update BHD2025

_includes/clients.html

@@ -14,7 +14,7 @@
                 </div>
     		<div class="col-md-3 col-sm-6">
                     <a href="#">
-                        <img src="img/logos/EHU_logo.svg" class="img-responsive img-centered" alt="">
+                        <img src="img/logos/EHU_logo_updated.svg" class="img-responsive img-centered" alt="">
                     </a>
                 </div>
 

_includes/program.html

@@ -95,16 +95,16 @@ <h2 class="white mb-3">
     </tr>
     <tr>
         <th>17:00 - 17:30</th>
-        <td rowspan="2">Project Time</td>
         <td rowspan="3">Project Time</td>
-        <td>Project Results</td>
+        <td rowspan="3">Project Time</td>
+        <td rowspan="2">Project Results</td>
     </tr>
     <tr>
         <th>17:30-18:00</th>
     </tr>
     <tr>
         <th>18:00-18:30</th>
-        <td>Closing Event</td>
+        <td>Closing Remarks</td>
     </tr>
     <tr>
         <th>18:30 Onwards</th>

_posts/2023-11-25-project-1.markdown

@@ -13,9 +13,13 @@ category:
 description: "Masters’ and PhD candidates earn their degree by completing a thesis, which typically contains one or more research articles. Yet, along the research path, researchers may create other outputs (e.g., protocols, methods, data, code), use reproducible and transparent practices (e.g., evidence synthesis, reporting guidelines, use of unique identifiers) and engage academics and non-academics to develop, conduct and disseminate (e.g., public engagement) the research. Implementing and sharing these practices and outputs accelerates progress by facilitating reuse, reproducibility and replication. To change research practice and culture, however, we must recognize and reward researchers for sharing more than research articles. 
 In this implementation-focused opt-in pilot program, we aim to offer University of Coimbra Masters’ and PhD candidates a formal reward for implementing reproducible, reusable and open research practices in their thesis research. We are co-creating the reward criteria with a Local Advisory Board (graduate students, course coordinators, and supervisors), with advice from an expert External Board. 
 The criteria include: (a) list of practices (e.g., reporting of null results, author contributions statements) and outputs (e.g., reusable step-by-step protocols, materials) from which the students can select, (b) assessment criteria for each practice/output (focus on quality), and (c) number of practices/outputs that must be implemented. The criteria are designed to be adaptable to different disciplines and projects. 
-The program will open in mid-2025. We will monitor participation, selected practices and outputs, and disciplines, for program improvement. In this talk we will present the program and share lessons learned during its development and implementation."
+The program will open in mid-2025. We will monitor participation, selected practices and outputs, and disciplines, for program improvement. In this talk we will present the program and share lessons learned during its development and implementation.<br />
+
+<br />
+<strong> About </strong><br />
+Inês A. T. Almeida is a researcher at the University of Coimbra with a background in psychology and neurosciences, now focusing on meta-research. She completed a PhD in Health Sciences – Biomedical Sciences at the Faculty of Medicine, University of Coimbra, following a licentiate degree in psychology with specialization in psychological assessment, counselling, and rehabilitation. In 2024, Inês joined the ERA Chair project EXCELScIOR at the Center for Neuroscience and Cell Biology (CNC) to establish meta-research in Coimbra, promoting more transparent, reliable, and socially responsive science. Alongside, she has been active in Open Science, co-founding the Open Science Community Coimbra, contributing to Eurodoc’s input to the UNESCO Recommendation on Open Science, and participating in international training and networks."
+
 
-bio: ""
 
 ---
 ---

_posts/2023-11-25-project-2.markdown

@@ -29,9 +29,11 @@ assigning priors to conditional means is often conceptually and practically easi
 assigning them to di@erences from a reference. This talk explores these three
 approaches—indicator coding, sum coding, and indexing—and discusses their
 implications for model interpretation and communication of results, both in Frequentist
-and Bayesian frameworks."
+and Bayesian frameworks.<br />
 
-bio: ""
+<br />
+<strong> About </strong><br />
+Iñigo earned his BAs in Translation and Interpreting, and in Basque Studies at the University of the Basque Country, where he also completed an MA in Theoretical and Experimental Linguistics. He is currently finishing a joint PhD in Linguistics at the University of the Basque Country and the University of Pau and the Adour Region. His dissertation analyses phenomena related to nasality in Zuberoan Basque from a phonetic perspective, focusing on the prenasalization of word-initial voiced stops and the loss of nasality in formerly nasalized vowels. During his PhD, he developed a strong interest in statistics and programming, and has taught several introductory courses on statistics for linguists and cognitive scientists. More recently, he has focused on Bayesian statistical approaches, which he values, among others, for their natural capacity to investigate both between and within-individual variability."
 
 
 ---

_posts/2023-11-25-project-3.markdown

@@ -18,7 +18,11 @@ description: "The storage and processing of large datasets, including neuroimagi
 - How to cut back on unnecessary computing <br />
 - Current methods for tracking computing energy usage and carbon emissions <br />
 
-The content discussed here will include specific examples from neuroimaging research, including how one can reduce the carbon footprint of preprocessing in fMRIPrep, and the effect of fMRI software choice on energy usage. The messages and approaches discussed here will also apply to any discipline requiring the processing of large amounts of data, beyond neuroimaging alone."
+The content discussed here will include specific examples from neuroimaging research, including how one can reduce the carbon footprint of preprocessing in fMRIPrep, and the effect of fMRI software choice on energy usage. The messages and approaches discussed here will also apply to any discipline requiring the processing of large amounts of data, beyond neuroimaging alone. <br />
+
+<br />
+<strong> About </strong><br />
+Nick is a postdoctoral researcher in the School of Psychology at the University of Sussex. In recent years, his work has focused on measuring and reducing the carbon footprint of computing required in human neuroimaging research, with a specific focus on functional magnetic resonance imaging (fMRI). This has included generating evidence-based recommendations aimed at multiple stages of the research process, from study planning, to data processing, to data dissemination. Nick has delivered workshops focused on green computing in relation to neuroimaging specifically and to research high-performance computing more generally. Nick's current work also focuses on the effect of working time reduction and long working hours, including novel collection in the Sussex 4 Day Week study and analysis of UK Biobank data."
 
 
 

_posts/2023-11-25-project-4.markdown

@@ -12,7 +12,12 @@ project-date: November 2025
 category:
 description: "Deep learning has made major progress in natural language processing. Beyond these technical performance, these algorithms offer new methods to understand and model how language is processed in the human brain. <br />
 Using both encoding (representation -> brain) and decoding (brain -> representations), we show that the comparison between modern speech and language models effectively accounts for brain responses to natural speech as recorded with EEG, MEG, iEEG and fMRI, including in children between 2 and 12 years old. <br />
-This systematic comparison provides an operational foundation to model language in the adult and developing brain, and thus offers a new path to understand the neural and computational bases of this human-specific ability."
+This systematic comparison provides an operational foundation to model language in the adult and developing brain, and thus offers a new path to understand the neural and computational bases of this human-specific ability. <br />
+<br />
+<strong> About </strong><br />
+Jean-Rémi King is a CNRS researcher at École Normale Supérieure currently detached to Meta AI, where he leads the Brain & AI team. This team aims to identify the brain and computational bases of human intelligence, with a focus on language. For this, they develop deep learning algorithms to decode and model brain activity recorded with MEG, EEG, electrophysiology and fMRI."
+
+
 
 ---