Photon-assisted stochastic resonance in nanojunctions

This open-data repository contains the raw data files and the Python script to reproduce the figuresfrom the manuscript: M. Ridley, L. Bellassai, M. Moskalets, L. Kantorovich, and R. TuovinenPhoton-assisted stochastic resonance in nanojunctionsarXiv:2501.05124 (2025)https://doi.org/10.48550/arXiv.2501.05124 All calculations were performed using the open-source QPORT package:https://www.bitbucket.org/rtuovine/qport The comments in the Python script (plot.py) will guide the reader through the raw data files

Dataset related to article "Lipid-loaded tumor-associated macrophages sustain tumor growth and invasiveness in prostate cancer"

This record contains raw data related to article “Lipid-loaded tumor-associated macrophages sustain tumor growth and invasiveness in prostate cancer" Abstract Tumor-associated macrophages (TAMs) are correlated with the progression of prostatic adenocarcinoma (PCa). The mechanistic basis of this correlation and therapeutic strategies to target TAMs in PCa remain poorly defined. Here, single-cell RNA sequencing was used to profile the transcriptional landscape of TAMs in human PCa, leading to identification of a subset of macrophages characterized by dysregulation in transcriptional pathways associated with lipid metabolism. This subset of TAMs correlates positively with PCa progression and shorter disease-free survival and is characterized by an accumulation of lipids that is dependent on Marco. Mechanistically, cancer cell-derived IL-1β enhances Marco expression on macrophages, and reciprocally, cancer cell migration is promoted by CCL6 released by lipid-loaded TAMs. Moreover, administration of a high-fat diet to tumor-bearing mice raises the abundance of lipid-loaded TAMs. Finally, targeting lipid accumulation by Marco blockade hinders tumor growth and invasiveness and improves the efficacy of chemotherapy in models of PCa, pointing to combinatorial strategies that may influence patient outcomes

AusTraits: a curated plant trait database for the Australian flora

AusTraits is a transformative database, containing measurements on the traits of Australia's plant taxa, standardised from hundreds of disconnected primary sources. So far, data have been assembled from > 300 distinct sources, describing > 500 plant traits and > 34,000 taxa. To handle the harmonising of diverse data sources, we use a reproducible workflow to implement the various changes required for each source to reformat it suitable for incorporation in AusTraits. Such changes include restructuring datasets, renaming variables, changing variable units, changing taxon names. While this repository contains the harmonised data, the raw data and code used to build the resource are also available on the project's GitHub repository, https://github.com/traitecoevo/austraits.build/. Further information on the project is available at the project website austraits.org and in the associated publication (see below). CONTRIBUTORS The project is jointly led by Dr Daniel Falster (UNSW Sydney), Dr Rachael Gallagher (Western Sydney University), Dr Elizabeth Wenk (UNSW Sydney), and Dr Hervé Sauquet (Royal Botanic Gardens and Domain Trust Sydney), with input from > 300 contributors from over > 100 institutions (see full list above). The project was initiated by Dr Rachael Gallagher and Prof Ian Wright while at Macquarie University. We are grateful to the following institutions for contributing data Australian National Botanic Garden, Brisbane Rainforest Action and Information Network, Kew Botanic Gardens, National Herbarium of NSW, Northern Territory Herbarium, Queensland Herbarium, Western Australian Herbarium, South Australian Herbarium, State Herbarium of South Australia, Tasmanian Herbarium, Department of Environment Land Water and Planning Victoria and the Royal Botanic Gardens Victoria. AusTraits has been supported by investment from the Australian Research Data Commons (ARDC), via their "Transformative data collections" (https://doi.org/10.47486/TD044) and "Data Partnerships" (https://doi.org/10.47486/DP720, https://doi.org/10.47486/DP720A) programs; and grants from the Australian Research Council (FT160100113, DE170100208, FT100100910) and Macquarie University, The ARDC is enabled by National Collaborative Research Investment Strategy (NCRIS). ACCESSING AND USE OF DATA The compiled AusTraits database is released under an open source licence (CC-BY), enabling re-use by the community. A requirement of use is that users cite the AusTraits resource paper, which includes all contributors as co-authors: Falster, Gallagher et al (2021) AusTraits, a curated plant trait database for the Australian flora. Scientific Data 8: 254, https://doi.org/10.1038/s41597-021-01006-6 In addition, we encourage users you to cite the original data sources, wherever possible. Note that under the license data may be redistributed, provided the attribution is maintained. The downloads below provide the data in two formats: austraits-X.X.X.zip: data in plain text format (.csv, .bib, .yml files). Suitable for anyone, including those using Python. austraits-X.X.X.rds: data as compressed R object. Suitable for users of R (see below). austraits-X.X.X-flattened.rds: contains a flattened version of the dataset for direct loading in R; all data tables are joined into a wider format austraits-X.X.X-flattened.parquet: contains a flattened version of the dataset in parquet format; all data tables are joined into a wider format  For R users, access and manipulation of data is assisted with the austraits R package. The package can both download data and provides examples and functions for running queries.STRUCTURE OF AUSTRAITS The compiled AusTraits database contains a series of relational tables and files. These elements include all the data, contextual information submitted with each contributed datasets, database schema, and trait definitions. The file dictionary.html provides the same information in textual format. Similar information is available at https://traitecoevo.github.io/traits.build-book/. CONTRIBUTING We envision AusTraits as an on-going collaborative community resource that: Increases our collective understanding the Australian flora; Facilitates accumulation and sharing of trait data; Builds a sense of community among contributors and users; and Aspires to fully transparent and reproducible research of the highest standard. As a community resource, we are very keen for people to contribute. Assembly of the database is managed on GitHub at https://github.com/traitecoevo/austraits.build/. Here are some of the ways you can contribute: Reporting Errors: If you notice a possible error in AusTraits, please post an issue on GitHub. Refining documentation: We welcome additions and edits that make using the existing data or adding new data easier for the community. Contributing new data: We gladly accept new data contributions to AusTraits. See full instructions on how to contribute at https://github.com/traitecoevo/austraits.build/. The AusTraits project received investment (https://doi.org/10.47486/TD044, https:// doi.org/10.47486/DP720, https://doi.org/10.47486/DP720A) from the Australian Research Data Commons (ARDC). The ARDC is funded by the National Collaborative Research Infrastructure Strategy (NCRIS)