The success of cellular therapies in acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) indicates leukemic cells are immune-sensitive when targeted appropriately. In a phase Ib clinical trial (NCT03066648), we profiled longitudinal bone marrow and peripheral blood samples from AML/MDS patients treated with the anti-TIM3 antibody sabatolimab in combination with the hypomethylating agent decitabine,employing single cell RNA+TCRαβ sequencing and flow cytometry alongside functional co-culture assays.Unlike CTLA4 and PD1, which are primarily restricted to T cells, TIM3 was broadly expressed across NK, myeloid and T cell populations. Therapy induced expansion of cytotoxic CD56dim and adaptive NK cell subsets, accompanied by robust type I interferon signaling, which was associated with reduced risk of relapse. Notably, phenotypically exhausted CD8+ T cells constituted a minor fraction (<1%) of bone marrow T cells, and response to therapy was not marked by reinvigoration of pre-existing expanded clones. Instead, responders exhibited an expansion of previously minor T cell clones, particularly oligoclonal cytotoxic CD4+ T cells. A patient who achieved a durable complete remission (>2 years) harbored CD4+ T cell large granular lymphocyte leukemia (T-LGLL) cells bearing TCRs targeting autologous AML blasts demonstrated by functional co-culture assays.

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The success of cellular therapies in acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) indicates leukemic cells are immune-sensitive when targeted appropriately. In a phase Ib clinical trial (NCT03066648), we profiled longitudinal bone marrow and peripheral blood samples from AML/MDS patients treated with the anti-TIM3 antibody sabatolimab in combination with the hypomethylating agent decitabine,employing single cell RNA+TCRαβ sequencing and flow cytometry alongside functional co-culture assays.Unlike CTLA4 and PD1, which are primarily restricted to T cells, TIM3 was broadly expressed across NK, myeloid and T cell populations. Therapy induced expansion of cytotoxic CD56dim and adaptive NK cell subsets, accompanied by robust type I interferon signaling, which was associated with reduced risk of relapse. Notably, phenotypically exhausted CD8+ T cells constituted a minor fraction (<1%) of bone marrow T cells, and response to therapy was not marked by reinvigoration of pre-existing expanded clones. Instead, responders exhibited an expansion of previously minor T cell clones, particularly oligoclonal cytotoxic CD4+ T cells. A patient who achieved a durable complete remission (>2 years) harbored CD4+ T cell large granular lymphocyte leukemia (T-LGLL) cells bearing TCRs targeting autologous AML blasts demonstrated by functional co-culture assays.

Posterior samples and mass-radius contours for the headline result of the millisecond pulsar PSR J0614−3329, whose analysis is presented in "A NICER view of the 1.4 M⊙ edge-on pulsar PSR J0614−3329" by Mauviard et al. (2025; arxiv:2506:14883)Also included are: supplementary figures, as well as data and reproduction package of all the X-PSI and NEoST runs.Please refer to the README for detailed information.

IntroductionThis is a dataset for: "SESR-Eval: Dataset to Evaluate LLMs in the Screening Process of Systematic Reviews".Folder structuredataThe data-folder contains:- Initial replication package selection (1-replication-package-selection)- Inter-rater reliablity agreement for replication package selection (2-replication-package-selection-reliability-agreement)- Processed replication packages (3-processed-data)- Replication packages are omitted due to size constraints, but are downloadable via provided links- LLM results (4-llm-results)- The SESR-Eval dataset (sesr-eval-dataset)     See: data/sesr-eval-dataset/README.mddocumentationThe documentation-folder contains miscellaneous documentation for the study.experimentsThe experiments-folder contains the LLM experiment source code.How to run the benchmarks?1. Install Python 32. Run python3 -m venv venv3. Run source venv/bin/acticate4. Run pip install -r requirements.txt5. Copy .env.example to .env6. Obtain:    1. Dataset (see data/sesr-eval-dataset/README.md)    2. OpenAI API key    3. Openrouter API key (if you wish to run other models than OpenAI)7. Run: ./run_experiments.shRequirements- Python 3Scopus API usageThe data was downloaded from Scopus API between January 1 and 18 July, 2025 via http://api.elsevier.com and http://www.scopus.com.LicenseThe replication package is licensed with the CC-BY-ND 4.0 license. Each dataset secondary study has their own license. However, Elsevier has their own terms and conditions regarding the use of our research data: ---- This work uses data that was downloaded from Scopus API between Jan 1 and Apr 24, 2025 via http://api.elsevier.com and http://www.scopus.com.Elsevier allows access to the Scopus APIs in support of academic research for researchers affiliated with a Scopus subscribing institution.The end product here is a scholarly published work, that utilizes publications in Scopus for our research effort. We want to publish a scholarly work regarding Scopus data relationships.The data downloaded from Scopus API, for our work, is published to make work follow the practices of open science. It also makes possible to reproduce our work's results. Elsevier allows this use case under the following conditions, which our work meets:- The research is for non-commercial, academic purposes only.- The research is performed by approved representative of the applying institution.- The research is limited to the scope of Software engineering (SE) - we are not mining the entire Scopus dataset.- The retention of original research dataset is limited to archival purposes and reproduction of the research results.- Public sharing of data for purpose of reproducibility with a specific party is permissible upon written request and explicit written approval.- Scopus has been identified as the data source as described in the Scopus Attribution Guide. - If the user is a bibliometrician doing work outside this use case, they contact Elsevier's International Center for the Study of Research. The data is not displayed in a website or in a public forum outisde of the output format of the scholarly published work. The data is only stored in Zenodo, in this replication package.

This file system package contains data sets for the publication "Repulsive interatomic potentials calculated at three levels of theory" by K. Nordlund, S. Lehtola and G. Hobler. It presents three quantum chemically calculated data sets ("MP2", "DMol", and "ZBL pair-specific") for diatomic interatomic potentials in the repulsive region, where the separation of the atoms is so short that the potential energy is >> 10 eV. The set also contains the fitted parameters for analytical NLH repulsive potentials that consist of a Coulomb term multiplied by a three-exponential screening function.The version from Oct 9, 2025 has updated NLH parameters for the pair Na-O (Z1=8, Z2=11). Otherwise the data is identical to before.The MP2 data sets contain potential data for all elements pairs Z1+Z2<=36, and the DMol, ZBL pair-specific and NLH potentials are proved for all elements pairs Z1, Z2 <=92.The data sets are arranged in the following directories:mp2/      : Data sets for the Hartree-Fock Moller-Plesset2 level calculationsdmol/     : Data sets for the Density Functional Theory calculations with the DMOL codenlh/      : Coefficients for the fits of the Nordlund-Lehtola-Hobler potential to the DMol data setszbl/      : Data sets for the pair-specific Ziegler-Biersack-Littmark potential calculationszbluniv/  : A directory with a Linux bash/awk script that generates the ZBL universal potential.zblspec/  : Coefficients for the pair-specific ZBL screening functions as contained in SRIM-2013.Each subdirectory has its own README.txt file giving additional details on the content of the directory and its subdirectories.

This repository contains networks of people and documents mined from the database dump of Prosobab - an open access prosopography of Babylonia in the Neo-Babylonian and Persian periods (c. 620-330 BCE) at https://prosobab.leidenuniv.nl/ (Waerzeggers et al., 2019; Waerzeggers & Groß, 2022). The database was created by the ERC Consolidator Grant project Persia and Babylonia at Leiden University. The database can be browsed online but, as it is a university project, the continuance of the the site cannot be guaranteed.From the database, we extracted information on all cuneiform documents, persons, and their attestations in documents. From this data, we created several node and edge lists that are ready-made for social network analysis. These include co-occurrence networks of persons and cuneiform documents, and several directed networks representing various social relations between persons. Node and edge lists contain rich metadata that has been extracted from Prosobab.Folders- Networks: node and edge lists for social networks analysis.- ProsobabData: the data we extracted from the database, available as an Excel worksheet and TSV files.- Queries: the SQL queries used for extracting data from the database.- SupplementaryFiles: various files used in data processing.ReferencesWaerzeggers, C., Groß, M., et al. (2019). Prosobab: Prosopography of Babylonia (c. 620–330 BCE). Leiden University. https://prosobab.leidenuniv.nl.Waerzeggers, C., & Groß, M. (2022). Prosobab (version 1.0). DANS Data Station Archaeology. https://doi.org/10.17026/dans-zvn-eece.

Data and code used for analyses in the research publication 'Aesthetic values predict bird trade, but the association varies across product types and trade regions'.A detailed description on data sources, data editing, and modelling is in the research publication and its' appendix1: Haukka et al., 2025. Aesthetic values predict bird trade, but the association varies across product types and trade regions. Biological Conservation, https://doi.org/10.1016/j.biocon.2025.111572Data filesAll final merged datasets used in the analysis and figure codes are given as Excel (.xlsx) and .RData -files. Refer to the Excel files for variable descriptions.Global level trade databirdtrade_data_Haukkaetal_2025.xlsx includes the raw collated presence in trade data for all the bird species in global trade models, this file includes the full explanations for the variable namesbirdtrade_tradedonly_data_Haukkaetal_2025.xlsx includes raw collated trade data for those bird species that are traded in any product type globally, this file includes the full explanations of the variable namesbirdtradebirdtrade_tradedonly_data_Haukkaetal_2025_data_collated.Rdata includes the full global level data on trade presence or absence for all bird speciesBirdtrade_tradedonly_collated.Rdata included the full global level data on presence or absence of trade in different product types for the species which are tradedbirdtreelist.Rdata is a list used to match the data to have the exact same bird species in pglmm and glmm models (as taxonomy matching to the bird phylogenetic tree resulted in the removal of a few species from the data set).endemics_list.Rdata has the list of endemic species of  9 countries, totalling 1325 species. Australia (434 species), Brazil (340 species), Bolivia (29 species), Colombia (122 species), Costa Rica (15 species), Ecuador (59 species), India (95 species), New Zealand (94 species) and Papua New Guinea (137 species). The lists were obtained via the country level check-list search on the Avibase-database (2024). EU Level trade dataEU_birdtrade_data_Haukkaetal_2025. xlsx includes the raw collated presence in trade data for all the bird species in EU trade models, this file includes the full explanations for the variable namesEU_birdtrade_tradedonly_data_Haukkaetal_2025.xlsx includes raw collated trade data for those bird species that are traded in any product type into or within the EU, this file includes the full explanations of the variable nameseu_birdtrade_data_collated.Rdata includes full EU level data on the presence or absence of documented trade for all bird species between EU countries or imports into any EU country (refer to manuscript for data sources)EU_Birdtrade_tradedonly_collated.Rdata inclues full EU level data on the presence or absence of documented trade in different product types for those birds that are traded (refer to manuscript for data sources)birdstreelist_eu_alldata.Rdata is a list used to match the data to have the exact same bird species in EU level pglmm and glmm models (as taxonomy matching to the bird phylogenetic tree resulted in the removal of a few species from the data set).Model code filespglmm_code.R includes the modelling code for all pglmm models used in the analysis (note that the models were all run in separate codes on a supercomputer and each model takes a few days to run depending on the available computational power. Code is given in one file for ease of sharing.model_code_for_glmm.R includes the modelling code for all the glmm models used in analysis, this also includes code for figures 1 & 4 in the manuscript, and produces some of the additional data sets needed to run all pglmm modelsEU_trade_glmm_models.R includes modelling code for all the EU trade level glmm modelsbird_orders_subset_models.R includes supplementary code for running the bird order or other bird species subgroup level models, and producing figures from the resultsFigure code filesCode for figures 1 & 4 is included in the model_code_for_glmm.R fileFigure2_code.R & estimates_fullmodels.csv to reproduce figure 2Figure3_code. R to reproduce the phylogeny plot in figure 3data_distribution_plots.R is used for figures A1-A6 in the appendixbird_orders_subset_models.R includes code for figures A7-A8 in the appendixEU_trade_glmm_models.R includes the code to produce figure A9 in the appendix

This repository documents the models developed under the PathFinder project (WP3.2), specifically the subtask Forest Structure and Living Biomass Carbon, focusing on forest growth and management modelling for scenario analysis. A pan-European scenario based on RCP4.5 and SSP2 for the period 2020–2050 is provided to demonstrate the modelling workflow, which combines the process- and data-based hybrid forest simulator FORMIT-M, the European Forestry Dynamics Model (EFDM), and the soil carbon model Yasso. FORMIT-M simulates forest development at the plot level, accounting for climate change impacts via the maximum annual gross primary productivity (maxGPP) parameter, with pre-calculated maxGPP values corresponding to RCP2.6, RCP4.5, and RCP8.5 across Europe. EFDM is a transition matrix model that simulates forest area dynamics based on management activity probabilities and transition probabilities, parameterized using paired permanent National Forest Inventory (NFI) plot data and FORMIT-M outputs. EFDM activity probabilities at the 1 km² resolution were derived using present-day forest management intensity maps generated with the CLUMondo land-use model. Initial forest area distributions for 2020 were based on analysis of high-resolution (10 m²) satellite imagery and NFI data. Biodiversity indicator calculation, including species richness and the Shannon index, were incorporated into the EFDM simulations. Variation in forest structure and growth patterns across European continent is accounted for by using ten tree species groups and three sub-regions (i.e. North, Central, and South Europe). Yasso is soil carbon model, in which decomposition of various carbon input components is modelled as a function of climate, chemical composition, and the size of woody litter elements. EFDM scenario outputs were processed into Yasso model inputs following the completion of a full EFDM simulation. Pan-European EFDM scenario for the period of 2020-2050 was created following CLUMondo RCP45 + SSP2 forest management storyline. Documentation and the complete FORMIT-M - EFDM - Yasso framework code is provided for modelling future forest structure and living biomass carbon at a resolution of 1 km2 over Europe.