions of incomplete penetrance being more prevalent in novel NDD syndromes.
Related individuals are marked with a blue box. (D) Photographs of teeth abnormalities observed in individuals with SATB1 variants. Dental abnormalities are seen for all variant types and include widely spaced teeth, dental fragility, missing teeth, disorganized teeth positioning, and enamel discoloration (Table  S1B ). (E) Computational average of facial photographs of 16 individuals with a missense variant (left) and 8 individuals with PTVs or (partial) gene deletions (right). (F) Mosaic plot presenting a selection of clinical features. (G) The Partitioning Around Medoids analysis of clustered HPO-standardized clinical data from 38 individuals with truncating (triangle) and missense (circle) variants shows a significant distinction between the clusters of individuals with missense variants (blue) and individuals with PTVs (red).
Applying Bonferroni correction, a p value smaller than 0. 025 was considered significant. For analyses displayed in (F) and (G), individuals with absence of any clinical data and/or low-level mosaicism for the SATB1 variant were omitted (for details, see supplemental materials and methods). S1B ). We performed functional analyses assessing consequences of different types of SATB1 variants for cellular localization, transcriptional activity, overall chromatin binding, and dimerization capacity. Based on protein modeling (Figure  2 , descriptions of 3D protein modeling in supplemental information), we selected five missense variants (observed in 14 individuals) in CUT1 and CUT2 affecting residues that interact with, or are close to, the DNA backbone (mosaic variant c. 1220A>G [p. Glu407Gly] and de novo variants c.
When compared to WT YFP-SATB1 or UK10K variants, most variants identified in affected individuals show a nuclear cage-like localization (A), stronger co-localization with the DNA-binding dye Hoechst 33342 (B), increased transcriptional repression (C), reduced protein mobility (D), and unchanged capacity of interaction with WT SATB1 (E). The American Journal of Human Genetics 108, 346-356, February 4, 2021 353 We are extremely grateful to all families participating in this study. In addition, we wish to thank the members of the Genome Technology Center and Cell culture facility, Department of Human Genetics, Radboud university medical center, Nijmegen, for data processing and cell culture of proband-derived cell lines. This work was financially supported by Aspasia grants of the Dutch Research Council (015.
Method for modeling CUT2 variants The first NMR model of the PDB entry 2CSF [DOI:10. 2210/pdb2CSF/pdb] was used as a template to align residues T491 to H577 of the SATB1 human protein (uniprot entry Q01826), and build a model using Swiss-PdbViewer 3. The resulting model has been superposed onto the CUT1 domain of pdb entry 2O4A 2 using the "magic fit" option of Swiss-PdbViewer to highlight the position of the variants with respect to DNA.
Method for modeling homeobox domain variants The Solution structure of the homeodomain of human SATB2 (second NMR model of the PDB entry 1WI3 [DOI:10. 2210/pdb1wi3/pdb]) was used as a template to align residues P647 to G704 of the SATB1 human protein (uniprot entry Q01826), and build a model using Swiss-PdbViewer 3. Chains A, C and D of the crystal structure of HNF-6alpha DNA-binding domain in complex with the TTR promoter (PDB entry 2D5V  4  ), which has a DNA binding domain similar to the CUT2 domain of SATB1 and a second DNA binding domain similar to the homeobox of SATB1, was used as a template to superpose the model of the SATB1 homeobox domain onto the HNF-6alpha structure using the "magic fit" option of Swiss-PdbViewer. The variant P181L variant sits in a linker region between the ubiquitin-like domain (ULD; grey) and a CUT repeat-like (CUTL) domain (dim green).
p. E413K Q420 is located at the surface of the CUT1 domain, not in direct contact with DNA. An arginine at this position could easily be accommodated, but since it is bulkier and positively charged, it may affect the binding of CUT1 to other domains. Of note, the superposition of the CUT1 domain onto the DNA binding domain of rat HNF6 alpha bound to the TTR promoter (pdb entry 2D5V, chain A) reveals that Q420R would be roughly in the same position as HNF6alpha K53, which points in the minor groove of the DNA and makes indirect contact to the DNA backbone via structural water molecules (Figure  6 ). This mutation may likely affect the overall affinity of the structural complex. p. Q525R Q525 is located in the CUT2 domain alpha-helix that binds the major groove of the DNA, and is the equivalent of CUT1 domain Q402.
Of note, the superposition of the CUT2 domain onto the DNA binding domain of rat HNF6 alpha bound to the TTR promoter (pdb entry 2D5V, chain A 4 ) reveals that E547K would be roughly in the same position as HNF6alpha E57, which is solvent exposed. Interestingly, it is also in a position close to the CUT1 domain variant Q420R, just one turn of alpha-helix away. This mutation will likely affect the overall binding affinity of other domains to the CUT2 domain. p. L682V L682 is not proximal to DNA. It is located at the end of the alpha-helix E672-L682, just before a loop, neither of which are either in contact with the DNA. It is buried and probably contributes to maintain the homeobox domain fold. The valine mutant will have a less optimal packing of this region, and its branched sidechain is predicted to moderately clash with Ala 655 and Leu 684 sidechains and is expected to induce a small conformational change in this region.
this study were facilitated by ERN ITHACA, one of the 24 European Reference Networks (ERNs) approved by the ERN Board of Member States, co-funded by European Commission. The aims of this study contribute to the Solve-RD project (E. d. B. , H. G. B. , S. B. , A. -S. D. -P. , L. F. , C. G. , A. J. , T. K. , A. V. , L. E. L. M. V. ), which has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 779257.
,000 18,380,000 18,390,000 18,400,000 18,410,000 18,420,000 18,430,000 18,440,000 18,450,000 18,460,000 18,470,000 18,480,000 18,490,000 18,500,000 18,510,000 18,520,000 18,530,000 18,540,000 UCSC Genes (RefSeq, GenBank, CCDS, Rfam, tRNAs & Comparative Genomics) NCBI RefSeq genes, curated subset (NM_*, NR_*, NP_* or YP_*) -Annotation Release GCF_000001405. 25_GRCh37. p13(2017-04-19).
Amino acid sequence alignments of the CUT1, CUT2 and Homeobox domain of SATB1 ,000 18,380,000 18,390,000 18,400,000 18,410,000 18,420,000 18,430,000 18,440,000 18,450,000 18,460,000 18,470,000 18,480,000 18,490,000 18,500,000 18,510,000 18,520,000 18,530,000 18,540,000 UCSC Genes (RefSeq, GenBank, CCDS, Rfam, tRNAs & Comparative Genomics) NCBI RefSeq genes, curated subset (NM_*, NR_*, NP_* or YP_*) -Annotation Release GCF_000001405. 25_GRCh37. p13(2017-04-19) A B Individual 27 Individual 20 SP|Q01826|SATB1_HUMAN p. Q525R SP|Q60611|SATB1_MOUSE TR|Q5U2Y2|Q5U2Y2_RAT TR|A0A1D5PV61|A0A1D5PV61_CHICK RQKPRPRTKISVEALGILQSFIQDVGLYPDEEAIQTLSAQLDLPKYTIIKFFQNQRYYLK 60 p. E407G 28 21 19 ---TRPRTKISVEALGILQSFIQDVGLYPDEEAIQTLSAQLDLPKYTIIKFFQNQRYYLK 57 ---TRPRTKISVEALGILQSFIQDVGLYPDEEAIQTLSAQLDLPKYTIIKFFQNQRYYLK 57 RQKTRPRTKISVEALGILQSFIQDVGLYPDEEAIQTLSAQLDLPKYTIIKFFQNQRYYLK 60 TR|F6W9B5|F6W9B5_XENTR RQQPRPRTKISVEALGILQSFIQDVGLYPDEEAIQTLSAQLDLPKYTIIKFFQNQRYYLK 60 SP|Q9UPW6|SATB2_HUMAN ---PRSRTKISLEALGILQSFIHDVGLYPDQEAIHTLSAQLDLPKHTIIKFFQNQRYHVK 57 * *****:**********:*******:***:**********:***********::* SP|Q01826|SATB1_HUMAN HHG 60 SP|Q60611|SATB1_MOUSE HHG 60 TR|Q5U2Y2|Q5U2Y2_RAT HH-62 TR|A0A1D5PV61|A0A1D5PV61_CHICK HH-62 TR|F6W9B5|F6W9B5_XENTR HH-62 SP|Q9UPW6|SATB2_HUMAN HHG 60 ** SP|Q01826|SATB1_HUMAN NGKPENNTMNINASIYDEIQQEMKRAKVSQALFAKVAATKSQGWLCELLRWKEDPSPENR 60 SP|Q60611|SATB1_MOUSE NGKPENNTMNINASIYDEIQQEMKRAKVSQALFAKVAATKSQGWLCELLRWKEDPSPENR 60 TR|Q5U2Y2|Q5U2Y2_RAT NGKPENNTMNINASIYDEIQQEMKRAKVSQALFAKVAATKSQGWLCELLRWKEDPSPENR 60 TR|A0A1D5PV61|A0A1D5PV61_CHICK NGKTENNSMNINASIYDEIQQEMKRAKVSQALFAKVAATKSQGWLCELLRWKEDPSPENR 60 TR|F6W9B5|F6W9B5_XENTR NGNLENCTMNINASIYDEIQQEMKRAKVSQALFAKVAASKSQGWLCELLRWKEDPSPENR 60 SP|Q9UPW6|SATB2_HUMAN PIKVDGANINITAAIYDEIQQEMKRAKVSQALFAKVAANKSQGWLCELLRWKENPSPENR 60 SP|Q01826|SATB1_HUMAN TLWENLSMIRRFLSLPQPERDAIYEQES 88 SP|Q60611|SATB1_MOUSE TLWENLSMIRRFLSLPQPERDAIYEQES 88 TR|Q5U2Y2|Q5U2Y2_RAT TLWENLSMIRRFLSLPQPERDAIYEQES 88 TR|A0A1D5PV61|A0A1D5PV61_CHICK TLWENLSMIRRFLSLPQPERDAIYEQES 88 TR|F6W9B5|F6W9B5_XENTR TLWENLSMIRRFLSLPQTERDVIYEQES 88 SP|Q9UPW6|SATB2_HUMAN TLWENLCTIRRFLNLPQHERDVIYEEES 88 ******.
********** CUT1 domain p. Q402R p. E407G/Q p. Q420R CUT2 domain p. E530K/Q/G p. E547K Homeobox domain p. L682V p. Q525R Scale chr3: 1 Mb hg19 17,500,000 18,000,000 18,500,000 19,000,000 19,500,000 20,000,000 UCSC Genes (RefSeq, GenBank, CCDS, Rfam, tRNAs & Comparative Genomics) NCBI RefSeq genes, curated subset (NM_*, NR_*, NP_* or YP_*) -Annotation Release GCF_000001405. 25_GRCh37. p13 (2017-04-19) PLCL2 PLCL2 PLCL2 MIR3714 TBC1D5 TBC1D5 TBC1D5 TBC1D5 TBC1D5 TRNA_Pseudo LOC339862 SATB1 SATB1 SATB1 U6 KCNH8 KCNH8 KCNH8 MIR4791 KCNH8 EFHB EFHB RAB5A RAB5A RAB5A PP2D1 PLCL2 PLCL2 MIR3714 TBC1D5 TBC1D5 TBC1D5 TBC1D5 TBC1D5 TBC1D5 TBC1D5 TBC1D5 TBC1D5 TBC1D5 TBC1D5 TBC1D5 TBC1D5 TBC1D5 TBC1D5 TBC1D5 TBC1D5 TBC1D5 TBC1D5 TBC1D5 TBC1D5 LOC105376975 LOC339862 SATB1 SATB1 SATB1 SATB1 SATB1 SATB1 SATB1 SATB1 SATB1 SATB1-AS1 KCNH8 MIR4791 EFHB EFHB RAB5A RAB5A PP2D1 Scale chr3: 50 kb hg19 18,370SATB1 SATB1 SATB1 SATB1 SATB1 SATB1 SATB1 SATB1 SATB1 SATB1 SATB1 SATB1 SATB1-AS1 1.
Emission spectra were corrected for source intensity (lamp and grating) and emission spectral response (detector and grating) by standard correction curves. Detailed information on the concentrations of zeolite and dye loading is listed in Table  S5 , Supporting Information, for each individual measurement. Microplate assays were performed on a Perkin Elmer EnSight multimode plate reader in OptiPlate-96 microplates. Quantum yield measurements were performed on a Horiba Jobin-Yvon IBH FL-322 Fluorolog-3 spectrometer with a Quanta-ϕ integrating sphere as an accessory attached. The data was acquired by the commercially available software FluorEssence (Horiba Jobin-Yvon) version 3. 5. Stoppedflow experiments were carried out on a JASCO FP-8300 fluorescence spectrometer equipped with a water thermostated (T = 25 °C) SFA-20 stopped-flow accessory from TgK Scientific Limited, which was driven by a pneumatic drive.
The raw data was analyzed by Microcal PEAQ-ITC analysis software using a 1:1 complexation model and the first data point was always omitted. All data were baseline corrected by the average value of the titration curve into water and corrected by an additional offset due to buffer mismatch effects caused by the ion leaching from the zeolites and ZARs. [73]  Informed consent was obtained from all individual participants included in the study. [74, 75].
Acknowledgements L. M. G. , S. S. , and F. B. acknowledge the  Deutsche Forschungsgemeinschaft (DFG Grants   BI-1805/2-1 ). L. M. G. and F. B. acknowledge the  Vector Stiftung  for financial support ( P2020-0135 ). F. B. and T. K. acknowledge  HEiKA -Heidelberg Karlsruhe Strategic Partnership  and thank  Shahzad Munir  for help with experiments involving two-photon microscopy. E. A. P. and L. D. C. acknowledge financial support from the  European Research Council  for  ERC Advanced Grant  (Grant Agreement no. 2009-247365 ) and  AXA Fundings. W. W. acknowledges funding from the  Deutsche Forschungsgemeinschaft (DFG)  under Germany's Excellence Strategy -2082/1 -39076171 and in GRK 2450. M. K. acknowledges that theoretical calculations were performed on the supercomputer ForHLR II funded by the  Ministry of Science, Research and the Arts Baden-Württemberg  and by the  Federal Ministry of Education and Research.
The Digit_Hum initiative, which they run in parallel with their respective activities at the École Normale Supérieure (ENS), also provides information for this overview as it offers a space for discussion about the digital humanities along with training and structuring of this field at the ENS and the Université Paris Sciences & Lettres 2. 1 Marie-Laure Massot is head of the "Humanités numériques et valorisation de corpus" initiative at the CAPHÉS (History and Philosophy of Science Archives Centre) which provides scientific and technical support for print and digital publishing projects led by ENS research teams. Agnès Tricoche is co-head of the Digital Humanities pole of the AOROC (Archaeology and Philology of the East and West) laboratory after having worked for 6 years as a database engineer at the TransferS Laboratory of Excellence (Investments for the Future programme ANR-10-IDEX-0001-02 PSL* and ANR-10-LABX-0099).