Sandra Jones is pro vice-chancellor of engagement at Australian Catholic University in Melbourne, Australia. As an academic, she has researched autistic adolescent development, public understanding and acceptance of autism, and autistic people’s lived experiences of inclusion and exclusion. As an autistic woman and the mother of two adult autistic sons, she is a passionate advocate for the inclusion of autistic people in all aspects of society.

Sandra Jones
Pro vice-chancellor of engagement, Australian Catholic University
From this contributor
How the loss of Asperger syndrome has lasting repercussions
Some people who have lost the diagnosis of Asperger syndrome say they feel a loss of identity and worry about a loss of services.

How the loss of Asperger syndrome has lasting repercussions
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Sharing Africa’s brain data: Q&A with Amadi Ihunwo
These data are “virtually mandatory” to advance neuroscience, says Ihunwo, a co-investigator of the Brain Research International Data Governance & Exchange (BRIDGE) initiative, which seeks to develop a global framework for sharing, using and protecting neuroscience data.

Sharing Africa’s brain data: Q&A with Amadi Ihunwo
These data are “virtually mandatory” to advance neuroscience, says Ihunwo, a co-investigator of the Brain Research International Data Governance & Exchange (BRIDGE) initiative, which seeks to develop a global framework for sharing, using and protecting neuroscience data.
Cortical structures in infants linked to future language skills; and more
Here is a roundup of autism-related news and research spotted around the web for the week of 19 May.

Cortical structures in infants linked to future language skills; and more
Here is a roundup of autism-related news and research spotted around the web for the week of 19 May.
The BabyLM Challenge: In search of more efficient learning algorithms, researchers look to infants
A competition that trains language models on relatively small datasets of words, closer in size to what a child hears up to age 13, seeks solutions to some of the major challenges of today’s large language models.

The BabyLM Challenge: In search of more efficient learning algorithms, researchers look to infants
A competition that trains language models on relatively small datasets of words, closer in size to what a child hears up to age 13, seeks solutions to some of the major challenges of today’s large language models.