IRI (Information Resources, Inc.) has just released an extensive set of data for academic use. This is 5 [now updated to 6] years of store scanner data and consumer panel data for 30 large consumer packaged goods categories. TNS is providing advertising data on two categories. This data covers stores in 49 markets for store level data and 2 markets for consumer panel data. This is such a large data set (over 50 gigabytes) that we are delivering it on a USB drive. More information about this data set can found at the IRI web site. http://us.infores.com/academic This site contains a high level description of the data set (Bronnenberg, Kruger and Mela, 2008, published in the July-August issue of Marketing Science), and a document containing terms of use for the dataset. We've set up a Google Groups site to support the data set. This group is available to those who've signed the terms of use and received the data set. This is the first database submission that Marketing Science has accepted.

ObjectiveThis study aims to explore potential ischemia-reperfusion injury (IRI) predictive biomarkers related to disulfidptosis following lung transplantation.MethodsThe study utilized datasets from the GEO database, specifically GSE145989 and GSE127003, which include samples of lung cold ischemia and reperfusion following transplantation. Differential expressed analysis and functional enrichment analysis were conducted to identify key genes associated with lung transplant IRI. Multiple machine learning algorithms (Generalized Linear Model, Support Vector Machine, and Random Forest) were applied for joint screening, leading to the construction of a predictive model. The CIBERSORT method was used to assess the infiltration levels of immune cells in lung tissue samples post-transplant. Finally, cell line and animal experiments were carried out to validate the effectiveness and applicability of the model.ResultsA total of 14,592 hub differential-expressed genes were identified, showing significant changes in cold ischemia and reperfusion samples. Using the three machine learning algorithms for joint analysis, a predictive model composed of SLC7A11 and LRPPRC was constructed. This model demonstrated excellent predictive efficacy across multiple datasets, with area under the curve (AUC) values of 0.742 and 0.938, respectively. Additionally, significant differences in neutrophils and macrophages were observed in lung transplant cold ischemia and reperfusion samples. Based on the differential genes associated with disulfidptosis and utilizing the CMap database, we identified two potential drugs targeting IRI: olanzapine and vortioxetine. Ultimately, cell line and animal experiments validated the predictive model’s reliability and potential clinical value, revealing that disulfidptosis presents in IRI, and high SLC7A11 expression promotes IRI, while low LRPPRC expression contributes to its occurrence.ConclusionSLC7A11 and LRPPRC can serve as predictive biomarkers for IRI following lung transplantation.

Introduction: The morbidity and mortality of acute kidney injury (AKI) are increasing. Epigenetic regulation and immune cell infiltration are thought to be involved in the AKI. However, the relationship between epigenetic regulation and immune cell infiltration in AKI has not been elucidated. This study was conducted to identify the differentially expressed genes (DEGs), differentially expressed RNA methylation genes (DEMGs), and infiltrated immune cells in the kidneys of ischemia reperfusion induced- acute kidney injury (IRI-AKI) models and further explore their relationships in IRI-AKI. Methods: This is a bioinformatic analysis using R programming language in 3 selected IRI-AKI datasets from the Gene Expression Omnibus (GEO) database, including 16 IRI-AKI kidney tissues and 10 normal kidney tissues. The DEGs were screened, and enrichment pathways were analyzed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The DEMGs and core DEMGs were identified using the R package. The ROC curve was plotted to predict disease occurrence of 7 core DEMGs. The correlation of 7 core DEMGs and other genes was analyzed using Pearson’s correlation test. The gene set enrichment analysis (GSEA) of each DEMG was conducted using the R package. The upstream miRNAs and transcript factors of 7 core DEMGs were predicted based on the RegNetwork database and Cytoscape software. The STITCH database was used to predict the possible binding compounds of the 7 core DEMGs. Immune cell infiltration in kidney tissues between the IRI-AKI group and control group was evaluated using the R package. Results: A total of 2367 DEGs were obtained, including 1180 upregulated and 1187 downregulated genes in IRI-AKI kidney associated with the cell structure, proliferation, molecule binding/interaction, and signaling pathways such as the leucocyte migration and chemokine signaling pathways. Ten DEMGs were identified, with Ythdf1, Rbm15, Trmt6, Hnrnpc, and Dnmt1 being significantly upregulated, while Lrpprc, Cyfip2, Mettl3, Ncbp2, and Nudt7 were significantly downregulated in IRI-AKI tissues. The molecules interacting with 7 core DEMGs were identified. Significant changes in the infiltration of 8 types of immune cells were observed in IRI-AKI kidneys compared to normal controls. The significant correlation between 6 core DEMGs and the infiltration of immune cells was observed. Conclusion: IRI may induce AKI through RNA methylation to regulate the expression of genes involved in immune cell infiltration.

The Industrial R&D Investment Scoreboard (IRI) is a scientific project carried out within the Directorate B of Growth and Innovation, one of the seven scientific institutes of the European Commission's Joint Research Centre (JRC). 'iriR' 's objective is to allow an easy connection with R to the European Commission's Economics of Industrial Research and Innovation data.

The Lamont-Doherty Deep-Sea Sample Repository, located in the Core Laboratory at the Lamont-Doherty Earth Observatory (LDEO) of Columbia University, contains archived sediment cores from every major world ocean and sea. The Core Repository contains approximately 72,000 meters of core composed of 9,700 piston cores; 7,000 trigger weight cores; and 1,500 other cores such as box, kasten, and large diameter gravity cores. There are also 4,000 dredge and grab samples including a large collection of manganese nodules, and many samples recovered by submersibles.

The core sample can be searched through the Deep-Sea Core Database at the LDEO/IRI Climate Data Library for coordinates of the site, water depth, topography, core length or dredge size, sample device, date and time of retrieval. "http://ingrid.ldgo.columbia.edu/SOURCES/.LDEO/.Deep_Sea_Core.cuf/"

Data on the cores is stored at NOAA/National Gephysical Data Center (NGDC) Marine Geology and Geophysics (MGG): "http://www.ngdc.noaa.gov:80/mgg/curator/curator.html"

This composition appears in the I-Ir-Na-Pr region of phase space. It's relative stability is shown in the I-Ir-Na-Pr phase diagram (left). The relative stability of all other phases at this composition (and the combination of other stable phases, if no compound at this composition is stable) is shown in the relative stability plot (right)

This composition appears in the Bi-I-Ir-Si region of phase space. It's relative stability is shown in the Bi-I-Ir-Si phase diagram (left). The relative stability of all other phases at this composition (and the combination of other stable phases, if no compound at this composition is stable) is shown in the relative stability plot (right)

The service provides access to the database of organic seeds maintained by ÚKZÚZ with the possibility of various filtering criteria.

Medical student empathy levels measured using IRI questionnaire. Includes data regarding university course, study year, age , gender, nationality, future speciality plans.

Vietnam Database Security Market was valued at USD 61.6 Million in 2023 and is anticipated to reach USD 137.1 Million with a CAGR of 14.1% through 2029.

GIS layers symbolizing various data in the WSPMS database This featureclass has a text field with values Very Poor, Poor, Fair, Good and Very Good indicating the lowest category of PSC or RCN, Rutting and IRI.A brief user guide is located at: https://data.wsdot.wa.gov/geospatial/DOT_WSPMS/WSPMSFeatureClassFieldDescription.docx.

Geonames is the database of geographic names of the Czech Republic. It is a system for administration of named features. A standard geographic name is maintained as a part of attribute information to approx. 165 types of named features. Geometric representation of some Geonames objects corresponds to the locations of features of ZABAGED®, to which the name applies. Another group of features, above all all field and forest lots and local parts of settlements have a simplified geometry corresponding to the location of its map lettering in the state map series. The Features are represented by a vector (punctual) component with attributes containing wider information about names.

Although the link between ischemia-reperfusion injury (IRI) and T cell-mediated rejection (TCMR) in kidney transplantation (KT) is well known, the mechanism remains unclear. We investigated essential genes and biological processes involved in interactions between IRI and TCMR. Methods: Renal IRI and TCMR datasets were obtained from the Gene Expression Omnibus database. IRI and TCMR co-expression networks were built using weighted gene co-expression network analysis, and essential modules were identified to acquire shared genes and conduct functional enrichment analysis. Shared genes were used for TCMR consensus clustering, differentially expressed genes (DEGs) were identified, and gene set enrichment analysis (GSEA) was conducted. Three machine learning algorithms screened for hub genes, which underwent miRNA prediction and transcription factor analysis. Hub gene expression was verified, and survival analysis was performed using Kaplan–Meier curves. Results: IRI and TCMR shared 84 genes. Functional enrichment analysis revealed that inflammation played a significant role. Based on shared genes, TCMR was divided into two clusters. GSEA revealed that graft rejection-related pathways varied between the two clusters. TCMR hub genes, guanylate-binding protein 1 (GBP1) and CD69, showed increased expression. Decreased survival rates were found in patients who had undergone KT and had high GBP1 and CD69 levels. Conclusions: The study demonstrates that renal IRI has a potential role in renal TCMR and the pathogenic pathways are potentially inflammation-related.

This database contains key information on boreholes. The database is generated about twice a year as a layer in a GIS environment for use in the Borehole Surveys application. Individual points in the application represent boreholes and polygons indicate groups of related boreholes.

This database contains the locations of points at which vertical electrical profiles and sounding curves were measured. The database of VES measurements (vertical electrical sounding) was established in 1994. The layer has been transformed according to the INSPIRE data specification for the Geoph Station object from the Geophysics application scheme.

Feature layer with the location of water-gauging stations. Over 90% of measurements are carried out through automatic stations with locally stored records or transmitted records. Discharge is derived from observed water level using rating curves, which are determined based on hydrometric measurements. The data from these stations are stored in the Central Regime Database of Surface Water which is maintained by the Hydrology Database and Water Budget Department.

The change database CLC2000-Changes (CHA2000_CZ) was derived from the CLC1990 database by visual comparison of raster data from 1990 and 2000. Identifies areas with different land cover in the CLC90 and CLC2000 databases. All changes meeting the mapping criteria were recorded (area > 5 ha, border shift > 100 m). More on the description of the processing methodology in Büttner, G., Kosztra, B. 2007. CLC2006 Technical Guidelines, European Environment Agency (EEA).

 We present an aerosol classification based upon AERONET level 2.0 almucantar retrieval products from the period 1993 to 2012. In the initial phase of this research we opto-physically identified five major types of Bulk Columnar Aerosol (BCA) - based solely upon intensive optical properties of spectral Single Scattering Albedo (SSA), spectral Indices of Refraction (real – RRI and imaginary - IRI), and two Angstrom Exponents (extinction – EAE and absorption - AAE). These BCA we classified as Maritime Aerosol, Dust Aerosol, Urban Industrial Aerosol, Biomass Burning Aerosol, and Mixed Aerosol. The classification of a particular observation as one of these aerosol types is determined by its five-dimensional Mahalanobis distance (MD) to the centroid of each reference cluster (itself a 5-D hyperellipsoid). To retain a greater number of AERONET sites in the study (200+), we kept the variable space to 5-D. To generate reference clusters, we only retained data points that lie wi...

English description below. Bodová vrstva dobíjecích stanic pro elektromobily nebo plug-in hybridy. Zdrojem je evidence Ministerstva průmyslu a obchodu, která je pravidelně aktualizovaná. Datová sada je aktuální ke dni 31. 3. 2025.Data jsou v souřadném systému WGS 1984 (EPSG 4326).Point layer of publicly available charging points for electric cars or plug-in hybrids. The source is the Ministry of Industry and Trade database. Dataset was updated 31. 3. 2025.Coordinate system - WGS 1984 (EPSG 4326).

Feature layer depicting the location of boreholes observing groundwater level in shallow and deep aquifers. Currently, most of the measuring objects are equipped with automatic stations that record locally or transmit the data. These stations record once a day. Originally, the boreholes were observed by volunteers with a weekly frequency of measurement. Most of new measuring objects were built during the so-called ISPA project. They went into operation in 2007. The data are stored in the Central Regime Database of Groundwater which is maintained by the Hydrology Database and Water Budget Department.