Multiferroic materials are of significant interest for spintronic and electronic applications. Of such materials, e-Fe2O3 is interesting because it is a hard ferrimagnet that exhibits strong magnetoelectric coupling at room temperature. Because the electronic structure has yet to be resolved, the origin of the multiferroicity is unclear. Here we will use the RIXS-MCD spectrometer at ID-32 to investigate the d-d excitations of e-Fe2O3 nanoparticles in both the low temperature incommensurate and room temperature ferrimagnetic (multiferroic) magnetic phases. Cr-doped samples (with missing eg orbital electrons) will provide further information about the role of the eg orbitals. This experiment will provide an improved understanding of the physics that underpin multiferroic perovskites.

Freshwater sources from Greenland and Antarctica. For details see https://github.com/NASA-GISS/freshwater-forcing-workshop and https://doi.org/10.5194/egusphere-2025-1940v7 update: See changelog at https://github.com/NASA-GISS/freshwater-forcing-workshop/compare/de5779e8b454a7d432b0159aebfe289665ced3c8...f4250709080c46d82fd96a082a80e4f18ceaa604

CY-Bench: A comprehensive benchmark dataset for sub-national crop yield forecastingOverviewCY-Bench is a dataset and benchmark for subnational crop yield forecasting, with coverage of major crop growing countries of the world for maize and wheat. By subnational, we mean the administrative level where yield statistics are published. When statistics are available for multiple levels, we pick the highest resolution. The dataset combines sub-national yield statistics with relevant predictors, such as growing-season weather indicators, remote sensing indicators, evapotranspiration, soil moisture indicators, and static soil properties. CY-Bench has been designed and curated by agricultural experts, climate scientists, and machine learning researchers from the AgML Community, with the aim of facilitating model intercomparison across the diverse agricultural systems around the globe in conditions as close as possible to real-world operationalization. Ultimately, by lowering the barrier to entry for ML researchers in this crucial application area, CY-Bench will facilitate the development of improved crop forecasting tools that can be used to support decision-makers in food security planning worldwide.* Crops : Wheat & Maize* Spatial Coverage : Wheat (29 countries), Maize (38).  See CY-Bench Summary for the list of countries.* Temporal Coverage : Varies. See CY-Bench Summary.Data Data formatThe benchmark data is organized as a collection of CSV files (with the exception of location information, see below), with each file representing a specific category of variable for a particular country. Each CSV file is named according to the category and the country it pertains to, facilitating easy identification and retrieval. The data within each CSV file is structured in tabular format, where rows represent observations and columns represent different predictors related to a category of variable.Data contentAll data files are provided as .csv.DataDescriptionVariables (units)Temporal ResolutionData Source (Reference)crop_calendarstart and end of growing seasonsos (day of the year),eos (day of the year)staticWorld Cereal (Franch et al, 2022)crop_maskcrop area fractioncrop_area (km2), crop_area_percentage (%)staticWorldCereal (Van Tricht et al., 2023; EC-JRC, 2024)fparfraction of absorbed photosynthetically active radiationfpar (%)Dekadal (3 times a month; 1-10, 11-20, 21-31)European Commission's Joint Research Centre (EC-JRC, 2024)ndvinormalized difference vegetation index-approximately weeklyMOD09CMG (Vermote, 2015)meteotemperature, precipitation (prec), radiation, potential evapotranspiration (et0), climatic water balance (= prec - et0)  tmin (C), tmax (C), tavg (C), prec (mm0, et0 (mm), cwb (mm), rad (J m-2 day-1)dailyAgERA5 (Boogaard et al, 2022)soil_moisturesurface soil moisture, rootzone soil moisturessm (kg m-2), rsm (kg m-2)dailyGLDAS (Rodell et al, 2004)soilavailable water capacity, bulk density, drainage classawc (c m-1), bulk_density (kg dm-3), drainage class (category)staticWISE Soil database (Batjes, 2016)locationcentroidlatitude, logitude, region_area (km2)static yieldend-of-season yieldyield (t ha-1)yearlyVarious country or region specific sources (see crop_statistics_... in https://github.com/WUR-AI/AgML-CY-Bench/tree/main/data_preparation) Folder structurecybench-data: The CY-Bench dataset has been structure at first level by crop type and subsequently by country. For each country, the folder name follows the ISO 3166-1 alpha-2 two-character code. A separate .csv is available for each predictor data and crop calendar as shown below. The csv files are named to reflect the corresponding country and crop type e.g. variable_croptype_country.csv.CY-Bench│└─── maize│   ││   └─── AO│   │   -- crop_calendar_maize_AO.csv│   │   -- crop_mask_maize_AO.csv│   │   -- fpar_maize_AO.cs│   │   -- location_maize_AO.csv│   │   -- meteo_maize_AO.csv│   │   -- ndvi_maize_AO.csv│   │   -- soil_maize_AO.csv│   │   -- soil_moisture_maize_AO.csv│   │   -- yield_maize_AO.csv│   │ │   └─── AR│       -- crop_calendar_maize_AR.csv│       -- crop_mask_maize_AR.csv│       -- fpar_maize_AR.csv│       -- ...│   └─── wheat│   ││   └─── AR│   │   -- crop_calendar_wheat_AR.csv│   │   -- crop_mask_wheat_AR.csv│   │   -- fpar_wheat_AR.csv│   │   ...Example : CSV data content for maize in country XX└─── crop_calendar_maize_X.csv│   -- crop_name (name of the crop)│   -- adm_id (unique identifier for a subnational unit)│   -- sos (start of crop season)│   -- eos (end of crop season)│ └─── crop_mask_maize_X.csv│   -- crop_name│   -- adm_id │   -- crop_area│   -- crop_area_percentage│   └─── fpar_maize_X.csv│   -- crop_name│   -- adm_id │   -- date (in the format YYYYMMdd)│   -- fpar│└─── location_maize_X.csv│   -- crop_name│   -- adm_id │   -- latitude│   -- longitude│   -- region_area│└─── meteo_maize_X.csv│   -- crop_name│   -- adm_id │   -- date (in the format YYYYMMdd)│   -- tmin (minimum temperature)│   -- tmax (maximum temperature)│   -- prec (precipitation)│   -- rad (radiation)│   -- tavg (average temperature)│   -- et0 (evapotranspiration)│   -- vpd (vapor pressure deficit)│   -- cwb (crop water balance)│ └─── ndvi_maize_X.csv│   -- crop_name│   -- adm_id│   -- date (in the format YYYYMMdd)│   -- ndvi  │   └─── soil_maize_X.csv│   -- crop_name│   -- adm_id│   -- awc (available water capacity)│   -- bulk_density│   -- drainage_class│   └─── soil_moisture_maize_X.csv│   -- crop_name│   -- adm_id│   -- date (in the format YYYYMMdd)│   -- ssm (surface soil moisture)│   -- rsm ()│   └─── yield_maize_X.csv│   -- crop_name│   -- country_code│   -- adm_id│   -- harvest_year│   -- yield│   -- harvest_area│   -- productioncentroids.zip and polygons.zip include shapes or geometries as centroids ( x and y coordinates) and polygons (multipolygons) of administrative regions respectively. They are organized as follows:centroids│   └─── AO│   │   -- AO.cpg│   │   -- AO.dbf│   │   -- AO.prj│   │   -- AO.shp│   │   -- AO.shx│   └─── AR│   │   -- AR.cpg│   │   -- AR.dbf│   │   -- AR.prj│   │   -- AR.shp│   │   -- AR.shx...polygons│   └─── AO│   │   -- AO.cpg│   │   -- AO.dbf│   │   -- AO.prj│   │   -- AO.shp│   │   -- AO.shx│   └─── AR│   │   -- AR.cpg│   │   -- AR.dbf│   │   -- AR.prj│   │   -- AR.shp│   │   -- AR.shx...Data accessThe full dataset can be downloaded directly from Zenodo or using the zenodo_get``` libraryLicense and citationWe kindly ask all users of CY-Bench to properly respect licensing and citation conditions of the datasets included. Version Notes1.0 is the dataset submitted to NeurIPS Datasets and Benchmarks Track. The paper and discussions are here: https://openreview.net/forum?id=jkJDNG468g#discussion1.1 and 1.2 fix some issues with column names and mismatches in adm_id between yield data and input data.1.3 includes location information in the form of centroids and polygons of admin regions.1.4 updates the fpar data for 2023. fpar data was incomplete for 2023 in earlier versions (due to unavailability in the data source itself).1.5 fixes an issue in crop calendar1.6 fixes an issue in ndvi time series1.7 updates storage precision to 3 decimal places to reduce data size1.8 filter out invalid yield values1.9 Add vpd. Add location. ET0 obtained from AgERA5 (was AQUASTAT-FAO ). Use AgERA5 2.0 (was AgERA5 1.1)1.10 Add region_are to location*.csv. Add crop_mask_*.csv. Fix error in yield Australia.

Understanding the interrelationship between the survival and reproductive states of salmonids is important for both management and conservation purposes; however, their complex life history introduces challenges. The survival probabilities of anadromous Dolly Varden (Salvelinus malma) are influenced by their reproductive cycle, while predicting their reproductive state in successive years is difficult, as it can vary based on an individual's sex. We developed a Bayesian multi-state capture-recapture model that estimates reproductive state transition probabilities, survival probabilities, and the effect of sex on state transitions using the Cormack-Jolly-Seber model. We applied the model to Dolly Varden data collected from five river systems in the western Canadian Arctic. We demonstrate sex-specific state transition probability differences, with females exhibiting higher transition rates into reproductive states. Moreover, survival probabilities were influenced by both sex and reproductive status. Across all rivers, survival probabilities for both sexes decreased by approximately 50% while spawning compared to non-spawning. This study provides important insights into how reproduction and sex affect survival among stocks, which improves their assessment to ensure management and conservation goals.

CY-Bench: A comprehensive benchmark dataset for sub-national crop yield forecastingOverviewCY-Bench is a dataset and benchmark for subnational crop yield forecasting, with coverage of major crop growing countries of the world for maize and wheat. By subnational, we mean the administrative level where yield statistics are published. When statistics are available for multiple levels, we pick the highest resolution. The dataset combines sub-national yield statistics with relevant predictors, such as growing-season weather indicators, remote sensing indicators, evapotranspiration, soil moisture indicators, and static soil properties. CY-Bench has been designed and curated by agricultural experts, climate scientists, and machine learning researchers from the AgML Community, with the aim of facilitating model intercomparison across the diverse agricultural systems around the globe in conditions as close as possible to real-world operationalization. Ultimately, by lowering the barrier to entry for ML researchers in this crucial application area, CY-Bench will facilitate the development of improved crop forecasting tools that can be used to support decision-makers in food security planning worldwide.* Crops : Wheat & Maize* Spatial Coverage : Wheat (29 countries), Maize (38).  See CY-Bench Summary for the list of countries.* Temporal Coverage : Varies. See CY-Bench Summary.Data Data formatThe benchmark data is organized as a collection of CSV files (with the exception of location information, see below), with each file representing a specific category of variable for a particular country. Each CSV file is named according to the category and the country it pertains to, facilitating easy identification and retrieval. The data within each CSV file is structured in tabular format, where rows represent observations and columns represent different predictors related to a category of variable.Data contentAll data files are provided as .csv.DataDescriptionVariables (units)Temporal ResolutionData Source (Reference)crop_calendarStart and end of growing seasonsos (day of the year), eos (day of the year)StaticWorld Cereal (Franch et al, 2022)fparfraction of absorbed photosynthetically active radiationfpar (%)Dekadal (3 times a month; 1-10, 11-20, 21-31)European Commission's Joint Research Centre (EC-JRC, 2024)ndvinormalized difference vegetation index-approximately weeklyMOD09CMG (Vermote, 2015)meteotemperature, precipitation (prec), radiation, potential evapotranspiration (et0), climatic water balance (= prec - et0)  tmin (C), tmax (C), tavg (C), prec (mm0, et0 (mm), cwb (mm), rad (J m-2 day-1)dailyAgERA5 (Boogaard et al, 2022)soil_moisturesurface soil moisture, rootzone soil moisturessm (kg m-2), rsm (kg m-2)dailyGLDAS (Rodell et al, 2004)soilavailable water capacity, bulk density, drainage classawc (c m-1), bulk_density (kg dm-3), drainage class (category)staticWISE Soil database (Batjes, 2016)locationcentroidlatitude, logitudestatic yieldend-of-season yieldyield (t ha-1)yearlyVarious country or region specific sources (see crop_statistics_... in https://github.com/BigDataWUR/AgML-CY-Bench/tree/main/data_preparation) Folder structurecybench-data: The CY-Bench dataset has been structure at first level by crop type and subsequently by country. For each country, the folder name follows the ISO 3166-1 alpha-2 two-character code. A separate .csv is available for each predictor data and crop calendar as shown below. The csv files are named to reflect the corresponding country and crop type e.g. variable_croptype_country.csv.CY-Bench│└─── maize│   ││   └─── AO│   │   -- crop_calendar_maize_AO.csv│   │   -- fpar_maize_AO.cs│   │   -- location_maize_AO.csv│   │   -- meteo_maize_AO.csv│   │   -- ndvi_maize_AO.csv│   │   -- soil_maize_AO.csv│   │   -- soil_moisture_maize_AO.csv│   │   -- yield_maize_AO.csv│   │ │   └─── AR│       -- crop_calendar_maize_AR.csv│       -- fpar_maize_AR.csv│       -- ...│   └─── wheat│   ││   └─── AR│   │   -- crop_calendar_wheat_AR.csv│   │   -- fpar_wheat_AR.csv│   │   ...Example : CSV data content for maize in country XX└─── crop_calendar_maize_X.csv│   -- crop_name (name of the crop)│   -- adm_id (unique identifier for a subnational unit)│   -- sos (start of crop season)│   -- eos (end of crop season)│    └─── fpar_maize_X.csv│   -- crop_name│   -- adm_id │   -- date (in the format YYYYMMdd)│   -- fpar│└─── location_maize_X.csv│   -- crop_name│   -- adm_id │   -- latitude│   -- longitude│└─── meteo_maize_X.csv│   -- crop_name│   -- adm_id │   -- date (in the format YYYYMMdd)│   -- tmin (minimum temperature)│   -- tmax (maximum temperature)│   -- prec (precipitation)│   -- rad (radiation)│   -- tavg (average temperature)│   -- et0 (evapotranspiration)│   -- vpd (vapor pressure deficit)│   -- cwb (crop water balance)│ └─── ndvi_maize_X.csv│   -- crop_name│   -- adm_id│   -- date (in the format YYYYMMdd)│   -- ndvi  │   └─── soil_maize_X.csv│   -- crop_name│   -- adm_id│   -- awc (available water capacity)│   -- bulk_density│   -- drainage_class│   └─── soil_moisture_maize_X.csv│   -- crop_name│   -- adm_id│   -- date (in the format YYYYMMdd)│   -- ssm (surface soil moisture)│   -- rsm ()│   └─── yield_maize_X.csv│   -- crop_name│   -- country_code│   -- adm_id│   -- harvest_year│   -- yield│   -- harvest_area│   -- productioncentroids.zip and polygons.zip include shapes or geometries as centroids ( x and y coordinates) and polygons (multipolygons) of administrative regions respectively. They are organized as follows:centroids│   └─── AO│   │   -- AO.cpg│   │   -- AO.dbf│   │   -- AO.prj│   │   -- AO.shp│   │   -- AO.shx│   └─── AR│   │   -- AR.cpg│   │   -- AR.dbf│   │   -- AR.prj│   │   -- AR.shp│   │   -- AR.shx...polygons│   └─── AO│   │   -- AO.cpg│   │   -- AO.dbf│   │   -- AO.prj│   │   -- AO.shp│   │   -- AO.shx│   └─── AR│   │   -- AR.cpg│   │   -- AR.dbf│   │   -- AR.prj│   │   -- AR.shp│   │   -- AR.shx...Data accessThe full dataset can be downloaded directly from Zenodo or using the zenodo_get``` libraryLicense and citationWe kindly ask all users of CY-Bench to properly respect licensing and citation conditions of the datasets included. Version Notes1.0 is the dataset submitted to NeurIPS Datasets and Benchmarks Track. The paper and discussions are here: https://openreview.net/forum?id=jkJDNG468g#discussion1.1 and 1.2 fix some issues with column names and mismatches in adm_id between yield data and input data.1.3 includes location information in the form of centroids and polygons of admin regions.1.4 updates the fpar data for 2023. fpar data was incomplete for 2023 in earlier versions (due to unavailability in the data source itself).1.5 fixes an issue in crop calendar1.6 fixes an issue in ndvi time series1.7 updates storage precision to 3 decimal places to reduce data size1.8 filter out invalid yield values1.9 Add vpd. Add location. ET0 obtained from AgERA5 (was AQUASTAT-FAO ). Use AgERA5 2.0 (was AgERA5 1.1)

During the primary feeding season, marine mammals often accumulate fat reserves, primarily in the form of blubber. Despite the ecological significance of feeding, uncertainty remains surrounding the timing of energy store accumulation in Hudson Bay beluga whales (Delphinapterus leucas (Pallas, 1776)). Blubber samples were collected from whales hunted by Inuit along the whale’s migration route, from 2015 to 2021 (excl. 2018). Sampling occurred in spring and fall, assumed to represent feeding in winter and summer, respectively. We analyzed blubber for lipid content and adipocyte size, two related indices of lipid dynamics, across three blubber sections (outer, middle, and inner). We found interannual variability in the season with the highest fat content, with some years showing higher lipid content in spring than fall. While adipocyte size did not differ seasonally, minima were observed in 2017 and 2019. Fat stores differed across blubber sections, with the highest lipid content and largest adipocytes in the middle section. The observed seasonal variation indicates there is no consistent season in which Hudson Bay beluga whales predominantly accumulate fat. Consequently, building energy stores in the form of blubber may not be the primary driving force behind the beluga whale’s migration between the wintering and summering areas.

This collection contains GWAS and XWAS summary statistics for hearing loss phenotypes from the Canadian Longitudinal Study on Aging (CLSA).GWAS summary statistics for autosomal chromosomesMetabolic and sensory GWAS in the discovery cohort including participants from all ancestries:met_better_gwas_sorted_noNA_add.gzsen_better_gwas_sorted_noNA_add.gzSex-stratified GWAS in the discovery cohort (all ancestries):met_better_females_gwas_sorted_noNA_add.gzmet_better_males_gwas_sorted_noNA_add.gzsen_better_females_gwas_sorted_noNA_add.gzsen_better_males_gwas_sorted_noNA_add.gzGWAS in European-only participants (used for fine-mapping):met_better_cluster4_gwas_sorted_noNA_add.gzsen_better_cluster4_gwas_sorted_noNA_add.gzGWAS in European-only participants excluding the opposite phenotype as a covariate (for genetic correlation analyses):met_better_cluster4_gwas_noSen_sorted_noNA_add.gzsen_better_cluster4_gwas_noMet_sorted_noNA_add.gzXWAS summary statistics for the X chromosomemet_xwas_stratFisher_model2.xstrat.linear.gzsen_xwas_stratFisher_model2.xstrat.linear.gz

This collection contains GWAS and XWAS summary statistics for hearing loss phenotypes from the Canadian Longitudinal Study on Aging (CLSA).GWAS summary statistics for autosomal chromosomesMetabolic and sensory GWAS in the discovery cohort including participants from all ancestries:met_better_gwas_sorted_noNA_add.gzsen_better_gwas_sorted_noNA_add.gzSex-stratified GWAS in the discovery cohort (all ancestries):met_better_females_gwas_sorted_noNA_add.gzmet_better_males_gwas_sorted_noNA_add.gzsen_better_females_gwas_sorted_noNA_add.gzsen_better_males_gwas_sorted_noNA_add.gzGWAS in European-only participants (used for fine-mapping):met_better_cluster4_gwas_sorted_noNA_add.gzsen_better_cluster4_gwas_sorted_noNA_add.gzGWAS in European-only participants excluding the opposite phenotype as a covariate (for genetic correlation analyses):met_better_cluster4_gwas_noSen_sorted_noNA_add.gzsen_better_cluster4_gwas_noMet_sorted_noNA_add.gzXWAS summary statistics for the X chromosomemet_xwas_stratFisher_model2.xstrat.linear.gzsen_xwas_stratFisher_model2.xstrat.linear.gz

Freshwater sources from Greenland and Antarctica. For details see https://github.com/NASA-GISS/freshwater-forcing-workshop and https://doi.org/10.5194/egusphere-2025-1940

Data files for the work "A Dynamical bulk-boundary Correspondence in Two Dimensional Topological Matter". Included are dat files with data for the return rates and Loschmidt eigenvalues, organised by folders. The structure is as follows.MEV refers to eigenvalues, and Bulk, Flake, Ribbon refer to the geomtries. Quench protocols are given by {100 \mu_0,100\Delta_0,100\mu_1,100\Delta_1}, all measured in units of J. System sizes should be self explanatory. Folders for each time are labelled by time steps which start from 0.01/J and progress in steps of 0.03/J. For the ribbon and bulk eigenvalues the data are organized by momenta from 0 to 2pi in steps of 2\pi/N. Only the magnitude of the lowest few eigenvalues are ever kept, not a complete set.For the return rate the files contain the time, the return rate, and the first deriviative of the return rate.Further explanation can be found in the published paper.This work was supported by the National Science Centre (NCN, Poland) under the grant 2024/53/B/ST3/02600 and by NSERC via the Discovery grants program.