Five of my palynology advertising tweets (have/will) introduce(d) researchers pushing forward the importance of palynology in contemporary organismal science. With four, I worked, and we published a good deal of papers including some pretty unique ones, which hopefully will provide templates for future cross-disciplinary research.
#FightTheFog (16) ancestors (3) animals (3) artwork (9) Austria (2) bad science (8) Beall's legacy (6) bias (3) biogeography (2) branch support (3) Bundestagswahl (6) comment (14) curiosities (1) data links (3) European (8) France (9) free science (5) funny things (3) Germany (10) how-to-analyse (6) in Deutsch (28) infographics (32) introduction (1) Ireland (1) Köppen-Geiger (3) Landtagswahlen (9) languages (5) lost science (3) not science (7) oddities (14) open access (1) open data (3) palaeontology (12) peer review (10) people (1) Philosophisches (5) phylo-networks (14) plants (14) politics (30) pollen (3) public interest (19) satire (10) scam (5) science-related (20) Sweden (4) terminology (4) tips (25) travelling (2) USA (18) Wahl-O-Mat (10)
One thing, full-blood scientists usually forget, is to advertise their work. Usually because they lack the time. I have plenty and started a series of daily threads on Twitter advertising palynological research. But my reach there is miniscule and the half-life of tweets is extremely short. Hence, this post series.
With the advances in sequencing, it has become easy to compile complete chloroplast genomes (plastomes) for plants. Given you have the money and workforce. The People's Republic of China is rich in both; hence, gene banks fill up with complete plastomes of tree genera, otherwise ignored by the scientific world. Such as maples (Acer). Beware the fully resolved trees.
One of my favourite hobbies as a tax-paid scientist was to check and groom, once in a while, my "impact". I still check, out of curiosity and vanity, and just noticed that my stag cracked the thirty. A little reflection of self-inflating impact with some tips for the game.
Somebody on the RAxML group posted this as a bold question, asking for tips how to speed up the analysis. Since I recently looked at virus genomes (infamous SARS group) myself, I have some ideas for this.
The new, faster and (meanwhile) very option-rich version of RAxML, RAxML-NG provides the full plethora of nucleotide substitution models, which can be confusing to the normal user. Hence, a practical tip based on my experiences with very different sets of nucleotide data (and from very different organisms).