The data can be used for repair planning, emergency services, financial planning and operational management focus. The obtained data are related to the research article "Monitoring of heating systems as a factor of energy safety of buildings" [1].The biodiesel production from waste soybean oil (using NaOH and KOH catalysts independently) was investigated in this study. The use of optimization tools (artificial neural network, ANN, and response surface methodology, RSM) for the modelling of the relationship between biodiesel yield and process parameters was carried out. The variables employed in the experimental design of biodiesel yields were methanol-oil mole ratio (6 - 12), catalyst concentration (0.7 - 1.7 wt/wt%), reaction temperature (48 - 62°C) and reaction time (50 - 90 min). Also, the usefulness of both the RSM and ANN tools in the accurate prediction of the regression models were revealed, with values of R-sq being 0.93 and 0.98 for RSM and ANN respectively.We present the first dataset that can be used to associate peoples' opinions with comprehensive biodiversity and cultural heritage values. The socio-ecological dataset includes 1) place-based information on peoples' recreational activities, values expressed as pleasant and unpleasant sites, and negative preferences concerning land use in terms of tourism, nature protection and forestry, and 2) compiled information on scored biodiversity values and protection level of sites. The data are organized in 1ha grid cells. The data were compiled from a rural nature-based tourism area in two municipalities northern Finland. Peoples' opinions were assessed using a public participation geographic information system (PPGIS) and the data were merged with spatial biodiversity data from the same area. The data are directly related to the article Tolvanen et al. [1]. Biodiversity data, also utilized in Tolvanen et al. 2020, were compiled from various sources and scoring was done in Kangas et al. [2]. References to individual respondents and spatial locations of markings were removed. The data are useful in evaluating the relationship between people's values and biodiversity.Satellite data provide the opportunity to explore different land surface properties, such as albedo (reflectivity) and forest structure, for multidisciplinary purposes. We estimated land surface black-sky albedo at shortwave, near-infrared and visible spectral regions at a fixed solar zenith angle (i.e., 38∘) during peak growing season in 2005 on a global scale. In addition, we estimated the links between albedo and forest structure variables including forest density [the number of trees/km2], tree cover [percent], and leaf area index [m2/m2] over pure forest pixels during peak growing season in 2005 on a global scale. We acquired and processed remotely sensed variables from moderate resolution imaging spectroradiometer (MODIS) and Landsat satellite images. This article provides 1) dataset of black-sky albedo at fixed solar zenith angle at a 1-km spatial resolution, 2) comparison between black-sky albedos at fixed solar zenith angle and local noon at a 1-km spatial resolution that are grouped based on forest types with the classes of evergreen needleleaf, evergreen broadleaf, deciduous needleleaf, deciduous broadleaf, mixed and woody savannah forests, and also the major biome zones including boreal, mediterranean, temperate and tropical region. 3) the links between black-sky albedo at fixed solar zenith angle and forest structure using generalized additive models at a 0.5-degree spatial resolution during peak growing season in 2005. The pre-processing steps to enhance the accuracy of these datasets include (1) identifying pure forest pixels, (2) excluding high slope pixels and those covered partially by water in the albedo product using high spatial resolution water (i.e., 30-m spatial resolution) and slope (i.e., 90-m spatial resolution) masks, and (3) using the most recent collection (collection 6) of MODIS satellite images. More details and interpretations of these datasets can be found in Alibakhshi et al. (2020) [1].The article includes raw and analyzed data directly related to the research paper entitled "Non-forested vs forest environments the effect of habitat conditions on host tree parameters and the occurrence of associated epiphytic lichens" [1]. These data concern the relationships between the composition of lichen communities and host-tree parameters in non-forested area and a natural lowland deciduous forest in northern Poland. Lichen species confined to non-forested area, associated with forest habitat, and non-specific mutual species occurring in both habitat types are listed together with their host-tree preferences. Data on the phenotypic variability of five common and native to Central Europe tree species in relation to the habitat type are provided. Data that concerns tree parameters are analyzed by the mixed model ANOVA and Principal Component Analysis. Additionally, sample rarefactions and indices of potential lichen species richness for both habitat types are included. Presented data could be used in further studies to compare epiphytic community structure and may be support for campaigns aimed at lichen conservation and at shaping the environment with concern for biodiversity.Cerebrospinal fluid (CSF) is a biofluid in direct contact with the brain and as such constitutes a sample of choice in neurological disorder research, including neurodegenerative diseases such as Alzheimer or Parkinson. Human CSF has still been less studied using proteomic technologies compared to other biological fluids such as blood plasma or serum. In this work, a pool of "normal" human CSF samples was analysed using a shotgun proteomic workflow that combined removal of highly abundant proteins by immunoaffinity depletion and isoelectric focussing fractionation of tryptic peptides to alleviate the complexity of the biofluid. The resulting 24 fractions were analysed using liquid chromatography coupled to a high-resolution and high-accuracy timsTOF Pro mass spectrometer.  https://www.selleckchem.com/products/ots964.html  This state-of-the-art mass spectrometry-based proteomic workflow allowed the identification of 3'174 proteins in CSF. The dataset reported herein completes the pool of the most comprehensive human CSF proteomes obtained so far. An overview of the identified proteins is provided based on gene ontology annotation.