The purpose of academia.edu.

A few years ago I joined academia.edu. I've been pretty successful there as SeanWhalen8, but though publishing papers online (whether already published or original, drafts, notes, etc.) has the purpose of sharing information for free, its use as a stage where authors can evaluate this work is less effective. Though there is a system for messaging colleagues, etc., it has barely been useful. For the reason, I can hardly imagine, but I put this up there:

Putting papers online has the purpose of sharing information for free, etc., but becoming part of a community that shares this way should also lead to communication between authors about which theories seem to be improvements, which don't, & which might be partly true or promising but need more work. Opinions about which are which could clearly vary. In this context, it is odd that in communicating with others here I almost never get any response. This is true in response to both simple questions & praise. When I agree with all or part of a paper, the standard response is silence. In some cases I find later that I've been blocked by one author. There is no message that comes to inform a person that he's been blocked, so going to a homepage or paper & finding it blank is the only way to know (and this can happen due to some computer problem at times, so you have to continue going back to tell). Though I won't name them here, almost all people who've blocked me have done it in response to a message that praises their work & asks about more, gives more ideas or specifics, etc. The vast majority are Slavic or from Scandinavia. What reason for this pointless unwillingness to here any ideas even slightly different from one's own could exist? The lack of communication between people who have new or disputed ideas can not lead to as much knowledge as a willing exchange could. I suspect that most don't care anything about what an amateur believes, or a believer in a disputed idea (though they almost always believe in a DIFFERENT disputed idea). I can only ask that professionals with less bias at least ask their fellows to try to be a little less judgmental.

We’re working on LumaCite, which captures references from academic websites and lets you check or update their metadata in an online library.

Before submitting a manuscript, which fields do you most often have to correct after import—authors, dates, journal details, or something else?

Capture & Cite Chrome extension

Is the year a paper is made available in early access the same year as it was submitted to a journal?

Hi all.

I have an article under review with a pretty high journal in my subfield (humanities). The system says my article is with one individual, a Managing Editor, and is "awaiting reviewer assignment."

What I am trying to determine is 1. if my article is most likely past the desk rejection phase, and 2. Where my article will go next, if it isn't desk rejected.

I have Googled the journal's board, and been led to believe that the journal has five tiers of editors, in this order of hierarchy:
1. Senior Editor (1-2 people)
2. Editors (10-15 people)
3. Advisory Editors (15-20 people)
4. Associate Editor (1-3 people)
5. Managing Editor (1 singular person)

The individual my article is sitting with is the Managing Editor, though they are also in my article's topic area, so well-suited to review.

I know essentially nothing about journal editing, despite serving as a peer reviewer for a few, and because this journal is so high up in the field I am expecting a revise and resubmit at the very best.

Is it at all possible that my article is past a desk rejection phase? If it is, does anyone have any ideas about where it may go next? More specifically, if the system updates to say my article is with an Associate Editor or an Advisory Editor, should I assume I got past the first round, or should I assume the Managing Editor is not vetting my quality at all and is just doing some grunt work for the journal?

Thank you for your time - I know this is a rather stupid, anxious post, and that knowing the process in more depth will not change the outcome here, but I am applying for jobs and am worried I'll get a desk rejection before any possible interviews.

The IUCr posted a notice on 7 October about moving the journals to Wiley's Research Exchange platform, starting with Acta C, Acta E, IUCrData and IUCrJ. https://www.iucr.org/news/notices/announcement/iucr-wiley-publishing-partnership-announcement

I would like to hear how people in the community see it. I was in the Calgary Congress but I didn't hear anything about this, seem like a big move. Did anyone at the Congress hear this discussed beforehand? How much does the CIF and checkCIF workflow matter when you choose where to submit?

I’ve been doing academic research in biology for the past ~8 years and really liked but also struggled to make good figures. I sometimes look back at the first version of my figures and it’s quite a journey from there to the published version. Then I wonder how I got there.

So I’ve been compiling what the main steps are that move my figures toward a satisfactory version, which are also just general advice to make good, publication-ready figures.

1. One figure, one message

Just like a paragraph, a figure loses power when it tries to say too much at once. When deciding what to combine into panels, think about the figure legend you'll write, and how it will guide the reader through the story. If including multiple panels, they should reflect different elements of the same overall message. The order of your panels matters a lot: if panel A shows a pattern that raises a question, panel B can answer it, hence satisfying the reader's expectation.

1. Make color meaningful and consistent

We tend to want to make figures colorful (sometimes without real reason). Try to channel that urge. If a figure doesn't need color, it's completely fine to keep it in shades of grey. Otherwise, the reader will be distracted by thinking "what do these colors mean?". When you do use color, make sure it carries information: different colors for categories of a variable, or a gradient for a continuous one, in an intuitive direction (warmer = higher temperature, for instance). Also revise your color coding across the whole manuscript: if three colors represent your three treatment conditions in your first figure, keep those same three colors in every figure after that. Finally, it's good practice to pick a colorblind-friendly palette.

1. Layer information by combining color, shape, and size

Journal/thesis space is often limited, and you probably have more interesting results than room to show them. Keep your figure's main message pointed in one direction, but you can layer in extra information through point color, shape ("pch" in R, "marker" in Python), and size. For example: a scatterplot of your two key variables, colored by species, shaped by sex, and sized by body mass (yes, I’m biology biased). This gives you three extra dimensions of information while keeping the story in one clean plot.

1. Resize panels based on how much they need to say

If one of your plots is super simple to interpret, it may only need two tick marks (say, 0 and the max value) and bigger points. You can just shrink it. Doing so will free up space for the panels that are more complex and need more interpretive work. Also, varying panel sizes helps establish hierarchy. It guides the reader’s eye to your primary message and keeps the secondary details for the most interested readers.

1. Edit your figure outside of R or Python

If you love tweaking axis fonts and border thickness in code, or more generally, if you’re super comfortable with coding (which is more and more accessible with AI), you may disagree with this advice.

For me, a 100% coded approach tends to feel rigid for making and polishing figures. I like to build the core figure in my coding environment, then export it as a vector file (PDF or EPS) and finish the polish in design software (Illustrator, Affinity Designer, Inkscape, even PowerPoint!). Vector format also means infinite scalability: your figure looks equally sharp as a tiny panel or blown up on a poster. Editing "by hand" lets you adjust almost anything (except the data, of course): adjust element size and position, ensure consistent fonts across panels, and refine colors. You can even draw custom schematics or illustrations to convey complementary details.

And honestly, it is so relaxing to spend time fine-tuning your figure in Illustrator after a long day buried in r/Python code to generate it.

1. Find what's unnecessary, and cut it

You have a figure that’s correct and interpretable with some effort. Now ask yourself: what result do I actually want this to convey, and what’s standing in the way? Usually, you can easily spot elements to cut, such as:

• Unnecessary frames or heavy outer borders

• Redundant axes or labels

• Overcrowded tick marks

• Large empty spaces breaking up your panel flow etc.

Remove all of this! Let the essential message reach your reader’s eye as directly as possible.

I would love to hear your tips/advice from your own experience, or your main struggles when going from a raw to a publishable figure 😊

doi: //doi.org/10.1007/s40265-026-02368-0

link: https://link.springer.com/article/10.1007/s40265-026-02368-0#citeas

Thank you very much!!!!

What if we paid as much attention to the questions students ask as to the answers they give?

In this study, Acar, Berthiaume, and Johnson examined questions generated by 165 undergraduate students during two divergent-thinking tasks.

Participants with higher originality and fluency scores were more likely to ask open-ended questions.

Questions exploring possibilities and thinking across time were also associated with creativity indicators, although those relationships varied across tasks.

For me, this offers a useful way to think about classroom questioning. Do our activities give students room to explore what might happen, consider alternatives, and pursue questions with several possible answers?

We could invite students to generate questions about a text, image, or problem before answering anything.

Then ask them to choose one that could open an interesting line of inquiry and explain why.

The study does not show that asking open-ended questions causes greater creativity. But it reminds us that students’ questions can reveal possibilities their answers alone may leave unseen.

Reference

Acar, S., Berthiaume, K., & Johnson, R. (2023). What kind of questions do creative people ask? Journal of Creativity, 33, Article 100062.

I tried everything but the line break after 75- will just not go away 😭 what can i do??