Abstract Short-term mining planning typically relies on samples obtained from channels or less-accurate sampling methods. The results may include larger sampling errors than those derived from diamond drill hole core samples. The aim of this paper is to evaluate the impact of the sampling error on grade estimation and propose a method of correcting the imprecision and bias in the soft data. In addition, this paper evaluates the benefits of using soft data in mining planning. These concepts are illustrated via a gold mine case study, where two different data types are presented. The study used Au grades collected via diamond drilling (hard data) and channels (soft data). Four methodologies were considered for estimation of the Au grades of each block to be mined: ordinary kriging with hard and soft data pooled without considering differences in data quality; ordinary kriging with only hard data; standardized ordinary kriging with pooled hard and soft data; and standardized, ordinary cokriging. The results show that even biased samples collected using poor sampling protocols improve the estimates more than a limited number of precise and unbiased samples. A welldesigned estimation method corrects the biases embedded in the samples, mitigating their propagation to the block model.

Russia Consumer Price Index (CPI): Same Mth PY=100: Cheese: Hard & Soft data was reported at 104.480 Same Mth PY=100 in Dec 2018. This records an increase from the previous number of 103.500 Same Mth PY=100 for Nov 2018. Russia Consumer Price Index (CPI): Same Mth PY=100: Cheese: Hard & Soft data is updated monthly, averaging 110.530 Same Mth PY=100 from Jan 1995 (Median) to Dec 2018, with 288 observations. The data reached an all-time high of 383.220 Same Mth PY=100 in Jan 1995 and a record low of 90.900 Same Mth PY=100 in Jan 2009. Russia Consumer Price Index (CPI): Same Mth PY=100: Cheese: Hard & Soft data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Inflation – Table RU.IA014: Consumer Price Index: Same Month Previous Year=100: Food.

Russia Average Consumer Price: Foodstuffs: Period End: Cheese: Hard & Soft data was reported at 502.550 RUB/kg in 2018. This records an increase from the previous number of 478.880 RUB/kg for 2017. Russia Average Consumer Price: Foodstuffs: Period End: Cheese: Hard & Soft data is updated yearly, averaging 141.490 RUB/kg from Dec 1993 (Median) to 2018, with 26 observations. The data reached an all-time high of 502.550 RUB/kg in 2018 and a record low of 2.760 RUB/kg in 1993. Russia Average Consumer Price: Foodstuffs: Period End: Cheese: Hard & Soft data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Prices – Table RU.PD002: Average Consumer Foodstuffs Price: Annual.

When building the tree of life, variability of phylogenetic signal is often accounted for by partitioning gene sequences and testing for differences. The same considerations however are rarely applied to morphological data, potentially undermining its use in evolutionary contexts. Here we apply partition heterogeneity tests to 59 animal datasets to demonstrate that significant differences exist between the phylogenetic signal conveyed by ‘hard’ and ‘soft’ characters (bones, teeth and shells versus myology, integument etc). Furthermore, the morphological partitions differ significantly in their consistency relative to independent molecular trees. The observed morphological differences correspond with missing data biases, and as such their existence presents a problem not only for phylogeny reconstruction, but also for interpretations of fossil data. Evolutionary inferences drawn from clades in which hard, readily-fossilizable characters are relatively less consistent and different from other morphology (mammals, bivalves) may be less secure. More secure inferences might be drawn from the fossil record of clades that exhibit fewer differences, or exhibit more consistent hard characters (fishes, birds). In all cases it will be necessary to consider the impact of missing data on empirical data, and the differences that exist between morphological modules.

Kind of data

Aggregate data [agg]

Mode of data collection

Other [oth]

This data product contains statistics on wheat-including the five classes of wheat: hard red winter, hard red spring, soft red winter, white, and durum-and rye. Includes data published in the monthly Wheat Outlook and previously annual Wheat Yearbook. Data are monthly, quarterly, and/or annual depending upon the data series. Most data are on a marketing year basis, but some are calendar year.

Abstract (en): In this paper we investigate whether differences exist among forecasts using real-time or latest-available data to predict gross domestic product (GDP). We employ mixed-frequency models and real-time data to reassess the role of surveys and financial data relative to industrial production and orders in Germany. Although we find evidence that forecast characteristics based on real-time and final data releases differ, we also observe minimal impacts on the relative forecasting performance of indicator models. However, when obtaining the optimal combination of soft and hard data, the use of final release data may understate the role of survey information.

Rankings and geographic distributions: WEF-GCI versus CSI.

Russia Consumer Price Index (CPI): Weekly Estimate: Prev Dec=100: Cheese: Hard & Soft data was reported at 101.200 Prev Dec=100 in 13 Aug 2018. This records an increase from the previous number of 101.100 Prev Dec=100 for 06 Aug 2018. Russia Consumer Price Index (CPI): Weekly Estimate: Prev Dec=100: Cheese: Hard & Soft data is updated weekly, averaging 101.150 Prev Dec=100 from Jul 2017 (Median) to 13 Aug 2018, with 58 observations. The data reached an all-time high of 103.400 Prev Dec=100 in 25 Dec 2017 and a record low of 100.100 Prev Dec=100 in 30 Apr 2018. Russia Consumer Price Index (CPI): Weekly Estimate: Prev Dec=100: Cheese: Hard & Soft data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Inflation – Table RU.IA007: Consumer Price Index: Weekly Estimate.

Substrate, Hard bottom vs. Soft bottom: This is a preliminary product. Cell values reflect whether the seafloor is hard bottom or soft bottom based on an unsupervised classification run using ArcGIS software with the Spatial Analyst extension. The classifications are based on backscatter, bathymetry, acoustic derivatives and optical data. Please see the individual metadata records for additional information on sources used and data processing steps for each location where data are available.

To study the reactions of funders to inconsistent information and how they interpret it, we collected data for this study. A total of 245 subjects, simulating funders, provided ratings for 199 peer-to-peer funding requests, each depicted by a narrative (soft message) and a score (hard message). The dataset was collected from eight series of surveys (questionnaires), with each series containing three types of surveys: both direct and indirect hard messages, only direct hard messages, and only indirect hard messages. Additionally, we collected information about the experience in investment and product preferences of the subjects in the dataset. The dataset mainly includes the ratings, experience, preference of the subjects.

Spearman’s rho: Correlation between the WEF-GCI and CSI.

The Hard Cheese and Soft Cheese market is a dynamic segment of the global dairy industry, recognized for its diverse applications and culinary versatility. These cheeses not only cater to the palates of consumers around the world but also play a crucial role in the food service and retail sectors. While hard cheeses

This dataset contains .gslib files (http://www.gslib.com/gslib_help/format.html, access 18-05-2021) of training images, soft data and hard data. The files following files correspond to geological element 1: softdataRed1.gslib: Soft data redox softdataGeo1.gslib: Soft data geology hdgeo1.gslib: Hard data ti1.gslib: Training image The files following files correspond to geological element 2: softdataRed2.gslib: Soft data redox softdataGeo2.gslib: Soft data geology hdgeo2.gslib: Hard data ti2.gslib: Training image This dataset also include the parameter files for running the simulations for both geological elements in DeeSse software (https://www.ephesia-consult.com/portfolio/deesse/, last access 18/5-2021)

Hard and soft bottom seafloor substrate data and maps for the NOAA Habitat Blueprint West Hawaii Focus Area (WHFA) in the Main Hawaiian Islands. Cell values in the raster datasets reflect whether the seafloor is hard bottom or soft bottom based on an unsupervised classification run using ArcGIS software with the Spatial Analyst extension. The classifications are based on bathymetry, acoustic derivatives and verified using seafloor optical data. The seafloor substrate map and associated data are accessible online via the Pacific Islands Benthic Habiat Mapping Center (PIBHMC) website at www.soest.hawaii.edu/pibhmc.

Seafloor substrate (i.e., hard vs. soft bottom) maps from 0 to up to 50 m depths around select islands and atolls in American Samoa, the Mariana Archipelago, and the Pacific Remote Island Areas were produced by the NOAA Fisheries Ecosystem Sciences Division (ESD). The islands and atolls include Tutuila, Ofu and Olosega, Tau, and Rose in American Samoa; Anatahan, Maug, Aguijan, Pagan, Rota, Tinian, Saipan, and Guam in the Mariana Archipelago; and Howland, Jarvis, Kingman, Palmyra, and Johnston in the Pacific Remote Island Areas. This is a preliminary product, derived from integrating two existing map products: hard and soft seafloor substrate maps derived from an unsupervised classification of multibeam backscatter and bathymetry derivatives produced by ESD, and shallow-water benthic habitat maps generated by the NOAA Centers for Coastal Ocean Science. The resulting maps were then updated with ESD's groundtruth data, including biological survey data and benthic cover data derived from the analysis of seafloor images. The final maps were interpolated to fill in gaps and smoothed to remove isolated pixels, and the substrate data were constrained up to 50-m depths. For the Pacific Remote Island Areas where no benthic habitat maps were available, hard and soft substrate maps were newly generated from high spatial resolution satellite images.

Preliminary hard and soft seafloor substrate map derived from an unsupervised classification of multibeam backscatter and bathymety derivatives at French Frigate Shoals, Northwestern Hawaiian Islands, USA. The dataset was derived using a combination of Simrad em3002d and Reson 8101 backscatter, bathymetric variance and bathymetric rugosity. The sonar frequencies are 300 kHz and 240 kHz for the em3002d and the 8101 data respectively and all data were resampled to 5 m grid cell size prior to the classification. Initial supervised classifications of the backscatter data into hard and soft seafloor substrates, using seafloor photographs for groundtruthing and to define regions of interest, were used to define unsupervised class types and to visually evaluate the accuracy of the unsupervised classification seafloor substrate map.

Data collected from World Bank data catalog https://datacatalog.worldbank.org

Preliminary hard and soft seafloor substrate map classified from sidescan data and bathymetric derivatives at Apra Harbor, Guam U.S. Territory. The dataset was created using Bathymetric Position Index (bpi) zones derived from Reson SeaBat 8125 multibeam data at a 1 m grid cell size, and Klein 3000 sidescan sonar data. The sonar frequency is 455 kHz for the Reson Seabat 8125 multibeam echosounder. Additional information on multibeam and sidescan datasets can be found in the Data Acquisition and Processing Report (DAPR) that can be accessed at: www.soest.hawaii.edu/pibhmc. Classification of the bathymetry and sidescan data into hard and soft seafloor substrates were validated using groundtruth data collected for the US Navy in Appendix J of the Final EIS Statement: Guam and CNMI Military Relocation accessed at: http://www.guambuildupeis.us/final_documents. Survey site images can be found at http://guamreeflife.com/htm/reeftour/cvn_survey_sites.htm. Although hard and soft classes from the substrate map are highly correlated with those from the optical validation data, the substrate map should be used with caution as groundtruth data were mostly collected at areas of known hard bottom in less than 60 ft of water depth.

Phylogenetic data matrices used to assess the differences between hard and soft morphological characters For more details see: Fossilization causes organisms to appear erroneously primitive by distorting evolutionary trees Robert S. Sansom & Matthew A. Wills Scientific Reports 3, Article number: 2545 (2013) doi:10.1038/srep02545