fairdataihub/poster-sentry-training-data huggingface dataset:
GATE-Time: Extraction of Temporal Expressions -and Events! Leon Derczynski Jannik Strötgen Diana Maynard Mark A. Greenwood Manuel Jung Time is critical to language • required to discuss past events, change, or plans • most information bounded in time: the sky isn’t always blue, George W Bush did not reign forever TimeML is an ISO standard for annotating time in language GATE is an established NLP platform We annotate time in language using GATE and following TimeML Temporal expressions are things like dates, times, and durations We find these using HeidelTime It marks up the times in the text .. e.g. “Can we reschedule for the Tuesday after next please” .. and links them to a calendar. We find events by upgrading an older system (Evita) The SVM/UM learns to find event words by exploiting the fact that most words aren’t events This gives us fast, flexible, state of the art performance Also, because GATE applications are modular, other Applications can be added * TIMEN * TimeNorm * Temporal co-reference GATE-Time is an open, modular, and supported tool for temporal information processing. Use it! PHEME
CARTE: Pretraining and Transfer for Tabular Learning Myung Jun Kim, Léo Grinsztajn, Gaël Varoquaux Pretrain Models for Tabular Learning Context-Aware Representation of Table Entries Schema Matching Not Required for CARTE Key Ingredients from CARTE GitHub Datasets Paper ▪Generalization of table entries with graph representation ✓No schema or entity matching across tables ▪Pretrain from large knowledgebases (knowledge graphs) ✓Train over 18.1 million triplets of 6.3 million entities in YAGO ▪Graph-attentional network that leverage context in tabular data Three key components of CARTE: Background information to downstream tasks Finetuning Strategies of CARTE CARTE Markedly Outperforms the Alternatives Entity Matching Not Required for CARTE More than LLMs: modeling numbers Toward tabular foundation models [Graph Representation] [Pretrain] [Graph-Attentional Network] ▪Finetune CARTE for single-tables and multi-tables ▪Bagging strategy on train/validation splits ▪Limit the number of layers 51 datasets representative of modern data science applications [Regression] [Classification] Extensive comparison over 42 baselines for tabular learning Preprocessing Methods TableVectorizer (TabVec) Target Encoder (TarEnc) Sentence-LLM (S-LLM) FastText Embeddings (FT) LLM Embeddings (LLM) Estimators CARTE CatBoost XGBoost (XGB) HistGradBoosting (HGB) RandomForests (RF) Linear (Ridge / Logistic) NN Models (ResNet / MLP) TabPFN Abundant but heterogenous tabular data ▪Manual entity matching of the entries to their corresponding YAGO entity ▪Knowledge graph embeddings (KEN) require entity matching to have good performance ▪CARTE is robust to entity variants Results over four datasets with manual entity matching: ▪Tables with meaningful columns and discrete entries Groups covering the same general topic but from different sources ▪12 topics - 125 cases for two-tables and 245 cases for multiple-tables [Comparison on Two-Tables] [CARTE Across Multiple-Tables] CARTE provides consistent improvements without column matching CARTE further benefits from additional source tables Performance boosting architecture ① “George Bush”? 41st or 43rd president? ② ‘Term’ and ‘Incumbency’ are they same columns? ③ ‘London’ and ‘Londres ’ are they same entities? Ambiguity in schema and entity resolution Different table specifications ✓Number of columns, inference tasks Out of vocabularies
All Errors Are not the Same: Different Memory Profiles between Aging, Alzheimer's Disease and Semantic Dementia from a New Memory Test G.T. Vallet1,2, M. Simard2,4, J. Macoir3,4, C. Hudon2,4, N. Bier5,6, & R. Versace1 1 Laboratoire EMC, Lyon 2 University, Lyon, France 4 CRUSMQ, Quebec City, Canada 2 School of Psychology, Laval University, Quebec City, Canada 5 École de Réadaptation, Université de Montréal, Montréal, Canada 3 Département de Réadaptation, Université Laval, Quebec City, Canada 6 Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Canada Memory Group Age Education Gender (M/F) MMSE (/30) Young (n=55) 24 (3.7) 16 (2.6) 15/40 30 (0.3) Elderly (n=36) 73 (6.9) 14 (5.1) 8/25 29 (1) Alzheimer's Disease (n=16) 76 (7.8) 14 (4.3) 5/11 24 (2.6) Semantic Dementia (n=3) 66 (11) 14 (1.2) 3/0 27 (2.1) Episodic Semantic Young OK OK Elderly +/- OK Alzheimer's Disease OUT +/- Semantic Dementia +/- OUT Memory Errors? Elderly False memories → Alzheimer False memories & intrusions → Semantic dementia Semantic errors → How to differentiate clinical populations? Few clinical tests rely on a qualitative approach Populations Episodic/Semantic Memory Test Adaptation of the Pyramid and Palm Trees Test Learning phase Test phase Material 32 cards with 3 line-drawing items: (1) reference (2) good match (3) distractor 64 cards: - 32 good matches - 16 distractors - 16 new items Tasks Matching task Learn the correct match Free recall Recognition task Delayed free recall Naming task Learning phase Errors qualification Reference Good match Item to learn Distractor Distinct Memory Profiles Conclusions Distinct profiles: Semantic Dementia (/others) ←→ Semantic errors Aging (/young) ←→ Confusions Alzheimer's Disease (/others) ←→ Intrusions HIT CONFUSION OR INTRUSION PPTT / Buschke Encoding Sum Free Recall Sum Cued Recall Delayed Free Recall Matching -0.14 0.22 0.12 -0.14 Free Recall 0.17 0.60* 0.46* 0.42* Free Recall – Conf 0.1 0.03 0.11 0.01 Delayed Free Recall 0.1 0.50* 0.25 0.35* Delayed Free Recall – Conf 0.09 -0.43* -0.39* -0.26 Recognition -0.01 0.34* 0.21 0.1 Recognition – Conf -0.07 0.08 -0.05 -0.11 Naming -0.05 0.14 0.18 -0.19 A validated memory test Notes. Correlations between the PPTT adapted and the Buschke memory test. Conf = confusions; * = p<0.05; Intrusions not included due to lack of variance. A valid memory test? - episodic memory test (correlations) - semantic memory test (remain to be assessed) Use a qualitative approach of memory errors Young
Decellularized extracellular matrix (ECM) hydrogels can be used to recapitulate the in vivo peri-islet niche. Creative Proteomics provides protein characterization services that can qualitatively and quantitively analyze the target protein and its post-translational modification. Characterization of Porcine Decellularized Tissues Protein Characterization Organ-on-a-chip platforms are cost-efficient testbeds for screening pharmaceutical agents, mimicking biological systems, and modeling human diseases. In the field of diabetes, the development of an islet-on-a- chip platform would have broad applications on understanding pathology and discovering potential therapies. Leveraging microphysiological systems for the long-term culture of primary pancreatic islets, however, has been limited by poor stability of these cells ex vivo, with the key factor being the disruption of islet-matrix interactions following isolation. The use of decellularized tissues integrates a diverse spectrum of biochemical and biophysical cues, adapted from natural biomaterials, for the encapsulated cells to be viable and functional. The composition of the ECM can be tailored by selecting the tissue source and the decellularization approach. In order to determine the protein composition of porcine decellularized tissues, protein characterization was performed. To characterize protein composition, decellularized tissue samples were analyzed using Dionex Ultimate 3000 Nano LC system coupled with an Orbitrap Q Exactive mass spectrometer. ASSAY OVERVIEW
PRESENCE OF SALMONELLA SPP. AND HEPATITIS E VIRUS IN ITALIAN PIG FARMS G. Ianiro1, E. Pavoni 2,G. Aprea3, R. Romantini3, G. Alborali2, D. D’Angelantonio3, G. Garofolo3, S. Scattolini3, L. De Sabato1, F. Ostanello4, I. Di Bartolo1. 1) Istituto Superiore di Sanità, Dept. Food Safety, Nutrition and Veterinary public Health, Rome Italy; 2) Istituto Zooprofilattico Sperimentale della Lombardia e dell'Emilia Romagna "Bruno Ubertini", Brescia, Italy; 3) Istituto Zooprofilattico Sperimentale dell'Abruzzo e del Molise "Giuseppe Caporale", Teramo, Italy; 4) University of Bologna, Faculty of Veterinary Sciences, Ozzano dell’Emilia, Italy. • Salmonella spp. was detected in 14/47 (29.8%) farms, while HEV was detected in 16/47 (34.0%) farms. • The highest Salmonella spp. prevalence was in breeding farms (8/18 positive farms, mean individual prevalence 14.4% per farm), while for HEV was in fattening farms (9/22 farms positive, mean individual prevalence 53.9% per farm) by testing young animals aged 3-4 months. . This poster is part of the European Joint Programme One Health EJP. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 773830. RESULTS METHODS EU is the second worldwide producer of pig meat after China and the biggest exporter worldwide. Due to the importance of this production for both economy and public health, being pork widely consumed, its whole food chain is strictly monitored to guarantee highly safe food. Salmonella spp., is a main cause of foodborne outbreaks, while hepatitis E virus (HEV) is a recently recognized emerging zoonotic foodborne pathogen, whose role need to be still defined. Pigs are reservoirs of both. In the framework of the European Joint Programme “Biosecurity practices for pig farming across Europe (BIOPIGEE)”, the presence of Salmonella spp. and HEV in pig farms over EU countries was evaluated. In Italy, 47 pig farms were investigated, including 22 finishing, 18 breeding, and 7 farrow-to-finish farms, by collecting a total of 958 pooled fecal samples. CONCLUSION AND PERSPECTIVES In conclusion, the main source of contamination of pigs is represented by the farming stage, probably due to the environmental contamination. Subsequently the monitoring of pigs at farm and the implementation of biosecurity measures remains critical to reduce the risk of slaughtering infected animals. BREEDING Total tested Positive (%) Total tested Positive (%) HEV 5 (27,77%) 20 (5,37%) Salmonella 8 (44,44%) 23 (6,18%) FATTENING Total tested Positive (%) Total tested Positive (%) HEV 9 (40,90%) 97 (21,74%) Salmonella 6 (27,27%) 23 (5,15%) 22 446 Farms Fecal pool samples 18 372 Farms Fecal pool samples FARROW-TO-FINISH Total tested Positive (%) Total tested Positive (%) HEV 2 (28,57%) 17 (12,14%) Salmonella 0 (0,00%) 0 (0,00%) TOTAL Total tested Positive (%) Total tested Positive (%) HEV 16 (34,04%) 134 (13,98%) Salmonella 14 (29,78%) 46 (4,81%) Farms Fecal pool samples 47 Farms Fecal pool samples 7 958 140
The Social Media for Learning in Higher Education Conference 2019 Dawne Irving-Bell, Edge Hill University and Lauren S. Schlesselman, University of Connecticut Beginning as strangers selected for participation in an International Collaborative Writing Group (ICWG), where group members are geographically located thousands of miles apart this work serves to address the question “Can social media be utilised to effectively support international collaborative writing?” Adopting an approach informed by collaborative autoethnography, focused on their experiences this work illuminates the stories of two of the six participants as they sought to establish themselves and their position within the group. Illuminating the challenges encountered the purpose of this preliminary study is to support others who may be engaged in or considering undertaking work of a similar nature. As such this presentation will be of interest to any colleagues thinking about using social media as a means of communication when engaged in collaborative academic work where geographical locations prevent physical engagement. Telling our stories Using social media to bridge geographical boundaries • Cultural differences in expectations and approaches to work • Finding suitable dates, and especially a suitable time to meet virtually • Interpreting written communication (namely emails) to understand the nuances of what was actually being said • Collating everyone’s ideas, ensuring everyone had a voice and an equal stake in the conception of the paper • Don’t assume anything! Don’t be afraid to ask for clarification, and always double check everything • Start by identifying who is good at what, and we don’t just mean in relation to writing, so who is best placed to set up work areas or host and schedule online meetings • Take time to reflect your understanding of what has been said and then articulate it back to the group to ensure everyone is on the same page • Use multiple means of communication • Meet online as often as you can, always consolidating discussions with follow up messaging, emails and texts • Try to meet physically if you can and when you do start with a social activity and schedule as many social activities together as you can! What were our Success Our tips for differently? What would we do Challenges?
Design, Implementation and Iteration of a Digital Preservation Processing Workflow Eamon Smallwood (eamon.smallwood@kaust.edu.sa), Marcelo Garcia (marcelo.garcia@kaust.edu.sa) University Library, King Abdullah University of Science and Technology (KAUST) https://library.kaust.edu.sa/ Iteration 1 Iteration 2 Iteration 3 Design Workflows describe, justify, and plan for the implementation of a sustainable, long-term digital preservation service at KAUST University Library Each workflow was designed with a specific audience in mind: Iteration 1: University Library Management and University IT Iteration 2: University Library Preservation and Digital Services Team Iteration 3: University Library Systems Team Designed in accordance with international standards and best practice for digital preservation(OAIS ISO 14721) Designs evolved due to: Assessment of existing storage solutions at KAUST Procurement of dedicated digital preservation technology and services Automation of workflow processes Implementation In 2020, the University Library began implementing the preservation processing workflow(Iteration 1) to bring our digital preservation capabilities in line with international standards and best practice Assessments revealed that the existing storage infrastructure was not well suited for long- term preservation In 2021, the University Library procured two key components of the preservation processing workflow: Dedicated digital preservation service – Preservica Dedicated digital preservation workstation – FRED (Forensic Recovery Evidence Device) Processed all University Library Archives digital collections into Preservica via FRED(Iteration 3) in December 2021 In 2022, the University Library began automating the preservation workflow processes(Python), available on Github - https://github.com/kaust-library/ArchiveAutomation/ ClamAV Archivera to Bagger integration DROID JHOVE Iteration Iteration 1: Making the case for a dedicated digital preservation service • High level • Designed to justify investment in a digital preservation service to Library Management and University IT • Minimal detail • Workflow components based on existing KAUST IT Infrastructure Iteration 2: Comprehensive preservation processing workflow • Internal documentation • Represents the end to end preservation workflow process, from appraisal to long-term preservation • Pre-Preservica implementation Iteration 3: Preservation ingest workflow • Internal documentation • Maps the first series of steps in the long-term preservation process • Working documentation for digital preservation processing procedures • Ideal workflow for identifying workflow process appropriate for automation Future Work Continued automation of the preservation workflow Identify and assess redundant processes in the preservation workflow
A History Of Changes Millennial Sea Level In The Southwestern AtlanƟc Karla Rubio-Sandoval, Timothy Shaw, Ben Horton, Nicole Khan, Augusto Luiz Ferreira-Júnior, Rodolfo Angulo, Guilherme Lessa, Maria CrisƟna de Souza, MaƩeo Vacchi, Deirdre Ryan, SebasƟan Richiano, Evan Gowan, Ciro Cerrone, Paolo Stocchi, Thomas Lorscheid, Thomas Felis, Ann-Kathrin Petersen, & Alessio Rovere * LIG: SL values from ~5.6 to 20 m a.s.l. in the continent and from ~2 to 10 m a.s.l. in the islands. * HOL: Mid-Holocene maximum transgression rising RSL from 2 to 4 m above its present level. * Relative sea level (RSL) changes result from the interactions between land motion and the global mean sea level. * The elevation of a sample is not the RSL. It requires quantifying other parameters associated with the mean sea level (MSL). Figure 2. Beach ridge indicaƟve range Level is NOT Sea Level! Results Holocene (HOL; ~11 ka – to present) Conclusions Figure 1. Example of a SL indicator. Modiﬁed from Rovere et al., 2016 The database ﬂow * Geographic location. * Accurate chronology. * Elevation of the sample with respect to a tidal datum. * The indicative meaning. Four main criteria: Last Interglacial (LIG; ~128-116 ka) The ﬁgures on this panel represent the RSLs during the LIG in the Southwestern AtlanƟc. How to study Paleo Sea Levels? * Sea level (SL) reconstructions are developed using sea-level indicators. * The past position of the sea level is deﬁned by sea-level index points or limiting data. Figures on this panel represent the RSLs during the HOL in the Southwestern AtlanƟc. A) Map showing locaƟons of records. B), C), & D) Sea level curves by area.
SGD and the Alliance of Genome Resources Stacia R. Engel, Edith D. Wong, Robert S. Nash, Felix Gondwe, Kevin A. MacPherson, Patrick Ng, Suzi Aleksander, Stuart Miyasato, J. Michael Cherry, and The SGD Project Department of Genetics, Stanford University, Stanford, CA 94305, USA The yeast research community has long enjoyed the support provided by the Saccharomyces Genome Database (SGD), and has flourished because of its existence, making great breakthroughs and technological advances, and contributing countless key insights to the fields of genetics and genomics over the past decades. SGD has recently joined forces with five other model organism databases (MODs) - WormBase, FlyBase, ZFIN, RGD, and MGI - plus the Gene Ontology Consortium (GOC) to form the Alliance of Genome Resources (the Alliance; alliancegenome.org). The Alliance website integrates expertly-curated information on model organisms and the functioning of cellular systems, and enables unified access to comparative genomics and genetics data, facilitating cross-species analyses. The site is undergoing rapid development as we work to harmonize various datatypes across the various organisms. Explore your favorite genes in the Alliance to find information regarding orthology sets, gene expression, gene function, mutant phenotypes, alleles, disease associations and more! The Alliance is supported by NIH NHGRI U24HG002223-19S1, NIH NHGRI U41HG001315 (SGD), NIH NHGRI P41HG002659 (ZFIN), NIH NHGRI U24HG002223 (WormBase), MRC-UK MR/L001020/1 (WormBase), NIH NHGRI U41HG000739 (FlyBase), NIH NHLBI HL64541 (RGD), NIH NHGRI HG000330 (MGD), and NIH NHGRI U41HG002273 (GOC, which also provides funding to WB, MGD, SGD). Goal: develop and maintain sustainable genome information resources that facilitate the use of diverse model organisms to understand the genetic and genomic bases of human biology, health, and disease Yeast, human, and model organism orthologs ! Alleles and phenotype variants ! Disease associations ! Expression sgd-helpdesk@lists.stanford.edu ! @yeastgenome @alliancegenome ! help@alliancegenome.org
The dataset can be downloaded at https://openneuro.org/datasets/ds004215 RESULTS 1. Data cleaning is a non-trivial step in curating FAIR datasets. 2. Systematically searching for data errors is better than incidentally discovering them.1 3. Documenting data decisions with reasoning is key to isolating the source of errors. Data curation of the NIMH Healthy Volunteer dataset Arshitha Basavaraj1, Eric Earl1, Travis Riddle1, Jeﬀrey D. Stout2, Anna M. Namyst2, Adam Thomas1, Allison C. Nugent2, Joyce Chung3 1Data Science & Sharing Team, NIMH, 2Magnetoencephalography Core Facility, NIMH, 3Oﬃce of the Clinical Director, NIMH TAKEAWAYS ●Resolving data problems once they have occurred is referred to as data cleaning.1 ●About 80% of the time preparing a dataset is spent on data cleaning.2 ●Here we share some curation good practices based on our experience curating the NIMH Healthy Volunteer dataset.3 INTRODUCTION METHODS ●Four types of data collected ○Online questionnaires ○Clinical Assessments ○Magnetic Resonance Imaging (MRI) ○Magnetoencephalography (MEG) ●Raw data was cleaned, and transformed to BIDS4-6 standard format using semi-automated scripts. All manual steps were documented to ensure reproducibility. ●Data cleaning phases can be divided into error detection, diagnosis and treatment. ●Errors were detected through exploratory analysis tools such as: ○Summary statistics ○Outlier detection ○Expected variable ranges ●Cleaning tabular data7 required the most eﬀort due to ○Loss of data provenance1,8 ○Manual data entry8 errors 1. Compile a version-controlled list of participants for each type of data. 2. Maintain a change log to document modiﬁcations to data collection processes. 3. Communicate frequently with study collaborators to speed up discovery and correction of data inaccuracies. 4. Allocating resources to monitor and clean data at every stage of data ﬂow can greatly reduce errors. RECOMMENDATIONS Scan the QR code for references and other links or visit: See also posters: ➔#1283 for more speciﬁcs on the study protocol. ➔#2464 for the phenotype standardization software tool. linktr.ee/arsh.b Fig. 1: Flow of data from collection to sharing. Fig. 2: Phases of data cleaning Detect Diagnose Fix
A Schoolyard to Play H. Guerra1, N. Cortes1, C. Santos1, students from class 4.º I1 1Escola Básica da Quinta dos Franceses, Agrupamento de Escolas Dr. António Augusto Louro, Seixal, Portugal Abstract This is an entrepreneurship project that promotes awareness, as its main action is to make the school more appealing. Students have raised this problem, and identified the main goal of improving the schoolyard spaces. Problems were surveyed and solutions presented, with the focus of intervention in green spaces, new paintings and organization of leisure spaces. Contacts were initiated with different stakeholders, for the potential implementation of the solutions found in this project. Keywords Awareness; biodiversity; cooperation; entrepreneurship; responsibility; schoolyard.