Mendeley Data Showcase

This dataset contains annual macro-agricultural and economic data used to analyze the resilience of the Malian cotton sector to climate variability, global price fluctuations, exchange-rate movements, and subsidy policies over the period 1990–2023. The data support a Structural Vector Autoregression (SVAR) analysis examining the dynamic transmission of external and domestic shocks to real cotton producer income per hectare. The dataset includes five core variables: (i) real cotton income per hectare (deflated using Mali’s rural CPI); (ii) annual rainfall as a proxy for climatic conditions; (iii) the Cotlook A Index as a measure of world cotton prices; (iv) the FCFA/USD exchange rate capturing macro-monetary exposure; and (v) input subsidies per hectare reflecting agricultural policy support. All monetary variables are expressed in real terms, and logarithmic transformations are applied where appropriate to facilitate elasticity interpretation and reduce heteroskedasticity. The dataset is designed to enable replication of the empirical results presented in the associated manuscript and to support further research on income dynamics, price transmission, and policy effectiveness in export-oriented agricultural systems in Sub-Saharan Africa.

This dataset was created in 2025 and consists of 643 synthetic audio samples generated using the Minimax.io platform. It includes both text-to-speech (TTS) and voice-to-voice (V2V) synthetic speech. Specifically, the dataset contains 603 TTS audios and 40 V2V audios. The material spans two languages—Spanish and English—and includes 302 female voices, with the remaining samples corresponding to male voices. This dataset enables multiple downstream applications, including: (1) the development and training of models for synthetic-audio detection, (2) the external benchmarking of audio-deepfake classification systems, and (3) the assessment of model robustness by introducing adversarial or signal-level perturbations to the audio samples.

Biomass pyrolysis has promising potential as an alternative to fossil-based compounds, but the high content of oxygenates in the liquid product limits its application on a large scale. To address this issue, alternatives have been proposed, such as the use of a catalyst and changes in the reaction atmosphere to reduce the oxygenate content of the liquid fraction. However, the challenges lie in understanding the reactions that occur during the process using experimental data to propose robust and accurate reaction schemes. Here, we present analytical pyrolysis data (Py-GC/MS) for Chilean oak (Nothofagus obliqua) torrefied and non-torrefied under four different conditions: Torr1 (523 K, 30 min), Torr2 (573 K, 15 min), Torr3 (573 K, 30 min), and Torr4 (523 K, 15 min). The tests were carried out in oxygen-depleted (He) and reductive (H₂) atmospheres over a catalyst based on natural Chilean zeolite loaded with 2 wt.% or 5 wt.% Zn or Ga. The catalysts were prepared via ion exchange, first with ammonium sulphate to remove the compensation cations from the zeolite, and then with precursor metal salts (nitrates) for metal loading. The data provided includes characterization of both untreated and torrefied biomass, as well as catalysts. Thermogravimetric analyses were also performed on the biomass samples at different heating rates to determine the triplet that controls their decomposition. The data collection also includes catalytic pyrolysis tests using various oaks (torrefied and non-torrefied) in He and H₂ atmospheres against the catalysts. Py-GC-MS experiments provide detailed information on the composition of condensable pyrolysis gases, which is useful for industrial scale-up proposals that utilize machine learning or multivariate statistics, as well as a description of reaction mechanisms via computational chemistry. The data is organized into three main folders, which contain the dataset in XLS and CSV formats, where the folders that include them have the same name according to the file type. The third folder contains machine-readable metadata files, which allow for the correct handling of the data provided. In this folder, you will locate files that link the names of individual files to the experimental conditions, column headers to the units, and compounds identified with their respective chemical formulas. On the other hand, the folders containing the data are organized as follows (both folders have the same organization): Folder 1. Raw materials characterization-Oak (untreated and torrefied) Folder 2. Characterisation of catalysts Folder 3. Thermogravimetric analysis at different heating rates Folder 4. Non-catalytic Pyrolysis and hydropyrolysis Folder 5. Catalytic Pyrolysis and hydropyrolysis

Raw data.

Background: Perioperative anesthesia(PA) drugs for deep vein thrombosis(DVE) patients can significantly affect their rehabilitation. Indeed, DVE is a major manifestation of Kawasaki disease(KD) patients. Hence, research targeting investigation of the role of PA in KD-associated DVE can provide novel insights into KD with DVE management. Methods: By combination of 3 DVE patient bulk profiles(GSE118259, GSE19151 and GSE48000) with perioperative anesthesia-related drug target(PARDTGs) and integrative bioinformatic analysis(Limma and machine learning framework), we identified hub differentially expressed PARDTGs for DVE patients. The dignostic performance and corresponding molecular and immune features were also estimated among DVE patients. Furthermore, natural compounds for alleviation of DVE targeting hub PARDTGs were also enriched by CTD database and validated by molecular docking. In addition, differentially expressed PARDTGs can guide the risk stratification for DVE patients via consensus clustering. Besides, KD peripheral blood single-cell profile(GSE254657) was utilized, and hub differentially expressed PARDTGs functional performance in investigation of association between DVT and patients were estimated in spatial and temporal manners.

Data are provided on molar concentration of nitrate, ammonium and total dissolved phosphorus (sum of organic and inorganic forms), together with salinity, for six estuaries in the south-east corner of the Bay of Biscay. These are the mesotidal estuaries of the Deba, Urola, Oria, Urumea, Oiartzun and Bidasoa rivers. In addition, the dataset includes the Endara stream, a tributary of the Bidasoa, and five other small rivers that also flow into the Basque coast: the Mijoa, Narrondo, Igara, Añorga and Molinao streams. The frequency of the nutrient data is approximately eight times per year for most of these systems, although the Oiartzun estuary and some of the streams present a smaller amount of data. The data were gathered from the water quality control network of the Gipuzkoa Provincial Council (GPC), which started in 1986. The GPC network stablished 5−9 sampling stations along the longitudinal axe of the estuaries, from head to mouth, and one station in each of the streams. Samplings took place mostly during the morning and the tidal state was not considered to choose the dates. The water samples were collected just below the surface using a clean bucket and immediately, their salinity was measured by field sensors. In the laboratory, from 2011 on, nitrate was determined by ion chromatography with chemical suppression followed by conductivity detector; ammonium by indophenol blue colorimetry; and phosphorus by inductively coupled plasma mass spectrometry (ICP−MS) after filtration (0.45 µm) and nitric acid acidification. This dataset mainly represents low salinity waters (≤1 PSU) because the analytical methods were not optimised for other matrices and, in the laboratory, the samples whose conductivity was higher than 1500 µS/cm were generally rejected. Therefore, the tidal state and the river runoff at the time of sampling largely influenced which estuarine stations were to be analysed for nutrients. The instrumentation and the detection limits varied little during long time periods (>10 years), both in the field and in the laboratory. We want to thank the staff of the GPC for their assistance with the field work and the Fraisoro Agro‑environmental Laboratory for performing the chemical analyses. The data processing was carried out by AZTI and funded by the GPC and by the Horizon Europe GES4SEAS project (grant agreement no. 101059877; www.ges4seas.eu).

This repository contains the supplementary material and 3D brain models associated with the paper “From Pairwise to Higher-Order Brain Community Detection: A Hypergraph Signal Processing Approach on Brain Functional Connectivity Analysis” by Breno C. Bispo, José R. de Oliveira Neto, Juliano B. Lima, and Fernando A. N. Santos.

Python code

Dataset and R code relative to the paper titled: An alternative test to evaluate the assistance provided by back-support exoskeletons: a pilot study

Portion of folders for the images used, which contain a Level 2 (radiometrically calibrated data) folder and a Level 3 (radiometrically calibrated and georeferenced data) folder produced by the HRSC instrument onboard the Mars Express space mission. Level 2 data are in the Vicar format to retrieve the position and time of the spacecraft. Level 3 data are in TIFF format and were used with QGIS software. Each folder for the orbits taken by HRSC was used to morphologically characterize atmospheric gravity waves and also to dynamically characterize them. The altitudes of the clouds and the winds were retrieved using Level 2 and Level 3 data from the "double-broom" observations. Each folder contains data in Level 2 and Level 3 formats (the corresponding level of processing on PSA is L3 for Level 2 data in this folder, and L4 for Level 3 data).