This folder contains the data used for the following paper: "What Do Tanzanian Parents Want from Primary Schools—and What Can Be Done about It?"

his project intends to find a correlation between the songs "Moonlight Sonata" and "Because" with the use of discrete mathematics. With music theory, the field of using math to analyze music, lacking exploration, our primary goal was to draw conclusions from what was found: analyze the data collected to better understand the behind the scenes mathematics in music. This was done by translating the sheet music into numbers using modular arithmetic (Mod 12) system; then, the numbers collected were classified into pitch class sets, normal form, and prime form. We also used matrices to test for the property of invariance. After thorough examination, it was clear that the songs were very similar in nature, which fundamentally demonstrates that a mathematical interpretation can be used to prove what is heard.

Methods We performed a cross-sectional survey using a Discrete Choice Experiment (DCE) on older adults treatment decision making. A fractional-factorial design was used, selecting a subset of combinations to maximize information on main effects and key interactions (D-efficiency) for a conditional logit model in Ngene 1.3 (ChoiceMetrix). Participants were volunteers from the Netherlands, aged 50+. Statistical Analysis A conditional logit model for the main effect of the attributes was derived. All attributed were treated as categorical variables with dummy-coding. The utility of all attributes was individually tested using a Wald chi square test. Interaction testing was not included in the main model, but - based on literature and consensus of the study team - specific pre-specified interactions between attributes and personal characteristics (gender and physical pain, age and maintaining independence, educational level and societal costs, gender and independence) were tested using Wald tests. Further, data was analyzed using latent class analysis. Class memberships were analyzed to define characteristics of the class members. Datafiles: 1. Original raw data file with choice data (stata corp 2018) 2. Long data file (stata corp 2018) 3. DO file, syntax (stata corp 2018)

Harmful algal blooms (HABs) are overgrowths of algae or cyanobacteria in water and can be harmful to humans and animals directly via toxin exposure or indirectly via changes in water quality and related impacts to ecosystems services, drinking water characteristics, and recreation. While HABs occur frequently throughout the United States, the driving conditions behind them are not well understood, especially in flowing waters. In order to facilitate future model development and characterization of HABs in the Illinois River Basin, this data release publishes a synthesized and cleaned collection of HABs-related water quality and quantity data for river and stream sites in the basin. It includes nutrients, major ions, sediment, physical properties, streamflow, chlorophyll and other types of water data. This data release contains files of harmonized data from the USGS National Water Information System (NWIS), the U.S. Army Corps of Engineers (USACE), the Illinois Environmental Protection Agency (IEPA), and a USGS Open File Report (OFR) containing toxin data in Illinois (Terrio and others, 2013: https://pubs.usgs.gov/of/2013/1019/pdf/ofr2013-1019.pdf). Both discrete data and continuous sensor data for 142 parameters (44 of which returned data) between October 1, 2015 and December 31, 2022 were downloaded from NWIS programmatically. All data were harmonized into a shared format (see files named data_{parameter_group}combined.csv). The USGS NWIS data went through additional cleaning and were also grouped by generic parameters (see pcode_group_xwalk.csv to see what parameter codes are mapped to which generic parameters). Any data not from USGS NWIS were kept outside of the parameter grouping files. Additional streamflow data for select locations was retrieved from the USACE and are available in data_usace_00060_combined.csv. Additional algal toxin data provided by the IEPA and in a USGS OFR report (Terrio and others, 2013), which include some lake sites, are available in data_algaltoxins_combined.csv. We also provide collapsed datasets of daily metrics for each water quality (“generic parameter”) group of USGS NWIS data (files named daily_metrics{parameter_group}.csv). Lastly, we include a site_metadata.csv containing site identification and location information for all sites with water quality and quantity data, and mappings to the National Hydrography Dataset flowlines where available. This work was completed as part of the USGS Proxies Project, an effort supported by the Water Mission Area (WMA) Water Quality Processes program to develop estimation methods for PFAS, harmful algal blooms, and metals, at multiple spatial and temporal scales.

Now that I have your attention, please up-vote this dataset and read the following!!!

What is DROP?

With system performance on existing reading comprehension benchmarks nearing or surpassing human performance, we need a new, hard dataset that improves systems' capabilities to actually read paragraphs of text. DROP is a crowdsourced, adversarially-created, 96k-question benchmark, in which a system must resolve references in a question, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counting, or sorting). These operations require a much more comprehensive understanding of the content of paragraphs than what was necessary for prior datasets.

AllenNLP provides an easy way for you to get started with this dataset, with a dataset reader that can be used with any model you design, and a reference implementation of the NAQANet model that was introduced in the DROP paper. Find more details in the links below.

Paper, describing the dataset and our initial model for it, Numerically-Augmented QANet (NAQANet), that adds some rudimentary numerical reasoning capability on top of QANet. Data, with about 77k questions in the train set and 9.5k questions in the dev set (and a similar number in a hidden test set). The data is distributed under the CC BY-SA 4.0 license.

Code for the NAQANet model lives in AllenNLP: dataset reader, NAQANet model. Code for the other baselines in the paper may get added to AllenNLP in the future; open an issue on github if there's something in particular you'd like to see. Leaderboard with an automated docker-based evaluation on a hidden test set. NAQANet demo - see how well current NLP systems understand paragraphs! The examples in the select box at the top should give you some sense of what kinds of questions are in DROP, what the system can do well, and a bit of what it can't. Change the paragraphs, input your own, try your own complex questions, and see what you find. If you find something interesting, let us know on twitter!

Source: https://allennlp.org/drop

Citation:

@inproceedings{Dua2019DROP, author={Dheeru Dua and Yizhong Wang and Pradeep Dasigi and Gabriel Stanovsky and Sameer Singh and Matt Gardner}, title={ {DROP}: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs}, booktitle={Proc. of NAACL}, year={2019} }

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This U.S. Geological Survey (USGS) Data Release provides discrete water-quality data collected from four sites on the Kansas River and four of its tributaries during July 2012 through September 2016. The water-quality constituents included in this data release are the cyanotoxins microcystin and cylindrospermopsin, the taste-and-odor compounds geosmin and 2-methylisoborneol, major ions, alkalinity, nutrients, suspended sediment, indicator bacteria, and actinomycetes bacteria.

Lidar (Light detection and ranging) discrete-return point cloud data are available in the American Society for Photogrammetry and Remote Sensing (ASPRS) LAS format. The LAS format is a standardized binary format for storing 3-dimensional point cloud data and point attributes along with header information and variable length records specific to the data. Millions of data points are stored as a 3-dimensional data cloud as a series of x (longitude), y (latitude) and z (elevation) points. A few older projects in this collection are in ASCII format. Please refer to http://www.asprs.org/Committee-General/LASer-LAS-File-Format-Exchange-Activities.html for additional information. This data set is a LAZ (compressed LAS) format file containing lidar point cloud data. Compression to an LAZ file was done with the LAStools 'laszip' program and can be unzipped with the same free program (laszip.org). LICENSE: US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensing-map-services-and-data-national-map

The authors observed the X-ray-bright E3 galaxy NGC 1600 and nearby members of the NGC 1600 group with the Chandra X-Ray Observatory ACIS-S3 to study their X-ray properties. NGC 1600 is the brightest member of the NGC 1600 group; NGC 1601 (1.6 arcminutes away) and NGC 1603 (2.5 arcminutes away) are the two nearest galaxies, both of which are non-interacting members. The authors adopted the 2MASS Point Source Catalog position of J2000.0 RA = 04^h 31^m 39.87^s, Dec = -05^o 05' 10.5" as the location of the center of the NGC 1600 galaxy. Unresolved emission dominates the Chandra observation; however, some of the emission is resolved into 71 sources, most of which are low-mass X-ray binaries associated with NGC 1600. Twenty-one of the sources have L_X > 2 x 10³⁹ ergs/s (0.3-10.0 keV; assuming they are at the distance of NGC 1600 of 59.98 Mpc), marking them as ultraluminous X-ray point source (ULX) candidates. NGC 1600 may have the largest number of ULX candidates in an early-type galaxy to date; however, cosmic variance in the number of background active galactic nuclei cannot be ruled out. The spectrum and luminosity function (LF) of the resolved sources are more consistent with sources found in other early-type galaxies than with sources found in star-forming regions of galaxies. The source LF and the spectrum of the unresolved emission both indicate that there are a large number of unresolved point sources. The authors propose that these sources are associated with globular clusters (GCs) and that NGC 1600 has a large GC specific frequency. Observations of the GC population in NGC 1600 would be very useful for testing this prediction. NGC 1600 was observed in two intervals on 2002 September 18-19 (ObsID 4283) and 2002 September 20 (ObsID 4371) with live exposures of 26,783 and 26,752 s, respectively. The first observation showed clear evidence of a major background "flare" in the first 20% of the observation. The second observation had some small fluctuations greater than 20% from the mean rate. After these were filtered, observations 4283 and 4371 had flare-free exposure times of 21,562 and 23,616 s, respectively. This table lists all 71 discrete sources detected by wavdetect over the 0.3-6 keV energy range in the combination of the two observations. The first 3 sources (source numbers 1, 2 and 3) are clearly extended according to the authors. The authors expect 11 +/- 2 foreground/background sources to be present based on the source counts in Brandt et al. (2000, AJ, 119, 2349) and Mushotzky et al. (2000, Nature, 404, 459). The authors determined the observed X-ray hardness ratios for the sources, using the same techniques that they have used previously. They define three hardness ratios as H21 = (M-S)/(M+S), H31 = (H-S)/(H+S), and H32 = (H-M)/(H+M), where S,M, and H are the total counts in the soft (0.3-1 keV), medium (1-2 keV) and hard (2-6 keV) respectively. From their previous definitions, they have reduced the hard band from 2-10 to 2-6 keV: since the 6-10 keV range is dominated by background photons for most sources, this should increase the S/N of the hardness ratio techniques. The hardness ratios measure observed counts, which are affected by Galactic absorption and quantum efficiency (QE) degradation in the Chandra ACIS detectors. In order to compare with other galaxies, it is useful to correct the hardness ratios for these two soft X-ray absorption effects. Therefore, the authors have calculated the intrinsic hardness ratios, denoted by a superscript 0, using a correction factor in each band appropriate to the best-fit spectrum of the resolved sources, and these are what are quoted in this table. This table was created by the HEASARC in May 2018 based on CDS Catalog J/ApJ/617/262/ file table1.dat, the list of detected discrete X-ray sources in the Chandra observation of the NGC 1600 group. This is a service provided by NASA HEASARC .

This data release is part of a cooperative study to assess streamflow availability under low-flow conditions for streams on the islands of O'ahu, Moloka'i, Maui, and Hawai'i from 2018 to 2022. This data release contains 24 child items that consist of the following files: (1) a metadata xml file describing the data release files and data attributes, (2) an annotated NWIS-Mapper screen-captured image showing the seepage-run measurement sites, and (3) a comma-delimited ascii data file with the discrete discharge measurements. These discrete discharge measurements form what is commonly referred to as a “seepage run.” The intent of the seepage run is to quantify the spatial distribution of streamflow along the reach during fair-weather, low-flow conditions, generally characterized by negligible direct runoff within the reach. The measurements can be used to characterize the net seepage of water into (water gain) or out of (water loss) the stream channel between measurement sites provided that the measurements were made during stable, nonchanging flow conditions (or, in some cases, were made simultaneously during transient flow conditions) and external surface inflows (for example, a tributary) or outflows (for example, a diversion) of water to the reach are quantified and accounted for in the computation of net seepage.

Objective: To identify what women want in a delivery health facility and how they rank the attributes that influence the choice of a place of delivery.

Design: A Discrete Choice Experiment was conducted to elicit rural women’s preferences for choice of delivery health facility. Data were analyzed using both a conditional logit model to evaluate relative importance of the selected attributes. A mixed multinomial model evaluated how interactions with sociodemographic variables influence the choice of the selected attributes.

Setting: Six health facilities in a rural sub-County.

Participants: Women aged 18-49 years who had delivered within six weeks.

Primary outcome: The DCE required women to select from hypothetical health facility A or B or opt-out alternative.

Results: A total of 474 participants were sampled, 466 participants completed the survey (response rate 98%).The attribute with the strongest association with health facility preference was having a kind and support...

Lidar (Light detection and ranging) discrete-return point cloud data are available in the American Society for Photogrammetry and Remote Sensing (ASPRS) LAS format. The LAS format is a standardized binary format for storing 3-dimensional point cloud data and point attributes along with header information and variable length records specific to the data. Millions of data points are stored as a 3-dimensional data cloud as a series of x (longitude), y (latitude) and z (elevation) points. A few older projects in this collection are in ASCII format. Please refer to http://www.asprs.org/Committee-General/LASer-LAS-File-Format-Exchange-Activities.html for additional information. This data set is a LAZ (compressed LAS) format file containing lidar point cloud data. Compression to an LAZ file was done with the LAStools 'laszip' program and can be unzipped with the same free program (laszip.org). LICENSE: US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensing-map-services-and-data-national-map

Lidar (Light detection and ranging) discrete-return point cloud data are available in the American Society for Photogrammetry and Remote Sensing (ASPRS) LAS format. The LAS format is a standardized binary format for storing 3-dimensional point cloud data and point attributes along with header information and variable length records specific to the data. Millions of data points are stored as a 3-dimensional data cloud as a series of x (longitude), y (latitude) and z (elevation) points. A few older projects in this collection are in ASCII format. Please refer to http://www.asprs.org/Committee-General/LASer-LAS-File-Format-Exchange-Activities.html for additional information. This data set is a LAZ (compressed LAS) format file containing lidar point cloud data. Compression to an LAZ file was done with the LAStools 'laszip' program and can be unzipped with the same free program (laszip.org). LICENSE: US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensing-map-services-and-data-national-map

Lidar (Light detection and ranging) discrete-return point cloud data are available in the American Society for Photogrammetry and Remote Sensing (ASPRS) LAS format. The LAS format is a standardized binary format for storing 3-dimensional point cloud data and point attributes along with header information and variable length records specific to the data. Millions of data points are stored as a 3-dimensional data cloud as a series of x (longitude), y (latitude) and z (elevation) points. A few older projects in this collection are in ASCII format. Please refer to http://www.asprs.org/Committee-General/LASer-LAS-File-Format-Exchange-Activities.html for additional information. This data set is a LAZ (compressed LAS) format file containing lidar point cloud data. Compression to an LAZ file was done with the LAStools 'laszip' program and can be unzipped with the same free program (laszip.org). LICENSE: US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensing-map-services-and-data-national-map

Lidar (Light detection and ranging) discrete-return point cloud data are available in the American Society for Photogrammetry and Remote Sensing (ASPRS) LAS format. The LAS format is a standardized binary format for storing 3-dimensional point cloud data and point attributes along with header information and variable length records specific to the data. Millions of data points are stored as a 3-dimensional data cloud as a series of x (longitude), y (latitude) and z (elevation) points. A few older projects in this collection are in ASCII format. Please refer to http://www.asprs.org/Committee-General/LASer-LAS-File-Format-Exchange-Activities.html for additional information. This data set is a LAZ (compressed LAS) format file containing lidar point cloud data. Compression to an LAZ file was done with the LAStools 'laszip' program and can be unzipped with the same free program (laszip.org). LICENSE: US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensing-map-services-and-data-national-map

Lidar (Light detection and ranging) discrete-return point cloud data are available in the American Society for Photogrammetry and Remote Sensing (ASPRS) LAS format. The LAS format is a standardized binary format for storing 3-dimensional point cloud data and point attributes along with header information and variable length records specific to the data. Millions of data points are stored as a 3-dimensional data cloud as a series of x (longitude), y (latitude) and z (elevation) points. A few older projects in this collection are in ASCII format. Please refer to http://www.asprs.org/Committee-General/LASer-LAS-File-Format-Exchange-Activities.html for additional information. This data set is a LAZ (compressed LAS) format file containing lidar point cloud data. Compression to an LAZ file was done with the LAStools 'laszip' program and can be unzipped with the same free program (laszip.org). LICENSE: US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensing-map-services-and-data-national-map

Lidar (Light detection and ranging) discrete-return point cloud data are available in the American Society for Photogrammetry and Remote Sensing (ASPRS) LAS format. The LAS format is a standardized binary format for storing 3-dimensional point cloud data and point attributes along with header information and variable length records specific to the data. Millions of data points are stored as a 3-dimensional data cloud as a series of x (longitude), y (latitude) and z (elevation) points. A few older projects in this collection are in ASCII format. Please refer to http://www.asprs.org/Committee-General/LASer-LAS-File-Format-Exchange-Activities.html for additional information. This data set is a LAZ (compressed LAS) format file containing lidar point cloud data. Compression to an LAZ file was done with the LAStools 'laszip' program and can be unzipped with the same free program (laszip.org). LICENSE: US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensing-map-services-and-data-national-map

Lidar (Light detection and ranging) discrete-return point cloud data are available in the American Society for Photogrammetry and Remote Sensing (ASPRS) LAS format. The LAS format is a standardized binary format for storing 3-dimensional point cloud data and point attributes along with header information and variable length records specific to the data. Millions of data points are stored as a 3-dimensional data cloud as a series of x (longitude), y (latitude) and z (elevation) points. A few older projects in this collection are in ASCII format. Please refer to http://www.asprs.org/Committee-General/LASer-LAS-File-Format-Exchange-Activities.html for additional information. This data set is a LAZ (compressed LAS) format file containing lidar point cloud data. Compression to an LAZ file was done with the LAStools 'laszip' program and can be unzipped with the same free program (laszip.org). LICENSE: US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensing-map-services-and-data-national-map

Lidar (Light detection and ranging) discrete-return point cloud data are available in the American Society for Photogrammetry and Remote Sensing (ASPRS) LAS format. The LAS format is a standardized binary format for storing 3-dimensional point cloud data and point attributes along with header information and variable length records specific to the data. Millions of data points are stored as a 3-dimensional data cloud as a series of x (longitude), y (latitude) and z (elevation) points. A few older projects in this collection are in ASCII format. Please refer to http://www.asprs.org/Committee-General/LASer-LAS-File-Format-Exchange-Activities.html for additional information. This data set is a LAZ (compressed LAS) format file containing lidar point cloud data. Compression to an LAZ file was done with the LAStools 'laszip' program and can be unzipped with the same free program (laszip.org). LICENSE: US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensing-map-services-and-data-national-map

Lidar (Light detection and ranging) discrete-return point cloud data are available in the American Society for Photogrammetry and Remote Sensing (ASPRS) LAS format. The LAS format is a standardized binary format for storing 3-dimensional point cloud data and point attributes along with header information and variable length records specific to the data. Millions of data points are stored as a 3-dimensional data cloud as a series of x (longitude), y (latitude) and z (elevation) points. A few older projects in this collection are in ASCII format. Please refer to http://www.asprs.org/Committee-General/LASer-LAS-File-Format-Exchange-Activities.html for additional information. This data set is a LAZ (compressed LAS) format file containing lidar point cloud data. Compression to an LAZ file was done with the LAStools 'laszip' program and can be unzipped with the same free program (laszip.org). LICENSE: US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensing-map-services-and-data-national-map

Lidar (Light detection and ranging) discrete-return point cloud data are available in the American Society for Photogrammetry and Remote Sensing (ASPRS) LAS format. The LAS format is a standardized binary format for storing 3-dimensional point cloud data and point attributes along with header information and variable length records specific to the data. Millions of data points are stored as a 3-dimensional data cloud as a series of x (longitude), y (latitude) and z (elevation) points. A few older projects in this collection are in ASCII format. Please refer to http://www.asprs.org/Committee-General/LASer-LAS-File-Format-Exchange-Activities.html for additional information. This data set is a LAZ (compressed LAS) format file containing lidar point cloud data. Compression to an LAZ file was done with the LAStools 'laszip' program and can be unzipped with the same free program (laszip.org). LICENSE: US Government Public Domain https://www.usgs.gov/faqs/what-are-terms-uselicensing-map-services-and-data-national-map