A flood frequency table and a plot was created using the average return period (2, 5, 10, 25, 50, 100, 250 and 500 year) flow obtained using different moving window time steps. The flows corresponding to different time steps were generated using Log Pearson Type III approach. The python code for generating the flow in included in this resource. The annual peakflow data of Tippecanoe River near Ora, IN (03331500) was used for this work.

Quality assessment of factorial designs, particularly mixed-level factorial designs, is a nontrivial task. Existing methods for orthogonal arrays include generalized minimum aberration, a modification thereof that was proposed by Wu and Zhang for mixed two- and four-level arrays, and minimum projection aberration. For supersaturated designs, E(s2) or χ2-based criteria are widely used. Based on recent insights by Grömping and Xu regarding the interpretation of the projected aR values used in minimum projection aberration, this article proposes three new types of frequency tables for assessing the quality of level-balanced factorial designs. These are coding invariant, which is particularly important for designs with qualitative factors. The proposed tables are used in the same way as those used in minimum projection aberration and behave more favorably when used for mixed-level arrays. Furthermore, they are much more manageable than the above-mentioned approach by Wu and Zhang. The article justifies the proposed tables based on their statistical information content, makes recommendations for their use, and compares them with each other and with existing criteria. As a byproduct, it is shown that generalized minimum aberration refines the established expected χ2 criterion for level-balanced supersaturated designs.

This is a pdf containing a flood frequency table for Sugar Creek at Crawfordsville, IN. The flow values are in cfs. The time steps represented include 2, 5, 10, 15, 25, and 30 years. The return periods included are 2, 5, 10, 25, 50, 100, 250, and 500 years. Instructions on how to create the table are included.

The SAS command file checks EPG data for errors. It will always run. However, you will only get the correct output if there are no errors. The data sets "simple psyllid" and "simple aphid" have no errors. Errorchecker will return two tables. The first is a record of the waveforms. Check to make sure that all waveforms are correct. A number of common errors will show here. PLEASE note that Np is a different waveform from NP or nP or np. Also, "NP" is different from " NP" and "NP " or " NP ". I wrote the code to be insensitive to these conditions using the condense() function to eliminate spaces and the upcase() function to make all letters capitals. However, it is safer to correct the problem rather than relying on the program. The second table is a frequency table showing all the transitions and transitional probabilities. Check to make sure that all transitions are possible. Your data is clean if you get these two tables and there are no problems evident in the tables.

Cochlear implants (CI) utilize default frequency allocation tables (“pitch maps”) to distribute the frequency range important for speech perception across their electrode array. Default pitch maps do not address the significant pitch-place mismatch that is inherent in cochlear implantation, nor the variability between subjects or array lengths. Recent research has utilized postoperative high-resolution flat-panel computed tomography (CT) imaging to measure the precise location of electrode contacts within an individual's cochlea, to generate a custom pitch map and decrease the pitch-place mismatch. The objective was to determine whether chronic use of CT pitch maps would improve CI user performance in the areas of speech and music perception, as compared to the non-custom default pitch maps provided by the CI manufacturer. A cohort of 10 experienced CI users (14 CI ears) were recruited to receive CT scans and then use a custom CT pitch map for 1 month. The efficacy of these maps was measured using a battery of speech and music tests. No change was found at the group level; however, large inter-subject variability of the benefit of the CT maps was correlated to CI electrode array placement. This application of a custom, strict CT mapping is not beneficial for all CI users. Results may be limited by long acclimation periods to default pitch maps before CT map usage. Methods Subjects Study subject demographics, describe the 10 MED-EL cochlear implant (CI) participants (14 CI ears). Four men and six women participated, with an average age of 64.3 ± 12.2 years. All participants were adults with at least 6 months of CI listening experience, implanted with a MED-EL CI, postlingually deafened, native speakers of American English, and used oral/aural communication as their primary modality of communication. The mean length of CI use was 3.6 ± 1.8 years and the mean duration of severe-profound hearing loss before implantation was 6.9 ± 9.6 years. The CI users utilized a variety of sound processor models and processing strategies. Subjects were excluded if they had intracochlear electrodes with open or short circuits, 4 or more extracochlear electrodes (≥1/3 of the array), or documented concomitant conditions that may affect performance (e.g., cognitive impairment). The Institutional Review Board at the University of California, San Francisco (UCSF) approved this study and informed consent was obtained from all participants. Study Design Experienced CI recipients were asked to use a CT experimental pitch map, full-time, for one month. Subjects completed a battery of speech and music tasks at the beginning and end of month on both their clinical (Clin) and experimental (Exp) pitch maps. CI users entered the study with chronic exposure (6+ months) to their clinical pitch maps. Our primary hypothesis, namely that CT mapping would improve speech and music perception, was thus best revealed by comparing performance following chronic use of each map, specifically the clinical map at the first test session and the experimental CT map at the second test session. CT Experimental Map The CT map experimental was intended to create a strict tonotopic pitch map while utilizing the full 70-8500Hz bandwidth available in the CI software. To this end, the characteristic frequency (CF) of each electrode was calculated and, in the CI programming software, the center of each channel band was matched to a corresponding electrode CF. This approach created a frequency allocation table that minimized pitch-place mismatch, such that frequencies of incoming sounds were directed to the most anatomically correct location in the cochlea. If there were electrodes whose CFs fell too far outside the programmable frequency range (>4 semitones from a channel center), then they were deactivated. The only fitting aspect that was explicitly changed for the CT maps was the frequency allocation table. Other fitting parameters, such as upper and lower electrical stimulation levels, for example, were held constant between the clinical and experimental maps. Test Conditions All study participants used their sound processors for the chronic trial period(s) and any corresponding speech and music testing. Speech and music stimuli were presented at 65 dBA and the non-test ear was masked. All CI users were asked if they could hear the test stimuli with the non-test ear while the CI on their test ear was turned off; all subjects confirmed being unable to hear the test stimuli with the non-test ear. During speech and music testing, any additional sound processing (i.e., directional microphones, steady-state noise reduction algorithms) were deactivated, and the volume and sensitivity settings were fixed to 100% and 75%, respectively. Upper stimulation levels (MCLs) for bilateral users were globally adjusted as needed to ensure a comfortable listening level in both ears; any global MCL adjustments needed for testing were replicated in both control and experimental fittings. Participants completed the experiment using a tablet computer (Microsoft Surface Pro 3) and were given the option of using a touchscreen or mouse. The computer ran the speech and music stimuli with the following software programs: Windows Media Player (v12, 2009), MATLAB (vR2012b, Mathworks, Natick, MA, USA), and LabView (v11.0, National Instruments, Austin, TX, USA). Test Metrics CNC Words and Phonemes The Consonant-Nucleus-Consonant (CNC) Monosyllabic Word Test (Peterson and Lehiste, 1962),used to test recognition of open-set monosyllabic words in quiet, was sourced from the Minimum Speech Test Battery for Adult CI Users (Luxford et al., 2001; MTSB, 2011). Sentences in Noise The Bamford-Kowal-Bamford Speech-in-Noise (BKB-SIN) Test (Bench, Kowal and Bamford, 1979; Etymōtic Research, 2005; Luxford et al., 2001) employs a modified adaptive approach wherein sentences are presented at a fixed level and four-talker babble (Auditec of St. Louis, 1971) is presented at increasingly more difficult signal-to-noise ratios (SNRs). An SNR-50 score was calculated for each sentence list-pair, which represents the level of SNR (in dB) at which the subject correctly recognized exactly half of the keywords throughout. Synthetic Vowels The Iowa Medial Vowel Test (Tyler et al.,1986) utilizes eight synthetic vowel stimuli presented in an ‘‘/h/vowel/d/’’ context (e.g., had, hid, heed, etc.). After familiarization with a practice module with the test interface and all eight stimuli, the subject then completed a closed-set test with five blocks of eight tokens each, where the token stimulus order was randomized. The test was administered twice, yielding 80 total trials. Timbre Discrimination Musical timbre is conveyed by spectrotemporal features, beyond fundamental frequency and amplitude, that give a particular sound its characteristic tone color. Timbre perception is typically examined using musical instrument identification tasks that generally reveal poor performance in cochlear implant (CI) users (Kang et al., 2009; Gfeller et al., 2002; Spitzer et al., 2008). Relatively little is known, however, regarding timbre discrimination—the ability to differentiate between two notes of different timbres—a task which may provide greater insight into CI-mediated listening than whether or not instruments can be correctly identified. A task developed by the present investigators was used to measure timbre discrimination through acoustic blends of source instruments (clarinet, flute, French horn, tuba, violin, and cello) which were combined in varying amplitude ratios while maintaining constant overall volume (Gilbert et al., 2019a). Higher performance on the timbre discrimination task indicates better ability to distinguish between musical instrument stimuli using timbre-related cues only. Pitch Ranking Our lab designed a test paradigm to characterize pitch height (for pure tones) across the entire stimulable frequency range of the MED-EL CI (70-8500Hz) (Jiam et al., 2019). Better performance on this task is indicated by a lower number of errors. During the analysis of this test, we dropped/didn’t count the errors/responses that corresponded to areas of the frequency range where the Clinical and CT Maps were overlapping. Overlapping was defined as the upper ends of the channels and the lower ends of the channels were each within 3 ST of each other. Statistical Analysis All statistical analyses were completed in JASP software (JASP, Version 0.18.3; Amsterdam, Netherlands) using p<0.05 to determine statistical significance. The primary comparison of interest was between performance with the incoming Clinical Map and following one month of chronic use of the Experimental Map. Shapiro-Wilk tests were used to test normal distribution of the data. All the performance test results were normally distributed, and so paired sample student t-tests assessed whether mean performance on the clinical and experimental maps were different. Linear correlations between test results and subject- or ear-specific factors (i.e., CI array alignment, demographic data, and hearing history) were explored with either Pearson’s r (for parametric) or Spearman’s rho (for non-parametric) tests.

This workshop takes you on a quick tour of Stata, SPSS, and SAS. It examines a data file using each package. Is one more user friendly than the others? Are there significant differences in the codebooks created? This workshop also looks at creating a frequency and cross-tabulation table in each. Which output screen is easiest to read and interpret? The goal of this workshop is to give you an overview of these products and provide you with the information you need to determine whick package fits the requirements of you and your user.

This table contains the source list from Low Frequency Array (LOFAR) Low Band observations of the 3C 295 field at 62 MHz. The images of this field and the Bootes field made at 62 MHz reach a noise level of 5 mJy beam^-1, making them the deepest images ever obtained at this frequency. In total, the authors detect 329 sources in the 3C 295 62-MHz field image, covering an area of 17.0 square degrees out to a primary-beam attenuation factor of 0.4. From the observations, the authors derive Euclidean-normalized differential source counts. The 62-MHz source counts agree with previous GMRT 153 MHz and Very Large Array 74 MHz differential source counts, scaling with a spectral index of -0.7. The authors find that a spectral index scaling of -0.5 is required to match up the LOFAR 34 MHz source counts. This result is also in agreement with source counts from the 38 MHz 8C survey, indicating that the average spectral index of radio sources flattens toward lower frequencies. The authors also find evidence for spectral flattening using the individual flux measurements of sources between 34 and 1400 MHz and by calculating the spectral index averaged over the source population. To select ultra-steep spectrum (alpha < -1.1) radio sources that could be associated with massive high-redshift radio galaxies, the authors compute spectral indices between 62 MHz, 153 MHz, and 1.4 GHz for sources in the Bootes field. They cross-correlate these radio sources with optical and infrared catalogs and fit the spectral energy distribution to obtain photometric redshifts. They find that most of these ultra-steep spectrum sources are located in the 0.7 <~ z <~ 2.5 range. The Bootes and 3C 295 fields were simultaneously observed on 2012 April 12 as part of a multi-beam observation with the LOFAR LBA stations. The idea behind the multi-beam setup was to use the 3C 295 observations as a calibrator field to transfer the gain amplitudes to the (target) Bootes field. The pointing center of the 3C 295 field was J2000.0 RA, Dec = 14^h 11^m 20.9^s, +52^o 13' 55". The total integration time on both fields was 10.25 hr. The observing band for the 3C 295 field 62-MHz observations was 54 - 70 MHz, was centered at 62 MHz, with a full coverage bandwidth of 16 MHz. The synthesized beam for this observation had dimensions of 29 arcseconds x 18 arcseconds. An overview of the observations is given in Table 1 of the reference paper, and an overview of the image characteristics in Table 2 of the reference paper. This table was created by the HEASARC in January 2015 based on some of the contents of the machine-readable version of Table 3 from the reference paper, namely the 329 entries listing sources in the 3C 295 field detected at 62 MHz. The remaining entries in this table listing the sources detected in the Bootes field at a frequency of 62 MHz. and the sources detected in the 3C295 field at frequencies of 34 and 46 MHz, are available as the HEASARC tables LOFARBF62M, LOF3C29534 and LOF3C29546, respectively. This is a service provided by NASA HEASARC .

This table contains the source list from Low Frequency Array (LOFAR) Low Band observations of the 3C 295 field at 46 MHz. The image of this field made at 46 MHz reaches a noise level of 8 mJy beam^-1, making it the deepest image ever obtained at this frequency. In total, the authors detect 367 sources in the 3C 295 46-MHz field image, covering an area of 30.5 square degrees out to a primary-beam attenuation factor of 0.4. From these and simultaneous observations made at other low-band frequencies, the authors derive Euclidean-normalized differential source counts. The 62-MHz source counts agree with previous GMRT 153 MHz and Very Large Array 74 MHz differential source counts, scaling with a spectral index of -0.7. The authors find that a spectral index scaling of -0.5 is required to match up the LOFAR 34 MHz source counts. This result is also in agreement with source counts from the 38 MHz 8C survey, indicating that the average spectral index of radio sources flattens toward lower frequencies. The authors also find evidence for spectral flattening using the individual flux measurements of sources between 34 and 1400 MHz and by calculating the spectral index averaged over the source population. To select ultra-steep spectrum (alpha < -1.1) radio sources that could be associated with massive high-redshift radio galaxies, the authors compute spectral indices between 62 MHz, 153 MHz, and 1.4 GHz for sources in the Bootes field. They cross-correlate these radio sources with optical and infrared catalogs and fit the spectral energy distribution to obtain photometric redshifts. They find that most of these ultra-steep spectrum sources are located in the 0.7 <~ z <~ 2.5 range. The Bootes and 3C 295 fields were simultaneously observed on 2012 April 12 as part of a multi-beam observation with the LOFAR LBA stations. The idea behind the multi-beam setup was to use the 3C 295 observations as a calibrator field to transfer the gain amplitudes to the (target) Bootes field. The pointing center of the 3C 295 field was J2000.0 RA, Dec = 14^h 11^m 20.9^s, +52^o 13' 55". The total integration time on both fields was 10.25 hr. The '46-MHz' observing band for the 3C 295 field observations was from 40 - 54 MHz, with 25 sub-bands more or less evenly distributed within this frequency range, with a total bandwidth of 4.9 MHz. The synthesized beam for this observation had dimensions of 40 arcseconds x 24 arcseconds. An overview of the observations is given in Table 1 of the reference paper, and an overview of the image characteristics in Table 2 of the reference paper. This table was created by the HEASARC in January 2015 based on some of the contents of the machine-readable version of Table 3 from the reference paper, namely the 367 entries listing sources in the 3C 295 field detected at 46 MHz. The remaining entries in this table listing the sources detected in the Bootes field at a frequency of 62 MHz. and the sources detected in the 3C295 field at frequencies of 34 and 62 MHz, are available as the HEASARC tables LOFARBF62M, LOF3C29534 and LOF3C29562, respectively. This is a service provided by NASA HEASARC .

Natural frequency table of balanced gear.

PublicationPrimahadi Wijaya R., Gede. 2014. Visualisation of diachronic constructional change using Motion Chart. In Zane Goebel, J. Herudjati Purwoko, Suharno, M. Suryadi & Yusuf Al Aried (eds.). Proceedings: International Seminar on Language Maintenance and Shift IV (LAMAS IV), 267-270. Semarang: Universitas Diponegoro. doi: https://doi.org/10.4225/03/58f5c23dd8387Description of R codes and data files in the repositoryThis repository is imported from its GitHub repo. Versioning of this figshare repository is associated with the GitHub repo's Release. So, check the Releases page for updates (the next version is to include the unified version of the codes in the first release with the tidyverse).The raw input data consists of two files (i.e. will_INF.txt and go_INF.txt). They represent the co-occurrence frequency of top-200 infinitival collocates for will and be going to respectively across the twenty decades of Corpus of Historical American English (from the 1810s to the 2000s).These two input files are used in the R code file 1-script-create-input-data-raw.r. The codes preprocess and combine the two files into a long format data frame consisting of the following columns: (i) decade, (ii) coll (for "collocate"), (iii) BE going to (for frequency of the collocates with be going to) and (iv) will (for frequency of the collocates with will); it is available in the input_data_raw.txt. Then, the script 2-script-create-motion-chart-input-data.R processes the input_data_raw.txt for normalising the co-occurrence frequency of the collocates per million words (the COHA size and normalising base frequency are available in coha_size.txt). The output from the second script is input_data_futurate.txt.Next, input_data_futurate.txt contains the relevant input data for generating (i) the static motion chart as an image plot in the publication (using the script 3-script-create-motion-chart-plot.R), and (ii) the dynamic motion chart (using the script 4-script-motion-chart-dynamic.R).The repository adopts the project-oriented workflow in RStudio; double-click on the Future Constructions.Rproj file to open an RStudio session whose working directory is associated with the contents of this repository.

Obesity is found to have a significant impact on body image perception and overall well-being. This study examines the impact of body image and perceived stigmatization on the psychological wellbeing of obese women in Kumasi metropolis, Ghana. A sample of 231 obese women was selected from health shops and some fitness centers using snowballing technique (purposive, snowballing technique and convenience). The study employed the descriptive survey design and made use of both descriptive and inferential data analysis approaches. The body shape questionnaire BSQ-34, the inventory of the Stigmatization Situation (SSI) and finally, the psychological well-being tools were used. Also, frequency distributions mean, and standard deviation, Pearson correlation coefficient and simple linear regression analysis were employed using SPSS version 23. Our findings indicated that obese women in the Kumasi metropolis were significantly satisfied with their body image. This is a true reflection of their higher self-esteem and standard of living. The body image and perceived stigmatization on the psychological wellbeing of the obese do have some counselling implications. Counselors, nutritionists, and clinical psychologists address specific schemes such as binge eating, dieting, and exercising to build the self-esteem of obese women.

This table contains the source list from Low Frequency Array (LOFAR) Low Band observations of the 3C 295 field at 62 MHz. The images of this field and the Bootes field made at 62 MHz reach a noise level of 5 mJy beam^-1, making them the deepest images ever obtained at this frequency. In total, the authors detect 329 sources in the 3C 295 62-MHz field image, covering an area of 17.0 square degrees out to a primary-beam attenuation factor of 0.4. From the observations, the authors derive Euclidean-normalized differential source counts. The 62-MHz source counts agree with previous GMRT 153 MHz and Very Large Array 74 MHz differential source counts, scaling with a spectral index of -0.7. The authors find that a spectral index scaling of -0.5 is required to match up the LOFAR 34 MHz source counts. This result is also in agreement with source counts from the 38 MHz 8C survey, indicating that the average spectral index of radio sources flattens toward lower frequencies. The authors also find evidence for spectral flattening using the individual flux measurements of sources between 34 and 1400 MHz and by calculating the spectral index averaged over the source population. To select ultra-steep spectrum (alpha < -1.1) radio sources that could be associated with massive high-redshift radio galaxies, the authors compute spectral indices between 62 MHz, 153 MHz, and 1.4 GHz for sources in the Bootes field. They cross-correlate these radio sources with optical and infrared catalogs and fit the spectral energy distribution to obtain photometric redshifts. They find that most of these ultra-steep spectrum sources are located in the 0.7 <~ z <~ 2.5 range. The Bootes and 3C 295 fields were simultaneously observed on 2012 April 12 as part of a multi-beam observation with the LOFAR LBA stations. The idea behind the multi-beam setup was to use the 3C 295 observations as a calibrator field to transfer the gain amplitudes to the (target) Bootes field. The pointing center of the 3C 295 field was J2000.0 RA, Dec = 14^h 11^m 20.9^s, +52^o 13' 55". The total integration time on both fields was 10.25 hr. The observing band for the 3C 295 field 62-MHz observations was 54 - 70 MHz, was centered at 62 MHz, with a full coverage bandwidth of 16 MHz. The synthesized beam for this observation had dimensions of 29 arcseconds x 18 arcseconds. An overview of the observations is given in Table 1 of the reference paper, and an overview of the image characteristics in Table 2 of the reference paper. This table was created by the HEASARC in January 2015 based on some of the contents of the machine-readable version of Table 3 from the reference paper, namely the 329 entries listing sources in the 3C 295 field detected at 62 MHz. The remaining entries in this table listing the sources detected in the Bootes field at a frequency of 62 MHz. and the sources detected in the 3C295 field at frequencies of 34 and 46 MHz, are available as the HEASARC tables LOFARBF62M, LOF3C29534 and LOF3C29546, respectively. This is a service provided by NASA HEASARC .

Context

This project (2009-2011, http://su.avedas.com/converis/contract/321) aimed at developing language learning word cards with a language's most frequent words corresponding to the Common European Framework (CEFR). They were developed for nine languages important for trade and associated countries, both LWUTL (Swedish, Norwegian, Greek, Polish) and MWUTL (Arabic, English, Chinese, Russian and Italian), as a complementary learning material.

Word cards are "focused, efficient and certain”(Nation) because they stimulate the necessary mental effort required. A native word is on the one side of the card and the target word on the other. Before turning the card over, you are stimulated to think. It is then, when you think, that you learn. Research shows that you can learn 30-100 words in an hour which is far more efficient than conventional learning methods.

The Swedish Kelly-list is a freely available frequency-based vocabulary list that comprises general-purpose language of modern Swedish. The list has been generated from a large web-acquired corpus (SweWAC) of 114 mln. words dating from the 2010’s. It is adapted to the needs of language learners and contains 8 425 most frequent lemmas that cover 80% of SweWAC.

The way the Swedish Kelly-list is compiled, it is a reliable resource for suggesting lexical syllabus for CEFR-based courses in Swedish as well as for use in evaluating learner appropriate texts for different CEFR levels, for compiling course books, creating vocabulary exercises and tests, compiling dictionaries, and for a number of other language learning purposes and NLP applications. The list can be used by language learners and teachers, test creators, lexicographers, comparative linguists, corpus linguists, computational linguists, and many other user groups.

The Swedish Kelly-list is a freely available electronic resource and is distributed under the license agreement CC-BY-SA 3.0, LGPL 3.0. You are encouraged to make a reference to any of the articles describing this list if you use the Swedish Kelly-list.

Content

The headwords on the Swedish Kelly-list contain the following information, see also Table below:

1. id/running number (i.e. relative placement in the frequency band);
2. raw frequency (RF);
3. relative frequency , i.e. “word-per-million” (WPM);
4. CEFR level (A1, A2, B1, B2, C1, C2);
5. source of lemma (indication whether the headword comes from SweWAC, from translation list (T2) or has been manually added);
6. grammar information, i.e. article or infinitive marker;
7. lemma, sometimes provided together with its spelling/stylistic variant;
8. word class;
9. comments/examples for some of the headwords

Example of items in the Swedish Kelly-list

The information should be read in the following way: the verb “att vara” (Eng. “to be, to last”) has a colloquial variant “va”; it can be used in a phrase “var så god!” (Eng. “here you go!”); it has the rank “88” in the list and thus belongs to the language’s top 100 words. It has been used 2 624 032 times in SweWAC (RF) which gives 23 017,26 wpm value. The item belongs to the most important vocabulary for language learners and should be learnt at A1 CEFR level (here marked as “1”).

Acknowledgements

Publications * Johansson Kokkinakis, S. and Volodina, E. (2011). Corpus-based approaches for the creation of a frequency based vocabulary list in the EU project KELLY – issues on reliability, validity and coverage. eLex 2011, Slovenia. pdf * Kilgarriff Adam, Charalabopoulou Frieda, Gavrilidou Maria, Bondi Johannessen Janne, Khalil Saussan, Johansson Kokkinakis Sofie, Lew Robert, Sharoff Serge, Vadlapudi Ravikiran Volodina Elena. (2014). Corpus-Based Vocabulary lists for Language Learners for Nine Languages. Language Resources and Evaluation Journal 48.1: 121-163. Springer, Netherlands. http://dx.doi.org/10.1007/s10579-013-9251-2 Download pdf through open access * Volodina, E. & Johansson Kokkinakis, S. (2012). Introducing Swedish Kelly-list, a new lexical e-resource for Swedish. LREC 2012, Turkey. pdf * Volodina, E. & Johansson Kokkinakis, S. (2012). Swedish Kelly: Technical Report. GU-ISS-2012-01. The Swedish Language Bank, Gothenburg University. pdf * Frieda Charalabopoulou, Maria Gavrilidou, Sofie Johansson Kokkinakis, Elena Volodina 2012. Building corpus-informed word lists for L2 voc...

Wetlands are areas where water is present at or near the surface of the soil during at least part of the year. Wetlands provide habitat for many species of plants and animals that are adapted to living in wet habitats. Wetlands form characteristic soils, absorb pollutants and excess nutrients from aquatic systems, help buffer the effects of high flows, and recharge groundwater. Data on the distribution and type of wetland play an important role in land use planning and several federal and state laws require that wetlands be considered during the planning process.The National Wetlands Inventory (NWI) was designed to assist land managers in wetland conservation efforts. The NWI is managed by the US Fish and Wildlife Service.Dataset SummaryPhenomenon Mapped: WetlandsGeographic Extent: 50 United States plus Puerto Rico, the US Virgin Islands, Guam, American Samoa, and the Northern Mariana IslandsProjection: Web Mercator Auxiliary SphereVisible Scale: This layer preforms well between scales of 1:1,000,000 to 1:1,000. An imagery layer created from this dataset is also available which you can also use to quickly draw wetlands at smaller scales.Source: U.S. Fish and Wildlife ServiceUpdate Frequency: AnnualPublication Date: October 26, 2024This layer was created from the October 26, 2024 version of the NWI. The features were converted from multi-part to a single part using the Multipart To Singlepart tool. Features with more than 50,000 vertices were split with the Dice tool. The Repair Geometry tool was run on the features, using the OGC option.The layer is published with a related table that contains text fields created by Esri for use in the layer's pop-up. Fields in the table are:Popup Header - this field contains a text string that is used to create the header in the default pop-up System Text - this field contains a text string that is used to create the system description text in the default pop-upClass Text - this field contains a text string that is used to create the class description text in the default pop-upModifier Text - this field contains a text string that is used to create the modifier description text in the default pop-upSpecies Text - this field contains a text string that is used to create the species description text in the default pop-upCodes, names, and text fields were derived from the publication Classification of Wetlands and Deepwater Habitats of the United States.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but an imagery layer created from the same data can be used at smaller scales to produce a webmap that displays across the full scale range. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter for System Name = 'Palustrine' to create a map of palustrine wetlands only.Add labels and set their propertiesCustomize the pop-upArcGIS ProAdd this layer to a 2d or 3d mapUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

Descriptive statistics of depression, purpose in life, and loneliness.

USA Crop Frequency is a thematic imagery service which serves the USDA National Agricultural Statistics Service Crop Frequency Data Layers. The service displays how many years corn, cotton, soybeans, or wheat were grown on a pixel since 2008. First, connect to the USA Crop Frequency service, then choose the processing template for the commodity you would like to view/analyze, whether corn, soybeans, wheat, or cotton.The default view of the USA Crop Frequency service shows how many years since 2008 that a pixel grows any of these four commodity crops. (Note: If two ore more commodity crops are both grown on the same pixel during a year, this counts as only one year in which any of the commodity crops was grown.)Variable mapped: Number of years corn, cotton, soybeans, and wheat were grown from 2008 to 2018.Data Projection: AlbersMosaic Projection: AlbersExtent: Conterminous USACell Size: 30mSource Type: ThematicVisible Scale: All scalesSource: USDA NASSPublication Date: 2019This service and the data making up the service are all in Albers Projection. Albers is an equal area projection, and this allows users of this service to accurately calculate acreage without additional data preparation steps. This also means it takes a tiny bit longer to project on the fly into web mercator, if that is the destination projection of the service.Use processing templates to display frequency of corn, soybeans, wheat, or cottonCorn, soybeans, wheat, and cotton are the chief produce crops by value in the United States, excepting alfalfa and hay. To see how many years just corn, soybeans, wheat, or cotton are grown, choose the processing template that is appropriate for that commodity. Two templates exist for each commodity, one built by USDA with the default USDA color scheme, and one built by Esri.In ArcGIS Online, choose a processing template by clicking ... under crop frequency in the Table of Contents, then choose Image Display.Next, choose a renderer in the dialogue to see just corn, soybeans, wheat, or cotton in either an Esri or USDA color scheme.Value in Billions of US Dollars, 2014:Corn $52.4Soybeans $40.3Wheat $11.9Cotton $5.1Corn (Zea mays) is the most widely produced feed grain in the United States. The largest share of the corn produced in the USA (33%) is used to feed livestock, followed by 27% used to make ethanol for fuel. 11% of it is used to create food for humans, including high fructose corn syrup, sweeteners, starch, beverage alcohol, and cereals.Soybeans (Glycine max) are a widely grown crop in the United States. The beans are edible and have many uses. The beans are 38-45% protein and constitute the most important protein source for feed farm animals in the United States. They are also widely used to extract soybean oil, and in processed foods.Wheat (Triticum spp.) is a grass grown for seed and is used to make pasta (durum wheat), bread, baked goods, and other foods. For this service, "wheat" is a combination of durum, spring, and winter wheat, spelt, and triticale. These subclasses of wheat are identified by pixel in the USA Cropland thematic imagery service for years 2008-2019.Cotton (Gossypium spp.) is a flowering plant grown for its balls of soft, fluffy fibers that grow in a boll. Almost all of the boll is used as fiber in textiles, but the seeds may also be used to make oils, and the seed hulls used to feed livestock.