fairdataihub/poster-sentry-training-data huggingface dataset:
Wikibase ❖open source software ❖created by WikiMedia Foundation ❖user friendly ❖rich in collaborative tools ❖using RDF ❖flexible data model ❖multilingual Reasons for WikiHum ❖Decentralisation of historical data ❖Lack of reliable and Persistent Identifiers ❖Mismatch between reference databases (VIAF, GND, Wikidata) and the needs of historians ❖Need for harmonisation of historical data Process of implementing data into WikiHum Label_pl Label_en Description_pl Description_en Królestwo Polskie (1370-1568) The Kingdom of Poland (1370-1568) terytorium Królestwa Polskiego territory of the Kingdom of Poland Tab.1 Administrative system: label and description Label_en P value The Kingdom of Poland (1370-1568) instance of administrative system The Kingdom of Poland (1370-1568) Starts at 1370-01-01 The Kingdom of Poland (1370-1568) Ends at 1568-12-31 The Kingdom of Poland (1370-1568) Aen The Crown of Poland The Kingdom of Poland (1370-1568) legal basis reformy ustrojowe XIV w. The Kingdom of Poland (1370-1568) legal basis statuty wiślicko-piotrowskie (1356-1362 r.) The Kingdom of Poland (1370-1568) subclass of secular administrative system The Kingdom of Poland (1370-1568) Wikidata ID Q1649871 The Kingdom of Poland (1370-1568) ontohgis database id https://onto.kul.pl/ontohgis/administrative_sys tem_44 Tab.2 Administrative system: statements Source: https://ontohgis.pl/ final view in WikiHUM “One Base to rule them all, one Base to ﬁnd them, One Base to bring them all, and in the Wiki bind them” Reference database for Polish historical data Content ❖Item metadata ❖DARIAH.Lab ID - Handle.net ❖External ID (e.g. Wikidata, VIAF, etc.) ❖Standards and Ontologies (CIDOC CRM, OntoHGIS, SKOS) Biographical data ❖Polish Biographical Dictionary ❖Polish National Library Authority File ❖Urzędnicy Dawnej Rzeczypospolitej. Spisy [Officials of the Old Commonwealth. Lists] ❖etc. Geographical data ❖Historical Atlas of Poland ❖Historical-Geographical Dictionary of the Polish Lands in the Middle Ages ❖Polish National Register of Boundaries ❖Polish National Register of Geographical Names Wikibase as an environment for harmonisation of data about past: the example of WikiHum Authors Adam Zapała azapala@ihpan.edu.pl Tomasz Królik tkrolik@ihpan.edu.pl The Tadeusz Manteuffel Institute of History Polish Academy of Sciences COMING SOON wikihum.lab.dariah.pl
Gemeinsame Infrastrukturen und Services zum Forschungsdatenmanagement in, aus und für Baden-Württemberg 1. Einleitung und Grundlage 2. Drei Fallbeispiele aus bwFDM Dr. Sophie G. Habinger1 0000-0002-5513-954X Maximilian Heber1 0000-0003-3399-7532 Dr. Philipp Kling2 0000-0003-0104-4905 1Kommunikations-, Informations-, Medienzentrum (KIM) der Universität Konstanz 2Universitätsrechenzentrum der Universität Heidelberg In Baden-Württemberg gibt es bereits eine Vielzahl an Landesdiensten und -services, die Bedarfe aus dem Forschungsdatenmanagement adressieren. Informieren, Austauschen, Vernetzen Welche FDM- Infrastrukturen gibt es in BW? Zugang zu Rechen- und Speicher- ressourcen Speicherung großer Mengen an kalten Forschungsdaten kollaboratives Arbeiten mit kleinen Datenmengen bwVisu Speicherung großer Mengen an heißen Forschungsdaten bwSFS LSDF Online Storage SDS@hd bwData Archive bwSync&Share bwFDM Helpdesks bwSupport Virtuelle Maschinen und Cloud Computing bwCloud bwUniCluster Speichern | Rechnen | Speichern und Rechnen | Kollaboration | Helpdesks/Supportservices Emilia Mikautsch1 0009-0005-1758-1790CC BY 4.0 –https://creativecommons.org/licenses/by/4.0/(Logos ausgenommen) Projektpartner der Landesinitiative: Gefördert vom: bwHelpdesk zum FDM • Rückgriff auf/Verwendung von bwSupport Portal geplant • Landesweite Helpdesk- Infrastruktur ermöglicht einfache Kollaboration der gehosteten Beratungsangebote zu den einzelnen Services ? --- --- Vernetzung Schaffung von Rahmen- bedingungen zum Austausch: • überregional: E-Science-Tage • BW-intern: AK FDM, bwFDM- Forum und Austausch mit bwHPC E-Science-Tage AK FDM bwFDM-Forum forschungsdaten.info • Technisch & administrativ: bwFDM • Inhaltlich: Redaktionsteam aus deutschsprachigem Raum forschungsdaten.info FORTH-BW fdm@DHBW
Michael Kleineberg1 , Miriam Kötter2 , Jasmin Schmitz3 , Paul Schultze-Motel4 , Katja Wermbter5 1: Distributed Network for Publishing Services, Freie Universität Berlin, Universitätsbibliothek 2: Landesinitiative openaccess.nrw / Universität Duisburg-Essen, Universitätsbibliothek 3: ZB MED - Informationszentrum Lebenswissenschaften, PUBLISSO Publikationsberatung 4: Helmholtz-Gemeinschaft / open-access.network 5: FID BAUdigital, TU Braunschweig, Universitätsbibliothek Wer wir sind • Best Practices zu: Aufbau eines Open-Access-Beratungsangebots Organisation eines Helpdesks Dokumentation der Kundenkommunikation Tools (z. B. Ticketsystem) Verteilung von Ressourcen interner und externer Kommunikation • Erstellung einer gemeinsamen Materialsammlung Mitmachen Ebenfalls auf der Agenda Sie betreiben oder planen selbst einen Open-Access-Helpdesk und möchten sich darüber austauschen? Die Fokusgruppe freut sich über neue Mitglieder! Weitere Informationen finden Sie hier: Was wir tun Liste der Helpdesks und Beratungs- angebote in den teilnehmenden Einrichtungen Sammlung der häufigsten Anfragen (z.B. zu CC-Lizenzen etc.) Kurzportraits der teilnehmenden Einrichtungen in Form von Vorträgen bei jedem Treffen Vernetzung und Kollaboration Übersicht der Helpdesks FAQ- Sammlung Vorstellung der Helpdesks Austausch über die Beratungsangebote und gegenseitige Unterstützung bei Herausforderungen Die Fokusgruppe entstand als Resultat eines Workshops des BMBF-Projekts „open-access. network“ zum Erfahrungsaustausch über den Aufbau und Betrieb von Open-Access- Helpdesks im November 2021. Die Gruppe umfasst derzeit 26 Mitglieder aus 20 verschiedenen Einrichtungen und trifft sich alle drei Monate. Für ihre Arbeit hat die Fokusgruppe zur internen Nutzung im Wiki der Fokusgruppen von open-access.network eine Übersicht von Open-Access-Helpdesks und -Beratungsange- boten angelegt. Bisher sind 21 Einträge ent- halten. In zukünftigen Treffen soll die Liste weiter ergänzt werden. Dieses Werk ist lizenziert unter einer Creative Commons Namensnennung 4.0 International Lizenz. https://open-access.network/vernetzen/digitale- fokusgruppen/fokusgruppe-open-access-helpdesks Ü b e rb lic k ü b e r d ie O A -B e ra tu n g s a n g e b o te u n d -H e lp d e s k s , d ie d e rz e it in d e r F o k u s g ru p p e o rg a n is ie rt s in d A n z a h l d e r E in trä g e U n iv e rs itä t F H P ro je k t L a n d e s v e rn e tz u n g s s te lle S o n s tig e E in ric h tu n g e n 0 2 4 6 8 1 0 1 2 1 4 1 6 Diese Veröffentlichung ist im Rahmen einer Fokusgruppe des Projekts open-access.network entstanden. Für den Inhalt sind allein die Autor*innen verantwortlich.
Learning to correct for atmospheric seeing from solar observations John Armstrong and Lyndsay Fletcher University of Glasgow j.armstrong.2@research.gla.ac.uk 1. Introduction • Atmospheric seeing is ubiquitous in ground-based astronomy[1]. • We propose a post-processing correction for seeing using a machine learning algorithm known as generative adversarial network[2] (GAN). • This provides a kernel-free deblurring technique. • In models assuming some blur kernel explicitly it is hard to deal with occluded (optically thick) regions without a complex kernel estimation[9]. This is avoided in our model. Fig. 1: Discriminator[4,6] (D) Fig. 2: Generator[3,5] (G) References [1] Davies, R. and Kasper, M. (2012) Annual Reviews of Astronomy and Astrophysics, 50:350-351. [2] Goodfellow, I. et al. (2014). ArXiv e-prints. [3] Kupyn, O. et al. (2017). ArXiv e-prints. [4] Isola, P. et al. (2017). Proceedings – 30th IEEE Conference on Computer Vision and Pattern Recognition. [5] Lecun, Y. et al. (1998). Proceedings of the IEEE, 86(11):2278-2324. [6] Li, C. and Wand, M. (2016). Lecture Notes in Computer Science, 9907 LNCS:702-716. [7] Tsuneta, S. et al. (2008). Solar Physics, 249(2):167-196. [8] He, K. et al. (2015). ArXiv e-prints. [9] Nah, S. et al. (2016). ArXiv e-prints. Overview • We employ a post-processing machine learning algorithm to correct for atmospheric seeing. • The model is trained on data from SOT on Hinode which is imprinted with an artificial blur. • The model is then applied to data from CRISP with promising results. 2. Discriminator and Generator • There are two parts to a GAN: discriminator (D) and generator (G). • The goal of the generator is to fool the discriminator into thinking the data that it has created is real. • The discriminator wants to classify whether or not data is real. • The two play a game against each other and train simultaneously. D divides the generated and true images into 64, 128, 256, 512 segments, respectively (different colours represent the number of segments produced). D classifies each segment as real or fake. G learns the difference between the sharp and blurred image[8]. G adds what it has learnt to the blurry image for comparison with the sharp image. G reassembles the segments. 3. Blur Model Blur image via randomly offsetting the image w.r.t. itself twice and averaging. Fig. 3: SOT[7] image pre- and post-blurred 4. CRISP Ca II 8542ÅCorrection 5. Conclusion • Reconstructs lines well from CRISP data. • This shows that the image deblurring works perceptually and preserves spectral integrity. • This implies that the method can be used to correct for atmospheric seeing. Fig. 4: Correcting a CRISP observation of the 2014-09-06 flare. Blurry Sharp Corrected
2 3 4 If I look at myself in the CP mirror I just see myself! All measurements are in agreement with the SM prediction within 68% CL. A CP odd state is excluded at 99.7% CL (3.0 σ) ● Exclusion for CP-odd hypothesis:  Observed (exp.) significance = 3.0σ (2.6σ)  ϕCP distribution in bins of NN score Investigated τ decay channels The τh are identified with the DeepTau NN-based ID2 Decay modes identified with MVA-algorithm3 τ + τ − The cross-section of the H→ττ process has a sinusoidal shape d σ d ϕCP ∝const −cos(ϕCP−2α H τ τ) The Higgs through the looking glass. Measurement of the CP quantum number of the Higgs boson in H→ττ decays Andrea Cardini (DESY) on behalf of the CMS collaboration CP-violation in the Higgs couplings is investigated in: ● HVV couplings ●Yukawa coupling: ●Production via ttH and ggH ●Decays into τ leptons The Standard Model predicts that I have spin-parity 0+ But do I? Better check with a CP mirror We invert spatial coordinates and swap particles with anti-particles 1 2 Multicategory classification used to identify the Higgs decays from genuine ττ and lep./jet→τh backgrounds: ●BDT for fully hadronic channels ●Neural Network for semileptonic Signal parametrized as function of αHττ: weighted combination of CP even odd and maximum mixing cases ~ 90% of background estimated with data-driven methods ●Tau embedding for genuine ττ processes ●FF method for j→τh fakes NN MACHINE LEARNING BDT Likelihood profiled1 with respect to: 1. CP mixing angle 2. Yukawa couplings 3. CP mixing angle + inclusive Higgs production signal strength 4. Higgs production signal strengths (ggH + VBF) 5. CP mixing angle for Run 2, Run 3 and HL-LHC 5 3 Each decay plane is defined by two vectors4: a momentum and a reference vector CP even αΗττ= 0° CP mix αΗττ= 45° CP odd αΗττ= 90° Machine learning techniques can help in identifying me At the LHC I am surrounded by processes that can produce similar signatures to my decays to τ leptons CP mixing angle Acoplanarity angle a1(1260) ρ(770) The spin correlation is preserved for decays to leptons and via mesonic resonances pion momentum impact parameter (IP) polarimeter vector (h) References. 1. CMS-HIG-20-006: “Analysis of the CP structure of the Yukawa coupling between the Higgs boson and τ leptons in proton-proton collisions at √s= 13 TeV” 2. CMS-DP-2019-033: “Performance of the DeepTau algorithm for the discrimination of taus against jets, electron, and muons” 3. CMS-DP-2020-041: “Identification of hadronic tau decay channels using multivariate analysis (MVA decay mode)” 4. Andrea Cardini: “Measurement of the CP properties of the Higgs boson in its decays to τ leptons with the CMS experiment” 2 4 1 3 4 6 CP mixing angle measured with simultaneous fit for signal and background models over full Run 2 Fit performed with ●NN/BDT score for ML background categories ●Acoplanarity angle distributions in bins of NN/BDT score for higgs category → split by decay mode To be continued in Run 3 Uncertainty statistically dominated 1 5 7 d σ d ϕCP
> Programm Heeremannscher Hof · Hofsaal im Erdgeschoss Königsstraße 47 48143 Münster > Wozu berechtigt Not? Workshop der Kolleg-Forschergruppe 8. April 2014 > Veranstaltungsort Sebastian Laukötter und Reinold Schmücker Kolleg-Forschergruppe „Normenbegründung in Medizinethik und Biopolitik“ Die Zahl der Teilnehmer ist begrenzt. Wir bitten daher um Anmeldung unter: normenbegruendung@wwu.de www.normenbegruendung-in-der-bioethik.de > Organisation Bildnachweis: Stefan Klatt. 9:30 Begrüßung 9:45 – 10:35 Wozu berechtigt Not? Zur Notwendigkeit und Möglichkeit einer Ethik für Notsituationen Reinold Schmücker (Münster) 10:35 – 11:25 Moralisch retten Guntolf Herzberg (Berlin) 11:55 – 12:45 Notsituationen als Herausforderung an die Moralphilosophie. Grundsätzliche Überlegungen über Möglichkeiten und Grenzen der Ethik Héctor Wittwer (Berlin) 15:00 – 15:50 Not und die Grenzen der Moral Simon Derpmann (Münster) 15:50 – 16:40 ‚Handeln aus Not‘. Moralphilosophische Implikationen eines durchaus gewöhnlichen Sprachspiels Anke Thyen (Ludwigsburg) 17:10 – 18:00 Wozu verpﬂ ichtet Not? Überlegungen zum Status globaler Hilfspﬂ ichten Sebastian Laukötter (Münster)
Grant applicants Open science is a priority for Kim, she only has a short publication list. She uses pre-registrations and shares her pre-prints, peer-review. She works together in an electronic notebook and afterwards shares the metadata, datasets and protocols in trusted repositories. Publishing in journals with a high impact factor is a priority for John, he already has a lot of high impact factor publications. He finds data management an administrative task and only shares data when requested by the journal. He doesn’t even share his metadata. Mia has a lot of high impact factor publications. She developed an app that citizens can download to help collect data for her research project. She decided to share nothing of the research (not even the metadata) because of sensitive personal data. Koen collaborates with industry and contributed to many patents, due to related IP issues he has a short publication list and does not share many datasets. He does have an open attitude towards open science. Once he can share, he will. Afterall he wants to contribute to society & researchers. MIA KOEN KIM JOHN Rewards_A2.indd 1 02/05/2023 21:34
Universität Tübingen · Philosophische Fakultät Seminar für Sprachwissenschaft Keplerstr. 2 · 72074 Tübingen Telefon +49 7071 29-77352 · https://talar.sfb833.uni-tuebingen.de Datenübernahme durch TALAR, das Tübinger Archive of Language Resources Vorhandene Daten in TALAR • Gepflegte Referenzdaten o Tübinger Baumbank-Collection o GermaNet • Gehostete Daten o Daten des SFB 833/SFB 441 o Daten des GRK 1808 • Daten externer Partner o SFB 638 o Index Thomisticus Treebank o ... • Community Daten o Universal Dependency Treebanks (UD) Zertifizierung des Repositoriums Seminar für Sprachwissenschaft Philosophische Fakultät Tübingen Archive of Language Resources (TALAR) Spezialisierung • Baumbanken/Treebanks • Wortnetze • Word-Embeddings Modalität • geschrieben • gesprochen Akzeptierte Datenformate • GermaNet XML-Format • CoNLL-U • Word-Embeddings • Weitere Formate nach Absprache Ansprechperson(en) • Thorsten Trippel • Claus Zinn Suchmöglichkeiten in den gepflegten Daten Zertifikat erteilt am 01. Juni 2023 Aktuell: turnusgemäße Erneuerung Daten Einbringen Initiale Datenübermittlung • Übermittlung nach BagIT-Standard • Werkzeugunterstützung: Bagman Voraussetzung: • Unterstützte Datentypen o Qualitätsgesichert o Auswahlprozess • Datenüberlassungsvertrag o Muster siehe https://uni-tuebingen.de/de/134320 • Offene Lizenz: CC-BY 4.0 oder höher • Metadaten nach ISO 24622 • Unterstützte Profile § CourseProfile § ExperimentProfile § LexicalResourceProfile § ResourceBundle Findable • Auffindbar mit persistentem Identifikator (PID) • Handle / DOI • Nachweis über Forschungsdatensuchmaschinen • z. B. VLO • Auffindbarkeit über Standardsuchmaschinen Accessible • Downloadmöglichkeit freier Daten • Zugang zu vorhandenen zugangsbeschränkten Daten • Metadaten frei zugänglich über technische Protokolle Interoperable • Je nach Datentyp • Nutzbar in den vorhandenen Auswertungswerkzeugen • Metadaten: standardkonform und maschinell interpretierbar Reusable • Nutzbar unter den Bedingungen der Lizenzen in eigener Software • Verwendung in anderen Projektkontexten • Klare Rechte an den Daten • Vertrauenswürdige Aufbewahrung F A I R Scott Martens (2013). TüNDRA: A Web Application for Treebank Search and Visualization. In: Proceedings of The Twelfth Workshop on Treebanks and Linguistic Theories (TLT12), Sofia, pp. 133—144. Hinrichs, Marie and Lawrence, Richard and Hinrichs, Erhard (2020): Exploring and Visualizing Wordnet Data with GermaNet Rover. In Proceedings of the CLARIN Annual Conference 2020, pp. 32-36. C. Zinn: Bagman - A Tool that Supports Researchers Archiving Their Data. Selected papers from the CLARIN Annual Conference 2021 (Virtual Event), Linköping University Electronic Press, vol. 189, pages 181-189, 2022. § SpeechCorpusProfile § TextCorpusProfile § ToolProfile § WebLichtWebService
• • • • • • • • • • • • ➢ • • ➢ • • • • × • • • 1990 1995 2000 2005 2010 2015 2020 4000 6000 8000 10000 12000 1 10 100 1000 Spill amount Spill amount (million gallons) Spill frequency Year Spill frequency Deepwater Horizon spill Hurricane Katrina • • • • ：
PE5 Changes - Spoke 4. D2 – Data Management Plan. https://doi.org/10.5281/zenodo.7977103 Producing FAIR research data within a NRRP project: PE5 Changes - Spoke 4 The Italian National Plan for Open Science 2021-2027 sets out four objectives related to scientific research data. The first is to contribute to the fulfilment of the FAIR data paradigm in the Italian research system. As part of the National Recovery and Resilience Plan, the project PE5 Changes - Spoke 4: virtual tehcnologies for museums and art collections, led by the University of Bologna, is applying the FAIR data paradigm to Cultural Heritage (CH) data. The activities of Spoke 4 focus on the impact of digital cultural heritage. Its objective is to make the digital enhancement of cultural heritage a permanent and widespread practice in museums and art collections, to increase the knowledge and organization of artifacts, expand public involvement, improve accessibility, inclusiveness, participation, and sustainability. This is achieved by involving different museums and collections (high-density and innovative museums, natural history and scientific museums, widespread art galleries, site museums, historical palaces, demo-ethnic-anthropological museums) that work as pilots for best practices to be adapted and reused in similar institutions. The collaboration with Cultural Heritage Intitutions is indeed one of the main strenghts of the project. Each Spoke partner and CH institution involved has: Bianca Gualandi - Research Division (ARIC), University of Bologna, Bologna, Italy Silvio Peroni - Dept of Classical Philology and Italian Studies, University of Bologna, Bologna, Italy identified someone internally responsible for the creation and management of data set up an internal data governance, including guidelines for data storage and deposit in a data repository identified intellectual property rights and selected a reuse license for CH data identified and described a number of planned datasets, describing e.g., their scope, formats and technologies used transmitted all this information to the Spoke leaders in order to coalesce on a common Data Management Plan, to serve as project documentation and as roadmap Typically someone senior, who is involved in the digitisation process Piano nazionale di digitalizzazione del patrimonio culturale. https://docs.italia.it/italia/icdp/icdp-pnd- dmp-docs/it/v1.0-giugno-2022/attivita-preliminari.html Some decisions are necessarily provisional but the DMP is a living document and will be updated! Working with CH data poses legal, technological and organisational challenges, both for researchers and for CH institutions. Planning their management from an early stage ensures that CH data are usable, interoperable, and last long-term.
References: Ohyama, Terashima, & Sakamoto 2015, ApJ, 805, 162 ● Sakamoto et al. 2014, ApJ, 797, 90 ● Sanders et al. 2003, AJ, 126, 1607 ● Stierwalt et al. 2013, ApJS, 206, 1 ● Sun et al. 2018, ApJ, 860, 172 ● ALMA Project Code 2015.1.00714.S 10 1 10 0 10 1 Peak TB (K) 0.0 0.5 1.0 1.5 Mass Fraction per dex Centers Non-centers Combined 10 1 10 0 10 1 10 1 10 0 10 1 PHANGS PHANGS NGC 3256 Build GMC catalog in NGC 3256 to compare with spirals ● Sizes ● Masses ● Boundedness ● CO conversion factor (expect LIRG to be low and from measurements XCO ∝ Σ / (𝜎TB), Narayanan+ '11) Replicate pixel-based and cloud-based analyses in new maps of entire NGC 4038/9 merger Next steps NGC 3256 additionally split into northern (triangles) and southern (stars) nuclei. Vertical lines show 𝛼vir of 1 and 2. Per-galaxy, mass-weighted, inner 68% of pixel value distributions. Squares are centers, circles are non-central regions. Mass-weighted densities of pixels from 120 pc resolution maps. Initial PHANGS sample for comparison (Sun+ '18). 10 3 10 6 10 9 Pturb ∝ Σ 𝜎 2 10 0 10 1 𝛼vir ∝ 𝜎 2Σ-1 10 0 10 1 10 2 𝜎(km s 1) 10 0 10 2 10 4 Σ ☉ (M pc 2) 10 0 10 1 10 2 10 3 10 4 ☉ Σ (M pc 2) 10 0 10 1 10 2 𝜎(km s 1) NGC 3256 M31&M33 PHANGS Sample NGC 4038/9 Comparison to "normal" galaxies 10 h27 m53.0 s 51.5 s 50.5 s -43°54'05" 10" 15" 20" 25" 30" RA (J2000) Dec. (J2000) 1 kpc 53.0 s 51.5 s 50.5 s RA (J2000) 1 kpc 0.5 1.0 1.5 2.0 2.5 Σ (10 4 ☉ M pc 2) 30 60 90 120 150 𝜎(km s 1) Bulk dense gas surface density and velocity dispersion ● Channel width: 5 km / s ● Per-channel RMS: 0.9 mJy / beam ● Integrated RMS: 80 mJy / beam km / s (34 M☉ / pc2) ● Milky Way conversion factor used here Marked regions ● Elliptical 1 kpc projected distances from nuclei ● Rectangles around southern nucleus jet CO (2-1) maps ● Data cover central 7 kpc at 235 GHz ● CO (2-1) and CS (5-4) observed with 2.5 km/s resolution ● Continuum bandwidth: ~7 GHz ● Two main array configurations ● ACA and total power data were obtained ● Interferometric maps sensitive to scales from 53 pc to 6200 pc ALMA observations HST optical and IR view of NGC 3256. Square shows ALMA FoV (Evans+). ● Distance: 44 Mpc (0.2" = 43 pc) ● LIR = 6.5 x 1011 L☉ (Sanders+ '03) ● Ideal LIRG to study GMC-scale star formation ● Late-stage merger (Stierwalt+ '13) ● Starburst driven outflow in face-on northern nucleus (v ~ 750 km / s) ● Jet in edge-on southern nucleus has AGN contribution (Sakamoto+ '14, Ohyama+ '15) NGC 3256 ★brunettn@mcmaster.ca Nathan Brunetti★ & Christine D. Wilson Molecular gas in nearest LIRG denser and more turbulent than local disks