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.

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.

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.

To understand the proximate and ultimate causes that shape acoustic communication in animals, objective characterizations of the vocal repertoire of a given species are critical, as they provide the foundation for comparative analyses among individuals, populations and taxa. Progress in this field has been hampered by a lack of standard in methodology, however. One problem is that researchers may settle on different variables to characterize the calls, which may impact on the classification of calls. More important, there is no agreement how to best characterize the overall structure of the repertoire in terms of the amount of gradation within and between call types. Here, we address these challenges by examining 912 calls recorded from wild chacma baboons (Papio ursinus). We extracted 118 acoustic variables from spectrograms, from which we constructed different sets of acoustic features, containing 9, 38, and 118 variables; as well 19 factors derived from principal component analysis. ...

Czech Republic Consumption: per Capita Avg: Food: ME: Cheese: Hard, Soft and Blue data was reported at 11.690 kg in 2022. This records a decrease from the previous number of 12.500 kg for 2021. Czech Republic Consumption: per Capita Avg: Food: ME: Cheese: Hard, Soft and Blue data is updated yearly, averaging 10.880 kg from Dec 1998 (Median) to 2022, with 25 observations. The data reached an all-time high of 12.500 kg in 2021 and a record low of 6.100 kg in 1998. Czech Republic Consumption: per Capita Avg: Food: ME: Cheese: Hard, Soft and Blue data remains active status in CEIC and is reported by Czech Statistical Office. The data is categorized under Global Database’s Czech Republic – Table CZ.H022: Food and Beverage Consumption: per Capita Average.

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.

Russia Avg Consumer Price: Cheese: Hard and Soft data was reported at 514.770 RUB/kg in 18 Feb 2019. This records a decrease from the previous number of 514.810 RUB/kg for 11 Feb 2019. Russia Avg Consumer Price: Cheese: Hard and Soft data is updated weekly, averaging 283.470 RUB/kg from Jan 2008 (Median) to 18 Feb 2019, with 569 observations. The data reached an all-time high of 514.810 RUB/kg in 11 Feb 2019 and a record low of 200.320 RUB/kg in 28 Sep 2009. Russia Avg Consumer Price: Cheese: Hard and 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.PA001: Average Consumer Price: Weekly.

The timing of transition out of one life history phase determines where in the seasonal succession of environments the next phase is spent. Shifts in the general environment (e.g., seasonal climate) affect the expected fitness for particular transition dates. Variation in transition date also leads to temporal variation in the social environment. For instance, early transition may confer a competitive advantage over later individuals. If so, the social environment will impose frequency- and density-dependent selection components. In effect, the general environment imposes hard selection while the social environment imposes soft selection on phenology. We examined hard and soft selection on seedling emergence time in an experiment on Brassica rapa. In monoculture (uniform social environment), early emergence results in up to a 1.5-fold increase in seed production. In bi-cultures (heterogeneous social environment), early-emerging plants capitalized on their head start, suppressing their late neighbors and increasing their fitness advantage to as much as 38-fold, depending on density. We devised a novel adaptation of contextual analysis to partition total selection (i.e., Cov(ω, z)) into the hard and soft components. Hard and soft components had similar strengths at low density, whereas soft selection was five times stronger than hard at high density.

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.

Russia Consumer Price Index (CPI): Weights: Food: Cheese: Hard & Soft data was reported at 0.880 % in 2019. This records a decrease from the previous number of 0.892 % for 2018. Russia Consumer Price Index (CPI): Weights: Food: Cheese: Hard & Soft data is updated yearly, averaging 0.879 % from Dec 2012 (Median) to 2019, with 8 observations. The data reached an all-time high of 0.907 % in 2015 and a record low of 0.819 % in 2013. Russia Consumer Price Index (CPI): Weights: Food: 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.IA027: Consumer Price Index: Weights.

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.

Seafloor substrate (i.e., hard vs. soft bottom) from 0 to up to 30 m depths around islands/atolls in Pacific Remote Island Areas produced by the NOAA Ecosystem Sciences Division (ESD). The islands/atolls include Howland, Baker, Jarvis, Kingman Reef, Palmyra Atoll, Johnston Atoll, Wake Atoll. This is a preliminary product derived from an unsupervised classification of depth-invariant indices prepared from high resolution satellite images. Unsupervised classification segmented the indices into multiple classes, and the binary substrate map was produced by mapping interpreter's informed judgments in attributing the predicted classes. Where required, area-specific class attribution and minor manual editing were undertaken to remove inaccurate predictions.

Russia Consumer Price Index (CPI): Prev Dec=100: Cheese: Hard & Soft data was reported at 104.480 Prev Dec=100 in Dec 2018. This records an increase from the previous number of 103.080 Prev Dec=100 for Nov 2018. Russia Consumer Price Index (CPI): Prev Dec=100: Cheese: Hard & Soft data is updated monthly, averaging 104.865 Prev Dec=100 from Jan 1995 (Median) to Dec 2018, with 288 observations. The data reached an all-time high of 233.010 Prev Dec=100 in Dec 1995 and a record low of 89.820 Prev Dec=100 in Sep 2008. Russia Consumer Price Index (CPI): 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.IA018: Consumer Price Index: Previous December=100: Food.

Kind of data

Aggregate data [agg]

Mode of data collection

Other [oth]

The Hard and Soft Tissue Dental Lasers market has rapidly evolved over the past few years, positioning itself as a transformative force in dental care. These specialized lasers are utilized in a variety of dental procedures, enabling dentists to perform both hard tissue (like bone and teeth) and soft tissue (like gu

The data can be directly opened and visualized in ParaView.

Preliminary hard and soft seafloor substrate map derived from an unsupervised classification of multibeam backscatter and bathymetry derivatives at Swains Island, American Samoa. The dataset was created from gridded (40 m cell size) multibeam bathymetry derivatives collected aboard R/V AHI, and NOAA ship Hi'ialakai; two scales of bathymetric variance and bathymetric rugosity. Backscatter data were from a 300 kHz Simrad EM300 and a 240 kHz Reson 8101 sonar, gridded at 5 m. Very limited seafloor photographs for groundtruthing are available for Swains Island and therefore no supervised classification was performed and we are unable to visually or empirically evaluate the accuracy of the unsupervised classification seafloor substrate map. However, in locations such French Frigate Shoals, NWHI, and Tutuila, American Samoa, where ground truth data are available, the unsupervised classification method is a robust predictor of substrate type in similar depth ranges and seafloor environments. Since groundtruthing was not used to validate the unsupervised classification at Swains Island extreme caution should be used when examining these data to locate habitat of biological significance. The map should be used in conjunction with bathymetric derivatives such as rugosity, slope, and Bathymetric Position Index (BPI).

The Soft Contact Lenses and Hard Contact Lenses market has witnessed significant growth over the past few years, driven by an increase in the demand for vision correction solutions and the rising prevalence of eye disorders. Soft contact lenses, known for their comfort and ease of use, have gained immense popularity

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

Names of factors, components and pillars.