Producer Prices in the United States increased to 148.07 points in May from 147.88 points in April of 2025. This dataset provides the latest reported value for - United States Producer Prices - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Contains a range of producer price indexes. Firstly, economy-wide indexes are presented within a Stage of Production (SOP) framework, followed by a set of partial, stand-alone measures relating to specific industry sectors of the economy (selected manufacturing, construction, mining and service industries).

Producer Price Inflation MoM in the United States increased to 0.10 percent in May from -0.20 percent in April of 2025. This dataset includes a chart with historical data for the United States Producer Price Inflation MoM.

Producer Prices in the United States increased 2.60 percent in May of 2025 over the same month in the previous year. This dataset provides - United States Producer Prices Change - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Producer Prices in Australia increased to 133 points in the fourth quarter of 2024 from 132 points in the third quarter of 2024. This dataset provides - Australia Producer Prices - actual values, historical data, forecast, chart, statistics, economic calendar and news.

aPlanarity;beccentricity;csecondary structure elements at the interface for the target and related partner;dGap volume;eGap volume index;fHydrogen bonds per 100 Å2 of interface;gSalt bridges;hAccessible surface area buried at the interface of the protein/protein complex;iAccessible surface area hidden by the inhibitor;jRoot mean square deviation (CA atoms) between unbound protein and complex;kwhen unbound protein was not available, rmsd between protein/protein and protein/ligand complexes was computed;lTotal pocket volume at the interface;mNumber of interface residue segments. For each parameter the mean and standard deviation are presented.

Site Description:

In this dataset, there are seventeen production crop fields in Bulgaria where winter rapeseed and wheat were grown and two research fields in France where winter wheat – rapeseed – barley – sunflower and winter wheat – irrigated maize crop rotation is used. The full description of those fields is in the database "In-situ crop phenology dataset from sites in Bulgaria and France" (doi.org/10.5281/zenodo.7875440).

Methodology and Data Description:

Remote sensing data is extracted from Sentinel-2 tiles 35TNJ for Bulgarian sites and 31TCJ for French sites on the day of the overpass since September 2015 for Sentinel-2 derived vegetation indices and since October 2016 for HR-VPP products. To suppress spectral mixing effects at the parcel boundaries, as highlighted by Meier et al., 2020, the values from all datasets were subgrouped per field and then aggregated to a single median value for further analysis.

Sentinel-2 data was downloaded for all test sites from CREODIAS (https://creodias.eu/) in L2A processing level using a maximum scene-wide cloudy cover threshold of 75%. Scenes before 2017 were available in L1C processing level only. Scenes in L1C processing level were corrected for atmospheric effects after downloading using Sen2Cor (v2.9) with default settings. This was the same version used for the L2A scenes obtained intermediately from CREODIAS.

Next, the data was extracted from the Sentinel-2 scenes for each field parcel where only SCL classes 4 (vegetation) and 5 (bare soil) pixels were kept. We resampled the 20m band B8A to match the spatial resolution of the green and red band (10m) using nearest neighbor interpolation. The entire image processing chain was carried out using the open-source Python Earth Observation Data Analysis Library (EOdal) (Graf et al., 2022).

Apart from the widely used Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), we included two recently proposed indices that were reported to have a higher correlation with photosynthesis and drought response of vegetation: These were the Near-Infrared Reflection of Vegetation (NIRv) (Badgley et al., 2017) and Kernel NDVI (kNDVI) (Camps-Valls et al., 2021). We calculated the vegetation indices in two different ways:

First, we used B08 as near-infrared (NIR) band which comes in a native spatial resolution of 10 m. B08 (central wavelength 833 nm) has a relatively coarse spectral resolution with a bandwidth of 106 nm.

Second, we used B8A which is available at 20 m spatial resolution. B8A differs from B08 in its central wavelength (864 nm) and has a narrower bandwidth (21 nm or 22 nm in the case of Sentinel-2A and 2B, respectively) compared to B08.

The High Resolution Vegetation Phenology and Productivity (HR-VPP) dataset from Copernicus Land Monitoring Service (CLMS) has three 10-m set products of Sentinel-2: vegetation indices, vegetation phenology and productivity parameters and seasonal trajectories (Tian et al., 2021). Both vegetation indices, Normalized Vegetation Index (NDVI) and Plant Phenology (PPI) and plant parameters, Fraction of Absorbed Photosynthetic Active Radiation (FAPAR) and Leaf Area Index (LAI) were computed for the time of Sentinel-2 overpass by the data provider.

NDVI is computed directly from B04 and B08 and PPI is computed using Difference Vegetation Index (DVI = B08 - B04) and its seasonal maximum value per pixel. FAPAR and LAI are retrieved from B03 and B04 and B08 with neural network training on PROSAIL model simulations. The dataset has a quality flag product (QFLAG2) which is a 16-bit that extends the scene classification band (SCL) of the Sentinel-2 Level-2 products. A “medium” filter was used to mask out QFLAG2 values from 2 to 1022, leaving land pixels (bit 1) within or outside cloud proximity (bits 11 and 13) or cloud shadow proximity (bits 12 and 14).

The HR-VPP daily raw vegetation indices products are described in detail in the user manual (Smets et al., 2022) and the computations details of PPI are given by Jin and Eklundh (2014). Seasonal trajectories refer to the 10-daily smoothed time-series of PPI used for vegetation phenology and productivity parameters retrieval with TIMESAT (Jönsson and Eklundh 2002, 2004).

HR-VPP data was downloaded through the WEkEO Copernicus Data and Information Access Services (DIAS) system with a Python 3.8.10 harmonized data access (HDA) API 0.2.1. Zonal statistics [’min’, ’max’, ’mean’, ’median’, ’count’, ’std’, ’majority’] were computed on non-masked pixel values within field boundaries with rasterstats Python package 0.17.00.

The Start of season date (SOSD), end of season date (EOSD) and length of seasons (LENGTH) were extracted from the annual Vegetation Phenology and Productivity Parameters (VPP) dataset as an additional source for comparison. These data are a product of the Vegetation Phenology and Productivity Parameters, see (https://land.copernicus.eu/pan-european/biophysical-parameters/high-resolution-vegetation-phenology-and-productivity/vegetation-phenology-and-productivity) for detailed information.

File Description:

4 datasets:

1_senseco_data_S2_B08_Bulgaria_France; 1_senseco_data_S2_B8A_Bulgaria_France; 1_senseco_data_HR_VPP_Bulgaria_France; 1_senseco_data_phenology_VPP_Bulgaria_France

3 metadata:

2_senseco_metadata_S2_B08_B8A_Bulgaria_France; 2_senseco_metadata_HR_VPP_Bulgaria_France; 2_senseco_metadata_phenology_VPP_Bulgaria_France

The dataset files “1_senseco_data_S2_B8_Bulgaria_France” and “1_senseco_data_S2_B8A_Bulgaria_France” concerns all vegetation indices (EVI, NDVI, kNDVI, NIRv) data values and related information, and metadata file “2_senseco_metadata_S2_B08_B8A_Bulgaria_France” describes all the existing variables. Both “1_senseco_data_S2_B8_Bulgaria_France” and “1_senseco_data_S2_B8A_Bulgaria_France” have the same column variable names and for that reason, they share the same metadata file “2_senseco_metadata_S2_B08_B8A_Bulgaria_France”.

The dataset file “1_senseco_data_HR_VPP_Bulgaria_France” concerns vegetation indices (NDVI, PPI) and plant parameters (LAI, FAPAR) data values and related information, and metadata file “2_senseco_metadata_HRVPP_Bulgaria_France” describes all the existing variables.

The dataset file “1_senseco_data_phenology_VPP_Bulgaria_France” concerns the vegetation phenology and productivity parameters (LENGTH, SOSD, EOSD) values and related information, and metadata file “2_senseco_metadata_VPP_Bulgaria_France” describes all the existing variables.

Bibliography

G. Badgley, C.B. Field, J.A. Berry, Canopy near-infrared reflectance and terrestrial photosynthesis, Sci. Adv. 3 (2017) e1602244. https://doi.org/10.1126/sciadv.1602244.

G. Camps-Valls, M. Campos-Taberner, Á. Moreno-Martínez, S. Walther, G. Duveiller, A. Cescatti, M.D. Mahecha, J. Muñoz-Marí, F.J. García-Haro, L. Guanter, M. Jung, J.A. Gamon, M. Reichstein, S.W. Running, A unified vegetation index for quantifying the terrestrial biosphere, Sci. Adv. 7 (2021) eabc7447. https://doi.org/10.1126/sciadv.abc7447.

L.V. Graf, G. Perich, H. Aasen, EOdal: An open-source Python package for large-scale agroecological research using Earth Observation and gridded environmental data, Comput. Electron. Agric. 203 (2022) 107487. https://doi.org/10.1016/j.compag.2022.107487.

H. Jin, L. Eklundh, A physically based vegetation index for improved monitoring of plant phenology, Remote Sens. Environ. 152 (2014) 512–525. https://doi.org/10.1016/j.rse.2014.07.010.

P. Jonsson, L. Eklundh, Seasonality extraction by function fitting to time-series of satellite sensor data, IEEE Trans. Geosci. Remote Sens. 40 (2002) 1824–1832. https://doi.org/10.1109/TGRS.2002.802519.

P. Jönsson, L. Eklundh, TIMESAT—a program for analyzing time-series of satellite sensor data, Comput. Geosci. 30 (2004) 833–845. https://doi.org/10.1016/j.cageo.2004.05.006.

J. Meier, W. Mauser, T. Hank, H. Bach, Assessments on the impact of high-resolution-sensor pixel sizes for common agricultural policy and smart farming services in European regions, Comput. Electron. Agric. 169 (2020) 105205. https://doi.org/10.1016/j.compag.2019.105205.

B. Smets, Z. Cai, L. Eklund, F. Tian, K. Bonte, R. Van Hoost, R. Van De Kerchove, S. Adriaensen, B. De Roo, T. Jacobs, F. Camacho, J. Sánchez-Zapero, S. Else, H. Scheifinger, K. Hufkens, P. Jönsson, HR-VPP Product User Manual Vegetation Indices, 2022.

F. Tian, Z. Cai, H. Jin, K. Hufkens, H. Scheifinger, T. Tagesson, B. Smets, R. Van Hoolst, K. Bonte, E. Ivits, X. Tong, J. Ardö, L. Eklundh, Calibrating vegetation phenology from Sentinel-2 using eddy covariance, PhenoCam, and PEP725 networks across Europe, Remote Sens. Environ. 260 (2021) 112456. https://doi.org/10.1016/j.rse.2021.112456.

Producer Prices in Poland decreased to 115.50 points in May from 115.70 points in April of 2025. This dataset provides - Poland Producer Prices - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The dataset provides information on fine root biomass (2-20mm ; 1m in depth) associated to edaphic factors (soil texture and fertility) in ecotone forests located on the eastern of Maracá Island, a fluvial island integrated to the Maracá Ecological Station, state of Roraima, northern Brazilian Amazonia. This area represents an ecotone zone of the southern region of the Guyana Shield, which is dominated by mosaics of ombrophilous and seasonal forests in contact with savanna areas. Our sampling took into consideration 30 permanent plots located in the PPBio (Biodiversity Research Program) 25-km2 research grid installed in the eastern of Maracá Island. The fieldwork was carried in July and October 2015 when we collected two soil cores (sub-samples) 1 m in depth for each plot. Each soil core was composed by 10 sections of 10 cm in length (000-010 cm; … ; 090-100 cm). All fine root (2-20 mm) found in each section was classified by diameter categories (2-5 mm; 5-10mm; 10-20mm), dried in electric stove (100±3oC) and weighted (0,0001g). Soil analysis were performed for each depth section taking into account a composite sample derived from both soil cores. We used the soil analysis methodology adopted by Embrapa (Embrapa. 2009. Manual de análises químicas de solos, plantas e fertilizantes. Embrapa Informação Tecnológica, 2. ed. rev. ampl. Brasília-DF. 627 p). Our dataset is presented in two files: (i) soil_analysis - sampling units codes (plotID) and their geographical reference (UTM, SAD69, Zone 20), associated with altitude (m a.s.l.), drainage (well/poor), section depth (cm), and soil analysis (mean of the edaphic variables for each section - texture, fertility, soil bulk density); (ii) fine_root – fine root biomass (g) for each soil section by root diameter category (2-5 mm; 5-10mm; 10-20mm), sampling date and sub-sample number. This dataset was supported by institutional project PPI/INPA 015/122 (Ecologia e manejo de savannas e florestas de Roraima). The Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq - Brazil) provided a fellowships for R.I. Barbosa (CNPq 304204/2015-3) and M.T. Nascimento (CNPq 308352/2015-7). L.C.S. Carvalho was supported by a fellowship from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES/PNPD). Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio) provided authorization for the study. This dataset is also available on the DataONE website (https://search.dataone.org/view/PPBioAmOc.135.9), as well as the soil bulk density data (https://search.dataone.org/view/PPBioAmOc.114.5).

The dataset titled “Lists of Magnetopause and Bow Shock Crossings of Mercury by MESSENGER Spacecraft” employs the measurements from the MESSENGER spacecraft’s Magnetometer (MAG) and Fast Imaging Plasma Spectrometer (FPIS) instruments to identify magnetopause and bow shock crossings during MESSENGER's orbit of Mercury. MESSENGER's data orbiting Mercury were collected between 23-03-2011 and 30-04-2015 and are available from the Planetary Data System’s Planetary Plasma Interactions (PDS/PPI) Node at https://pds-ppi.igpp.ucla.edu.

The dataset includes four lists:

a, Bow_Shock_Out_Time_Duration_public_version_WeijieSun_20230829.txt

b, Bow_Shock_In_Time_Duration_public_version_WeijieSun_20230829.txt

c, MagPause_In_Time_Duration_public_version_WeijieSun_20230829.txt

d, MagPause_Out_Time_Duration_public_version_WeijieSun_20230829.txt

Here are examples for the time in the list:

Example A

2011 03 23 15 39 10.5 2011 03 23 16 24 02.4 BSO m

This entry represents multiple bow shock crossings. The first six columns indicate the time of the first boundary crossing, while the next six columns indicate the time of the last boundary crossing. “BSO” stands for outbound crossing of the bow shock, and “m” indicates that this is a multiple bow shock crossing made by MESSENGER. Only the first and last boundaries were selected out, we did not identify the boundary crossings in between.

Example B

2011 03 25 13 04 24.2 2011 03 25 13 04 24.0 BSI s

This entry represents a single bow shock crossing. The first six columns and the next six columns are identical, indicating that this is a single event. “BSI” stands for inbound crossing of the bow shock, and “s” indicates that this is a single bow shock crossing made by MESSENGER.

The dataset does not include magnetopause and bow shock crossings during the following time intervals:

a. From 03:02 to 20:00 on 05-04-2011

b. From 24-05-2011 to 03-06-2011

c. From 17:50 to 22:53 on 16-04-2012

d. From 09-06-2012 to 13-06-2012

e. From 07:30 on 08-01-2013 to 16:00 on 09-01-2013

f. From 07:55 to 18:33 on 28-02-2013

g. From 14:22 to 17:38 on 26-12-2014

The current version is updated on 29 August 2023.

This work was supported by NASA Discovery Data Analysis Program (DDAP) Grant #80NSSC22K1061 (PI Weijie Sun).

The peak of season (POS), one of the Vegetation Phenology and Productivity (VPP) parameters, is a product of the pan-European Medium Resolution Vegetation Phenology and Productivity (MR-VPP) component of the Copernicus Land Monitoring Service (CLMS).

The peak of season (POS) expresses the maximum Plant Phenology Index (PPI) values reached during the season. It is computed as the mean value of the times for which, respectively, the left edge has increased to the 80 % level and the right edge has decreased to the 80 % level.

The Plant Phenology Index (PPI) is a physically based vegetation index, developed for improving the monitoring of the vegetation growth cycle. The PPI index values, with 5-day satellite revisit cycle, are first used in a function fitting to derive the PPI Seasonal Trajectories. From these Seasonal Trajectories, a suite of 13 Vegetation Phenology and Productivity (VPP) parameters are then computed and provided, for up to two seasons each year. The peak of season (POS) is one of the 13 parameters. The full list is available in the Product User Manual: https://land.copernicus.eu/user-corner/technical-library/clms_mrvpp_pum_d1-0.pdf

The peak of season (POS) time series dataset is made available as raster files with 500x 500m resolution, in ETRS89-LAEA projection corresponding to the MCD43 tiling grid, for those tiles that cover the EEA38 countries and the United Kingdom and for two seasons in each year from 2000 onwards. It is updated in the first quarter of each year.

The full on-line access to open and free data for this resource will be made available in the second half of 2024. Until then the data will be made available 'on-demand' by filling in the form at: https://land.copernicus.eu/contact-form

BackgroundPrevious study revealed proton pump inhibitors (PPIs) have an effect on gut microbiota. Alteration of the microbiome causes changes of the host immune system and then induces the development of autoimmune diseases (ADs). This study aimed to explore the possible association between PPIs use and ADs.MethodsThis study was conducted using data from the Taiwan National Health Insurance Research Database in the period between 2002 and 2015. We performed multivariate and stratified analysis through the Kaplan-Meier method and Cox proportional hazard models to estimate the association between proton pump inhibitor use and the risk of autoimmune diseases.ResultsOf the 297,099 patients treated with PPI identified, the overall mean (SD) age was 49.17 (15.63) years and 56.28% of the subjects was male. As compared with the non-PPI group, the adjusted hazard ratio (aHR) were higher for incident organ specific ADs such as Graves disease (aHR=3.28), Hashmoto thyroiditis (aHR=3.61), autoimmune hemolytic anemia (aHR=8.88), immune thrombocytopenic purpura (aHR=5.05) Henoch-Schonlein pupura (aHR=4.83) and Myasthenia gravis (aHR=8.73). Furthermore, the adjusted hazard ratio (aHR) were also higher for incident systemic ADs such as ankylosing spondylitis (aHR=3.67), rheumatoid arthritis (aHR=3.96), primary Sjogren syndrome (aHR=7.81), systemic lupus erythemtoasus (aHR=7.03). systemic vasculitis (aHR=5.10), psoriasis (aHR=2.57), systemic scleroderma (aHR=15.85) and inflammatory myopathy (aHR=37.40). Furthermore, we observed no dose-dependent effect between PPI use and the risk of ADs.ConclusionsOur retrospective population-based cohort study showed that the prescription of proton pump inhibitors is associated with a higher risk of ADs.