We compiled macroinvertebrate assemblage data collected from 1995 to 2014 from the St. Louis River Area of Concern (AOC) of western Lake Superior. Our objective was to define depth-adjusted cutoff values for benthos condition classes (poor, fair, reference) to provide tool useful for assessing progress toward achieving removal targets for the degraded benthos beneficial use impairment in the AOC. The relationship between depth and benthos metrics was wedge-shaped. We therefore used quantile regression to model the limiting effect of depth on selected benthos metrics, including taxa richness, percent non-oligochaete individuals, combined percent Ephemeroptera, Trichoptera, and Odonata individuals, and density of ephemerid mayfly nymphs (Hexagenia). We created a scaled trimetric index from the first three metrics. Metric values at or above the 90th percentile quantile regression model prediction were defined as reference condition for that depth. We set the cutoff between poor and fair condition as the 50th percentile model prediction. We examined sampler type, exposure, geographic zone of the AOC, and substrate type for confounding effects. Based on these analyses we combined data across sampler type and exposure classes and created separate models for each geographic zone. We used the resulting condition class cutoff values to assess the relative benthic condition for three habitat restoration project areas. The depth-limited pattern of ephemerid abundance we observed in the St. Louis River AOC also occurred elsewhere in the Great Lakes. We provide tabulated model predictions for application of our depth-adjusted condition class cutoff values to new sample data. This dataset is associated with the following publication: Angradi, T., W. Bartsch, A. Trebitz, V. Brady, and J. Launspach. A depth-adjusted ambient distribution approach for setting numeric removal targets for a Great Lakes Area of Concern beneficial use impairment: Degraded benthos. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 43(1): 108-120, (2017).

Output from programming code written to summarize 2018 monarch butterfly abundance from monitoring data acquired using a modified Pollard walk at custom 2017 GRTS draw sites within select monitoring areas (see SOP 2 in ServCat reference 103367 for methods) of FWS Legacy Regions 2 and 3. Areas monitored included Balcones Canyonlands (TX), Hagerman (TX), Washita (OK), Neal Smith (IA) NWRs and several locations near the town of Lamoni, Iowa and northern Missouri. Input data file is named 'FWS_2018_MM_SOP2_for_SAS.csv' and is stored in ServCat reference 136485. See SM 5 (ServCat reference 103388) for dictionary of data fields in the input data file.

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Output from programming code written to summarize immature monarch butterfly, milkweed and nectar plant abundance from monitoring data acquired using a grid of 1 square-meter quadrats at custom 2017 GRTS draw sites within select monitoring areas (see SOP 3 in ServCat reference 103368 for methods) of FWS Legacy Regions 2 and 3. Areas monitored included Balcones Canyonlands (TX), Hagerman (TX), Washita (OK), Neal Smith (IA) NWRs and several locations near the town of Lamoni, Iowa and northern Missouri. Input data file is named 'FWS_2018_MonMonSOP3DS1_forSAS.csv' and is stored in ServCat reference 137698. See SM 5 (ServCat reference 103388) for dictionary of data fields in the input data file.

This data publication contains the data and SAS code corresponding to the examples provided in the publication "A tutorial on the piecewise regression approach applied to bedload transport data" by Sandra Ryan and Laurie Porth in 2007 (see cross-reference section). The data include rates of bedload transport and discharge recorded from 1985-1993 and 1997 at Little Granite Creek near Jackson, Wyoming as well as the bedload transport and discharge recorded during snowmelt runoff in 1998 and 1999 at Hayden Creek near Salida, Colorado. The SAS code demonstrates how to apply a piecewise linear regression model to these data, as well as bootstrapping techniques to obtain confidence limits for piecewise linear regression parameter estimates.These data were collected to measure rates of bedload transport in coarse grained channels.Original metadata date was 05/31/2007. Metadata modified on 03/19/2013 to adjust citation to include the addition of a DOI (digital object identifier) and other minor edits. Minor metadata updates on 12/20/2016.

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Average (standard deviation) RMSD value, SI score, SAS score, and match with reference alignments for the Sokol and Skolnick data sets for similar and dissimilar protein pairs.

This dataset reference a Cahors FASTEO 50 kW charging station with two T2S 22 kW bases and a Cahors FASTEO 100 kW charging station with two T2S 22 kW bases This dataset meets the specifications of the diagram “Electric vehicle charging infrastructure” available on the website schema.data.gouv.fr

Integrated Postsecondary Education Data System (IPEDS) Complete Data Files from 1980 to 2023. Includes data file, STATA data file, SPSS program, SAS program, STATA program, and dictionary. All years compressed into one .zip file due to storage limitations.Updated on 2/14/2025 to add Microsoft Access Database files.From IPEDS Complete Data File Help Page (https://nces.ed.gov/Ipeds/help/complete-data-files):Choose the file to download by reading the description in the available titles. Then, click on the link in that row corresponding to the column header of the type of file/information desired to download.To download and view the survey files in basic CSV format use the main download link in the Data File column.For files compatible with the Stata statistical software package, use the alternate download link in the Stata Data File column.To download files with the SPSS, SAS, or STATA (.do) file extension for use with statistical software packages, use the download link in the Programs column.To download the data Dictionary for the selected file, click on the corresponding link in the far right column of the screen. The data dictionary serves as a reference for using and interpreting the data within a particular survey file. This includes the names, definitions, and formatting conventions for each table, field, and data element within the file, important business rules, and information on any relationships to other IPEDS data.For statistical read programs to work properly, both the data file and the corresponding read program file must be downloaded to the same subdirectory on the computer’s hard drive. Download the data file first; then click on the corresponding link in the Programs column to download the desired read program file to the same subdirectory.When viewing downloaded survey files, categorical variables are identified using codes instead of labels. Labels for these variables are available in both the data read program files and data dictionary for each file; however, for files that automatically incorporate this information you will need to select the Custom Data Files option.

Datasets produced by SAS as outcomes of the simulation study. Each zip-file contains the outcomes for a scenario. The "Pathist" dataset is the dataset containing the "Reference Disease Progression" of the superpopulation in the simulation study (N=50,000).

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This directory contains analytic code used to build cohorts, dependent variables, and covariates, and run all statistical analyses for the study, "Changes in care associated with integrating Medicare and Medicaid for dual eligible individuals: Examination of a Fully Integrated Special Needs Plan."The code files enclosed in this directory are:SAS_Cohorts_Outcomes 23-9-30.sas. This SAS code file builds study cohorts, dependent variables, and covariates. This code produced a person-by-month level database of outcomes and covariates for individuals in the integration and comparison cohorts.STATA_Models_23-6-5_weight_jama.do. This Stata program reads in the person-by-month level database (output from SAS) and conducts all statistical analyses used to produce the main and supplementary analyses reported in the manuscript.We have provided this code and documentation to disclose our study methods. Our Data Use Agreements prohibit publishing of row-level data for this study. Therefore, researchers would need to obtain Data Use Agreements with data providers to implement these analyses. We also note that some measures reference macros with proprietary code (e.g., Medispan® files) which require a separate user license to run. Interested readers should contact the study PI, Eric T. Roberts (eric.roberts@pennmedicine.upenn.edu) for further information.

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🪔 South Asian Sounds - KIIT 🎧

A curated dataset of 9,450 labeled audio segments capturing the rich and diverse soundscapes of South Asia. This dataset is designed for audio classification tasks and includes sounds ranging from traditional music and religious rituals to environmental noises and animal calls.

📁 Dataset Details

• Total Audio Files: 9,450
• Format: WAV
• Segment Duration: 4 seconds each
• Number of Classes: 21
• File Naming Convention: Class-ID_Class-Name_Segment-Number.wav
• Organization: Randomly distributed across 10 folders
• Metadata File: Included (metadata.csv)

🏷️ Classes and Labels

📄 Metadata Columns

• slice_file_name: Name of the audio segment
• slicing_start_time: Start time of the segment
• slicing_end_time: End time of the segment
• ClassID: Numeric class label (0 to 20)
• Class_name: Descriptive class name
• folder: Folder containing the segment

🌐 Official Website

🔗 sas-kiit.netlify.app

📚 Citation

Paper Link : https://ieeexplore.ieee.org/document/10829485 If you use this dataset, please cite: @inproceedings{chatterjee2024south, title={South Asian Sounds: Audio Classification}, author={Chatterjee, Rajdeep and Bishwas, Pappu and Chakrabarty, Sudip and Bandyopadhyay, Tathagata}, booktitle={2024 4th International Conference on Computer, Communication, Control & Information Technology (C3IT)}, pages={1--6}, year={2024}, organization={IEEE} }

👥 Contributors

• Sudip Chakrabarty
• Pappu Bishwas
• Rajdeep Chatterjee
• Tathagata Bandyopadhyay

📌 License & Usage

This dataset is intended for research and academic use only.
Please provide proper citation when using it in your work.

Output from programming code written to summarize data describing 2017 MCSP Trial monitoring sites acquired using a SOP 1 (see ServCat reference 103364) of FWS Legacy Regions 2 and 3. 2017 monitoring sites were selected using a custom GRTS draw conducted by USGS, within monitoring areas associated with select NWRS stations. Areas monitored included Balcones Canyonlands (TX), Hagerman (TX), Washita (OK), Neal Smith (IA), Necedah (WI) NWRs and several locations near the town of Lamoni, Iowa and private lands in northern Missouri.

The 3.5 GHz citizens broadband radio service (CBRS) band in the U.S. is a key portion of mid-band spectrum shared between commercial operators and existing federal and non-federal incumbents. To protect the federal incumbents from harmful interference, a spectrum access system (SAS) is required to use a common, standardized algorithm, called the move list algorithm, to suspend transmissions of some CBRS devices (CBSDs) on channels in which the incumbent becomes active. However, the current reference move list implementation used for SAS testing is non-deterministic in that it uses a Monte Carlo estimate of the 95th percentile of the aggregate interference from CBSDs to the incumbent. This leads to uncertainty in move list results and in the aggregate interference check of the test. We propose to use upper and lower bounds on the aggregate interference distribution to compute deterministic move lists. These include the reference move list used by the testing system and an operational move list used by the SAS itself. We evaluate the performance of the proposed deterministic move lists using reference implementations of the standards and simulated CBSD deployments in the vicinity of federal incumbent dynamic protection areas (DPAs). The data include numerical results of the proposed deterministic move lists for a single protection point Pensacola DPA and forty offshore DPAs along the U.S. coasts. The data is associated with the article, "Deterministic Move Lists for Federal Incumbent Protection in the CBRS Band," T. T. Nguyen and M. R. Souryal, in IEEE Transactions on Cognitive Communications and Networking, Vol. 7, No. 3, September 2021.

X-rays are a powerful probe of activity in the early stages of star formation. They allow us to identify young stars even after they have lost the IR signatures of circumstellar disks and provide constraints on their distance. Here, the authors report on XMM-Newton observations that detected 121 young stellar objects (YSOs) in two fields between the filamentary dark cloud complex Lynds 1641S and the star Kappa Ori. These observations extend the Survey of Orion A with XMM and Spitzer (SOXS). The YSOs are contained in a ring of gas and dust apparent at millimeter wavelengths, and in far-IR and near-IR surveys. The X-ray luminosity function of the YSOs detected in the two fields indicates a distance of 250-280 pc, much closer than the Orion A cloud and similar to the distance estimates for Kappa Ori. The authors propose that the ring is a 5-8 pc diameter shell that has been swept up by Kappa Ori. This ring contains several groups of stars detected by Spitzer and WISE including one surrounding the Herbig Ae/Be star V1818 Ori. In this interpretation, the Kappa Ori ring is one of several shells swept up by massive stars within the Orion Eridanus Superbubble and is unrelated to the southern portion of Orion A/L 1641S. The XMM-Newton observations consist of two fields, north (Field N = KN) and south (Field S = KS), and were obtained in 2015 March 10 and 15 using EPIC as the primary instrument. Table 1 in the reference paper shows the details of the exposures, each one with a duration of about 50 ks and taken with the Medium filter. The authors used SAS version 14.0 to reduce the observation data files (ODFs) and to obtain calibrated lists of events for the MOS and pn instruments. They filtered the events in the 0.3-0.8 keV energy band and used only events with FLAG = 0 and PATTERN < 12 as prescribed by the SAS manual. With SAS, the authors obtained exposure maps in the 0.3-8.0 keV band and performed source detection with a code based on wavelet convolution that operated simultaneously on MOS and pn data. They used a threshold of significance of 4.5 sigma of the local background to discriminate real sources from spurious background fluctuations. However, they added few sources to the final list with significance S in 4.0 < S < 4.5 for the cases of positional match with objects in SIMBAD or PPMX catalogs. The final list was also checked for spurious sources that could appear at the border of the CCDs. In sum, the authors detected 238 X-ray sources with significance > 4 sigma of the local background; 104 sources are in KN and 134 in KS. The authors cross-correlated the positions of the X-ray sources with the coordinates of the IR catalog of Megeath et al. (2012, AJ, 144, 192). This IR catalog is the result of a survey of Orion with Spitzer that produced a classification of protostars and stars with disks. Of the 238 X-ray sources, 191 are identified within 8 arcseconds of one of 206 IR objects, 99 sources in KS, 92 sources in KN. Some X-ray sources were multiple matches within 8 arcsec of IR objects. For these cases, the authors assigned the most likely counterparts based on IR photometry and visual inspection of X-rays and IR images. However, nine X-ray sources were left associated with two or three IR objects. Among the IR matches, the authors found 15 stars with disks in KN and 35 in KS with X-ray detection. One protostar in KN and three in KS were detected in X-rays. The authors used X-ray detection of sources without IR excess as criteria to identify disk-less stars (hereafter Class III stars). They classified as Class III stars those IR objects with X-ray detections, with [4.5um]-[8.0um] colors < 0.3 mag and brighter than [4.5um] magnitude < 14. At the distance of the ONC (400 pc), the [4.5um] magnitude ~ 14 threshold at an age of 4-5 Myrs roughly identifies M3-M4 spectral types and masses around 0.3 solar masses. With this selection scheme, the authors identified 48 objects in KN and 19 in KS as Class III candidates. This table was created by the HEASARC in August 2016 based on the electronic version of Table 2 from the reference paper which was obtained from the CDS (their catalog J/ApJ/820/L28 file table2.dat). This is a service provided by NASA HEASARC .

In this paper, we investigate the use of Bayesian networks to construct large-scale diagnostic systems. In particular, we consider the development of large-scale Bayesian networks by composition. This compositional approach reflects how (often redundant) subsystems are architected to form systems such as electrical power systems. We develop high-level specifications, Bayesian networks, clique trees, and arithmetic circuits representing 24 different electrical power systems. The largest among these 24 Bayesian networks contains over 1,000 random variables. Another BN represents the real-world electrical power system ADAPT, which is representative of electrical power systems deployed in aerospace vehicles. In addition to demonstrating the scalability of the compositional approach, we briefly report on experimental results from the diagnostic competition DXC, where the ProADAPT team, using techniques discussed here, obtained the highest scores in both Tier 1 (among 9 international competitors) and Tier 2 (among 6 international competitors) of the industrial track. While we consider diagnosis of power systems specically, we believe this work is relevant to other system health management problems, in particular in dependable systems such as aircraft and spacecraft. Reference: O. J. Mengshoel, S. Poll, and T. Kurtoglu. "Developing Large-Scale Bayesian Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft." Proc. of the IJCAI-09 Workshop on Self-* and Autonomous Systems (SAS): Reasoning and Integration Challenges, 2009 BibTex Reference: @inproceedings{mengshoel09developing, title = {Developing Large-Scale {Bayesian} Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft}, author = {Mengshoel, O. J. and Poll, S. and Kurtoglu, T.}, booktitle = {Proc. of the IJCAI-09 Workshop on Self-$\star$ and Autonomous Systems (SAS): Reasoning and Integration Challenges}, year={2009} }