The data center sector saw **** major deals worth over a billion U.S. dollars in 2023 and 2024. During that period, the biggest deal with a reported valuation was Blackstone's acquisition of Airtrunk, worth approximately **** billion U.S. dollars. Two other deals surpassed the **** billion threshold — Silverlake's minority investment in Vantage and Brookfield and Ontario Teachers' acquisition of Compass.

The Ireland Data Center Market report segments the industry into Hotspot (Dublin, Rest of Ireland), Data Center Size (Large, Massive, Medium, Mega, Small), Tier Type (Tier 1 and 2, Tier 3, Tier 4), and Absorption (Non-Utilized, Utilized). Get five years of historical data alongside five-year market forecasts.

Responding to a 2024 survey, data center owners and operators reported an average annual power usage effectiveness (PUE) ratio of 1.56 at their largest data center. PUE is calculated by dividing the total power supplied to a facility by the power used to run IT equipment within the facility. A lower figure therefore indicates greater efficiency, as a smaller share of total power is being used to run secondary functions such as cooling.

The Priority Programme for China's Agenda 21 consists of full-text program descriptions supporting China's economic and social development. The descriptions represent 69 programs covering legislation, policy, education, agriculture, environment, energy, transportation, regional development, population, health, and global change research. Each description includes project scope, background, objectives, activities, inputs, and benefits. This data set is produced in collaboration with the Administrative Center for China's Agenda 21 (ACCA21), United Nations Development Programme (UNDP), and the Columbia University Center for International Earth Science Information Network (CIESIN).

The National Aggregates of Geospatial Data Collection: Population, Landscape, And Climate Estimates, Version 3 (PLACE III) data set contains estimates of national-level aggregations in urban, rural, and total designations of territorial extent and population size by biome, climate zone, coastal proximity zone, elevation zone, and population density zone, for 232 statistical areas (countries and other UN recognized territories). This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

Objective: Assess the effectiveness of providing Logical Observation Identifiers Names and Codes (LOINC®)-to-In Vitro Diagnostic (LIVD) coding specification, required by the United States Department of Health and Human Services for SARS-CoV-2 reporting, in medical center laboratories and utilize findings to inform future United States Food and Drug Administration policy on the use of real-world evidence in regulatory decisions. Materials and Methods: We compared gaps and similarities between diagnostic test manufacturers’ recommended LOINC® codes and the LOINC® codes used in medical center laboratories for the same tests. Results: Five medical centers and three test manufacturers extracted data from laboratory information systems (LIS) for prioritized tests of interest. The data submission ranged from 74 to 532 LOINC® codes per site. Three test manufacturers submitted 15 LIVD catalogs representing 26 distinct devices, 6956 tests, and 686 LOINC® codes. We identified mismatches in how medical centers use LOINC® to encode laboratory tests compared to how test manufacturers encode the same laboratory tests. Of 331 tests available in the LIVD files, 136 (41%) were represented by a mismatched LOINC® code by the medical centers (chi-square 45.0, 4 df, P < .0001). Discussion: The five medical centers and three test manufacturers vary in how they organize, categorize, and store LIS catalog information. This variation impacts data quality and interoperability. Conclusion: The results of the study indicate that providing the LIVD mappings was not sufficient to support laboratory data interoperability. National implementation of LIVD and further efforts to promote laboratory interoperability will require a more comprehensive effort and continuing evaluation and quality control. Methods Five medical centers and three test manufacturers extracted data from laboratory information systems (LIS) for prioritized tests of interest. The data submission ranged from 74 to 532 LOINC® codes per site. Three test manufacturers submitted 15 LIVD catalogs representing 26 distinct devices, 6,956 tests, and 686 LOINC® codes. We identified mismatches in how medical centers use LOINC® to encode laboratory tests compared to how test manufacturers encode the same laboratory tests. Of 331 tests available in the LIVD files, 136 (41%) were represented by a mismatched LOINC® code by the medical centers (Chi-square 45.0,4 df,p < .0001). Data Collection from Medical Center Laboratory Pilot Sites: Each medical center was asked to extract about 100 LOINC® Codes from their LIS for prioritized tests of interest focused on high-risk conditions and SARS-CoV-2. For each selected test (e.g., SARS-CoV-2 RNA COVID-19), we collected the following data elements: test names/descriptions (e.g., SARS coronavirus 2 RNA [Presence] in Respiratory specimen by NAA with probe detection), associated instruments (e.g., IVD Vendor Model), and LOINC® codes (e.g., 94500-6). High risk conditions were defined by referencing the CDC’s published list of Underlying Medical Conditions Associated with High Risk for Severe COVID-19.[29] A data collection template spreadsheet was created and disseminated to the medical centers to help provide consistency and reporting clarity for data elements from sites. Data Collection from IVD Manufacturers: We coordinated with SHIELD stakeholders and the IICC to request manufacturer LIVD catalogs containing the LOINC® codes per IVD instrument per test from manufacturers.

Abstract: The Antarctic Site Inventory Project has collected biological data and site-descriptive information in the Antarctic Peninsula region since 1994. This research effort has provided data on those sites which are visited by tourists on shipboard expeditions in the region. The aim is to obtain data on the population status of several key species of Antarctic seabirds, which might be affected by the cumulative impact resulting from visits to the sites. This project will continue the effort by focusing on two heavily-visited Antarctic Peninsula sites: Paulet Island, in the northwestern Weddell Sea and Petermann Island, in the Lemaire Channel near Anvers Island. These sites were selected because both rank among the ten most visited sites in Antarctica each year in terms of numbers of visitors and zodiac landings; both are diverse in species composition, and both are sensitive to potential environmental disruptions from visitors. These data collected focus on two important biological parameters for penguins and blue-eyed shags: (1) breeding population size (number of occupied nests) and (2) breeding success (number of chicks per occupied nests). A long-term data program will be supported, with studies at the two sites over a five-year period. The main focus will be at Petermann Island, selected for intensive study due to its visitor status and location in the region near Palmer Station. This will allow for comparative data with the Palmer Long Term Ecological Research program. Demographic data will be collected in accordance with Standard Methods established by the Convention for the Conservation of Antarctic Marine Living Resources Ecosystem Monitoring Program and thus will be comparable with similar data sets being collected by other international Antarctic Treaty nation research programs. While separating human-induced change from change resulting from a combination of environmental factors will be difficult, this work will provide a first step to identify potential impacts. These long-term data sets will contribute to a better understanding of biological processes in the entire region and will contribute valuable information to be used by the Antarctic Treaty Parties as they address issues in environmental stewardship in Antarctica.

State and territorial executive orders, administrative orders, resolutions, proclamations, and other official publicly available government communications are collected from government websites and cataloged and coded using Microsoft Excel by one or more coders with one or more additional coders conducting quality assurance.

Data were collected to determine when individuals in states and territories were subject to executive orders, administrative orders, resolutions, proclamations, and other official publicly available government communications related to COVID-19 banning gatherings of various sizes either (1) generally, or specified that the gathering limit applied only when social distancing was not possible, or (2) even if participants practiced social distancing.

These data are derived from on the publicly available state and territorial executive orders, administrative orders, resolutions, and proclamations (“orders”) for COVID-19 that expressly ban gatherings found by the CDC, COVID-19 Community Intervention and Critical Populations Task Force, Monitoring and Evaluation Team & CDC, Center for State, Tribal, Local, and Territorial Support, Public Health Law Program from March 11, 2020 through August 15, 2021. These data will be updated as new orders are collected. Any orders not available through publicly accessible websites are not included in these data. Only official copies of the documents or, where official copies were unavailable, official press releases from government websites describing requirements were coded, as well as official government communications such as announcements that counties have progressed through new phases of reopening pursuant to an executive order, directive, or other executive branch action, and posted to government websites; news media reports on restrictions were excluded. Recommendations and guidance documents not included or adopted by reference in an order are not included in these data. These data do not include mandatory business closures, curfews, or requirements/recommendations for people to stay in their homes. Due to limitations of the National Environmental Public Health Tracking Network Data Explorer, these data do not include tribes or cities, nor was a distinction made between county orders that applied county-wide versus those that were limited to unincorporated areas of the county. Effective and expiration dates were coded using only the date provided; no distinction was made based on the specific time of the day the order became effective or expired. These data do not necessarily represent an official position of the Centers for Disease Control and Prevention.

Abstract

This survey is based on the health care centers administration records; it provides data on the main indicators of environment in the governmental and non-governmental organizations working in the health care sector, including water, management of health Care waste, and management of wastewater

Geographic coverage

Palestinian Territory

Analysis unit

Health care centers

Universe

All the Palestinian private national services establishment in the Palestinian Territory whether profit or non-profit.

Kind of data

Complete enumeration [enu]

Sampling procedure

The sample is a single-stage stratified cluster random sample.

Sampling Frame: The Sampling frame was based on the finding of the 1997 Establishment Census conducted by PCBS, which was updated by frame modification survey1999.

Sample Design: The sample of this survey is part of the sample of the Services Survey that is conducted annually. The selected establishment in the medical survey module were all medical establishment in the sample of the Services Survey and classified as private national where the national private sector or individuals owned 51% of the establishment capital or more

Sampling deviation

no applicable

Mode of data collection

Self Assessment Directed Questionnaire [SAQ]

Research instrument

The environmental questionnaire was designed in accordance with the similar country experiments and according to international standards and recommendations for the most important indicators, taking into account the special situation of Palestine.

Cleaning operations

In this stage data were entered into the computer, using ORACLE 8.0 database. The data entry program was prepared to satisfy a number of requirements such as: · Duplication of the questionnaire on the computer screen. · Logical and consistency check of data entered. · Possibility for internal editing of questions answers. · Maintaining a minimum of digital data entry and fieldwork errors. · User-Friendly handling. · Possibility of transferring data into another format to be used and analyzed using other statistical analytical systems such as SAS and SPSS.

Response rate

82.7%

Sampling error estimates

1. Statistical Errors This type of errors could be determined easily, and it is result from sampling errors, and this type of errors concern the data of private health care centers. And to reduce this errors the data mast pass tow stage:

2. Non-Statistical Errors This type of errors result from non-sampling errors, and could not be determined easily due to the diversity of sources (e.g. the interviewers, respondent, editor, data entry operator... etc).

This paper presents the results of a Secchi depth data mining study for the North Sea - Baltic Sea region. 40,829 measurements of Secchi depth were compiled from the area as a result of this study. 4.3% of the observations were found in the international data centers [ICES Oceanographic Data Center in Denmark and the World Ocean Data Center A (WDC-A) in the USA], while 95.7% of the data was provided by individuals and ocean research institutions from the surrounding North Sea and Baltic Sea countries. Inquiries made at the World Ocean Data Center B (WDC-B) in Russia suggested that there could be significant additional holdings in that archive but, unfortunately, no data could be made available. The earliest Secchi depth measurement retrieved in this study dates back to 1902 for the Baltic Sea, while the bulk of the measurements were gathered after 1970. The spatial distribution of Secchi depth measurements in the North Sea is very uneven with surprisingly large sampling gaps in the Western North Sea. Quarterly and annual Secchi depth maps with a 0.5° x 0.5° spatial resolution are provided for the transition area between the North Sea and the Baltic Sea (4°E-16°E, 53°N-60°N).

The design of this survey protocol is based on the indicator framework presented in Wall et. al (2017 https://doi.org/10.1175/WCAS-D-16-0008.1) and is intended to evaluate projects funded by Climate Adaptation Science Centers. All survey questions were optional to complete. The intended respondents are stakeholders who were engaged in the creation of scientific knowledge and tools during these projects. The questions cover three topical areas: process (engagement in the process of knowledge production), outputs/outcomes (use of information), and impacts (building of relationships and trust). Results of the survey are presented as summary tables in order to protect personal identifiable information of the respondents. Summary information is in the form of tables and word cloud graphics to communicate results of open ended questions.

Mean quality indices for the different levels of healthcare delivery, from immunization data quality audit; Kabarole District, 2015.

EyeFi Dataset

This dataset is collected as a part of the EyeFi project at Bosch Research and Technology Center, Pittsburgh, PA, USA. The dataset contains WiFi CSI values of human motion trajectories along with ground truth location information captured through a camera. This dataset is used in the following paper "EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching" that is published in the IEEE International Conference on Distributed Computing in Sensor Systems 2020 (DCOSS '20). We also published a dataset paper titled as "Dataset: Person Tracking and Identification using Cameras and Wi-Fi Channel State Information (CSI) from Smartphones" in Data: Acquisition to Analysis 2020 (DATA '20) workshop describing details of data collection. Please check it out for more information on the dataset.

Clarification/Bug report: Please note that the order of antennas and subcarriers in .h5 files is not written clearly in the README.md file. The order of antennas and subcarriers are as follows for the 90 csi_real and csi_imag values : [subcarrier1-antenna1, subcarrier1-antenna2, subcarrier1-antenna3, subcarrier2-antenna1, subcarrier2-antenna2, subcarrier2-antenna3,… subcarrier30-antenna1, subcarrier30-antenna2, subcarrier30-antenna3]. Please see the description below. The newer version of the dataset contains this information in README.md. We are sorry for the inconvenience.

Data Collection Setup

In our experiments, we used Intel 5300 WiFi Network Interface Card (NIC) installed in an Intel NUC and Linux CSI tools [1] to extract the WiFi CSI packets. The (x,y) coordinates of the subjects are collected from Bosch Flexidome IP Panoramic 7000 panoramic camera mounted on the ceiling and Angle of Arrivals (AoAs) are derived from the (x,y) coordinates. Both the WiFi card and camera are located at the same origin coordinates but at different height, the camera is location around 2.85m from the ground and WiFi antennas are around 1.12m above the ground.

The data collection environment consists of two areas, first one is a rectangular space measured 11.8m x 8.74m, and the second space is an irregularly shaped kitchen area with maximum distances of 19.74m and 14.24m between two walls. The kitchen also has numerous obstacles and different materials that pose different RF reflection characteristics including strong reflectors such as metal refrigerators and dishwashers.

To collect the WiFi data, we used a Google Pixel 2 XL smartphone as an access point and connect the Intel 5300 NIC to it for WiFi communication. The transmission rate is about 20-25 packets per second. The same WiFi card and phone are used in both lab and kitchen area.

List of Files Here is a list of files included in the dataset:

|- 1_person |- 1_person_1.h5 |- 1_person_2.h5 |- 2_people |- 2_people_1.h5 |- 2_people_2.h5 |- 2_people_3.h5 |- 3_people |- 3_people_1.h5 |- 3_people_2.h5 |- 3_people_3.h5 |- 5_people |- 5_people_1.h5 |- 5_people_2.h5 |- 5_people_3.h5 |- 5_people_4.h5 |- 10_people |- 10_people_1.h5 |- 10_people_2.h5 |- 10_people_3.h5 |- Kitchen |- 1_person |- kitchen_1_person_1.h5 |- kitchen_1_person_2.h5 |- kitchen_1_person_3.h5 |- 3_people |- kitchen_3_people_1.h5 |- training |- shuffuled_train.h5 |- shuffuled_valid.h5 |- shuffuled_test.h5 View-Dataset-Example.ipynb README.md

In this dataset, folder 1_person/ , 2_people/ , 3_people/ , 5_people/, and 10_people/ contains data collected from the lab area whereas Kitchen/ folder contains data collected from the kitchen area. To see how the each file is structured, please see below in section Access the data.

The training folder contains the training dataset we used to train the neural network discussed in our paper. They are generated by shuffling all the data from 1_person/ folder collected in the lab area (1_person_1.h5 and 1_person_2.h5).

Why multiple files in one folder?

Each folder contains multiple files. For example, 1_person folder has two files: 1_person_1.h5 and 1_person_2.h5. Files in the same folder always have the same number of human subjects present simultaneously in the scene. However, the person who is holding the phone can be different. Also, the data could be collected through different days and/or the data collection system needs to be rebooted due to stability issue. As result, we provided different files (like 1_person_1.h5, 1_person_2.h5) to distinguish different person who is holding the phone and possible system reboot that introduces different phase offsets (see below) in the system.

Special note:

For 1_person_1.h5, this file is generated by the same person who is holding the phone, and 1_person_2.h5 contains different people holding the phone but only one person is present in the area at a time. Boths files are collected in different days as well.

Access the data To access the data, hdf5 library is needed to open the dataset. There are free HDF5 viewer available on the official website: https://www.hdfgroup.org/downloads/hdfview/. We also provide an example Python code View-Dataset-Example.ipynb to demonstrate how to access the data.

Each file is structured as (except the files under "training/" folder):

|- csi_imag |- csi_real |- nPaths_1 |- offset_00 |- spotfi_aoa |- offset_11 |- spotfi_aoa |- offset_12 |- spotfi_aoa |- offset_21 |- spotfi_aoa |- offset_22 |- spotfi_aoa |- nPaths_2 |- offset_00 |- spotfi_aoa |- offset_11 |- spotfi_aoa |- offset_12 |- spotfi_aoa |- offset_21 |- spotfi_aoa |- offset_22 |- spotfi_aoa |- nPaths_3 |- offset_00 |- spotfi_aoa |- offset_11 |- spotfi_aoa |- offset_12 |- spotfi_aoa |- offset_21 |- spotfi_aoa |- offset_22 |- spotfi_aoa |- nPaths_4 |- offset_00 |- spotfi_aoa |- offset_11 |- spotfi_aoa |- offset_12 |- spotfi_aoa |- offset_21 |- spotfi_aoa |- offset_22 |- spotfi_aoa |- num_obj |- obj_0 |- cam_aoa |- coordinates |- obj_1 |- cam_aoa |- coordinates ... |- timestamp

The csi_real and csi_imag are the real and imagenary part of the CSI measurements. The order of antennas and subcarriers are as follows for the 90 csi_real and csi_imag values : [subcarrier1-antenna1, subcarrier1-antenna2, subcarrier1-antenna3, subcarrier2-antenna1, subcarrier2-antenna2, subcarrier2-antenna3,… subcarrier30-antenna1, subcarrier30-antenna2, subcarrier30-antenna3]. nPaths_x group are SpotFi [2] calculated WiFi Angle of Arrival (AoA) with x number of multiple paths specified during calculation. Under the nPath_x group are offset_xx subgroup where xx stands for the offset combination used to correct the phase offset during the SpotFi calculation. We measured the offsets as:

The measurement is based on the work [3], where the authors state there are two possible offsets between two antennas which we measured by booting the device multiple times. The combination of the offset are used for the offset_xx naming. For example, offset_12 is offset 1 between antenna 1 & 2 and offset 2 between antenna 1 & 3 are used in the SpotFi calculation.

The num_obj field is used to store the number of human subjects present in the scene. The obj_0 is always the subject who is holding the phone. In each file, there are num_obj of obj_x. For each obj_x1, we have the coordinates reported from the camera and cam_aoa, which is estimated AoA from the camera reported coordinates. The (x,y) coordinates and AoA listed here are chronologically ordered (except the files in the training folder) . It reflects the way the person carried the phone moved in the space (for obj_0) and everyone else walked (for other obj_y, where y > 0).

The timestamp is provided here for time reference for each WiFi packets.

To access the data (Python):

import h5py

data = h5py.File('3_people_3.h5','r')

csi_real = data['csi_real'][()] csi_imag = data['csi_imag'][()]

cam_aoa = data['obj_0/cam_aoa'][()] cam_loc = data['obj_0/coordinates'][()]

For file inside training/ folder:

Files inside training folder has a different data structure:

|- nPath-1 |- aoa |- csi_imag |- csi_real |- spotfi |- nPath-2 |- aoa |- csi_imag |- csi_real |- spotfi |- nPath-3 |- aoa |- csi_imag |- csi_real |- spotfi |- nPath-4 |- aoa |- csi_imag |- csi_real |- spotfi

The group nPath-x is the number of multiple path specified during the SpotFi calculation. aoa is the camera generated angle of arrival (AoA) (can be considered as ground truth), csi_image and csi_real is the imaginary and real component of the CSI value. spotfi is the SpotFi calculated AoA values. The SpotFi values are chosen based on the lowest median and mean error from across 1_person_1.h5 and 1_person_2.h5. All the rows under the same nPath-x group are aligned (i.e., first row of aoa corresponds to the first row of csi_imag, csi_real, and spotfi. There is no timestamp recorded and the sequence of the data is not chronological as they are randomly shuffled from the 1_person_1.h5 and 1_person_2.h5 files.

Citation If you use the dataset, please cite our paper:

@inproceedings{eyefi2020, title={EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching}, author={Fang, Shiwei and Islam, Tamzeed and Munir, Sirajum and Nirjon, Shahriar}, booktitle={2020 IEEE International Conference on Distributed Computing in Sensor Systems (DCOSS)},

Abstract

Environmental statistics in the health care centers is very interested and this statistics is an important instrument to make decisions, planning, and draw the outlines for environment. And relating to infrequency of data about this subject in the Palestinian Territory, the Palestinian Central Bureau of Statistics (PCBS) building up and develop a database about environmental in the health care centers.

Geographic coverage

Palestinian Territory

Analysis unit

Health care centers

Universe

1. Governmental health care centers (Ministry of Health, Health Care Military Service, Lijan Azakah).
2. Non-governmental health care centers (Health Union Committees, Union of Palestine Health Care Relief Committees, Refits Friends Benevolent Society, UNRWA, Red Crescent Society, and Red Cross) in the Palestinian Territory.
3. Private health care sector.

Kind of data

Complete enumeration [enu]

Sampling procedure

Sampling Frame: The sampling frame was based on by type of health care centers: 1. Governmental and Non-governmental Health care centers: The frame of the all centers which work in sector of health care which owned by Governmental and Nongovernmental health care centers updated annually by thru the administration records in the PCBS, and the frame which came from Establishment Center Census 2004. 2. Private Health Care Sector: The general frame for the establishments which finding of the Establishment Center Census 2004. Sample size: The sample size was 196 privet health care center in the Palestinian Territory. It was distributed according to the economic activities into 39 centers of hospital activities, 120 centers of medical and dental practice activities, 37 centers of other human health activities. Sample Design: The sample of the survey is a single-stage stratified cluster random sample.

Sampling deviation

Non

Mode of data collection

Self Assessment Directed Questionnaire [SAQ]

Research instrument

The environmental questionnaire was designed in accordance with the similar country experiments and according to international standards and recommendations for the most important indicators, taking into account the special situation of Palestinian Territory. To test the questionnaire we take the results of the last surveys that implemented by the PCBS in 2001, 2003, 2004 as a pretest; consequently some modifications were made on the questionnaire and on the instructions.

Cleaning operations

The data processing stage consisted of the following operations: Editing before data entry: All questionnaires were edited again in the office using the same instructions adopted for editing in the fields. Data entry: In this stage data were entered into the computer, using Microsoft Access. The data entry program was prepared to satisfy a number of requirements such as: • Duplication of the questionnaire on the computer screen. • Logical and consistency check of data entered. • Possibility for internal editing of questions answers. • Maintaining a minimum of digital data entry and fieldwork errors. • User-Friendly handling. • Possibility of transferring data into another format to be used and analyzed using other statistical analytical systems such as SAS and SPSS.

Response rate

75%

Sampling error estimates

1. Statistical Errors This type of errors could be determined easily, and it is result from sampling errors, and this type of errors concern the data of private health care centers. And to reduce this errors the data mast pass tow stage:
2. Non-Statistical Errors This type of errors result from non-sampling errors, and could not be determined easily due to the diversity of sources (e.g. the interviewers, respondent, editor, data entry operator... etc).

Number of clinics available in health centers by health regions for the year 2020

OWLETS2_Ship_Data_1 is the Ozone Water-Land Environmental Transition Study (OWLETS-2) data collected onboard the Smithsonian Environmental Research Center (SERC) Vessel. OWLETS was supported by the NASA Science Innovation Fund (SIF). Data includes ozone and nitrogen dioxide measurements, meteorological parameters, and ship navigational data collected via in-situ instrumentation. OWLETS and OWLETS-2 were supported by the NASA Science Innovation Fund (SIF). Data collection is complete.Coastal regions have typically posed a challenge for air quality researchers due to a lack of measurements available over water and water-land boundary transitions. Supported by NASA’s Science Innovation Fund (SIF), the Ozone Water-Land Environmental Transition Study (OWLETS) field campaign examined ozone concentrations and gradients over the Chesapeake Bay from July 5, 2017 – August 3, 2017, with twelve intensive measurement days occurring during this time period. OWLETS utilized a unique combination of instrumentation, including aircraft, TOLNet ozone lidars (NASA Goddard Space Flight Center Tropospheric Ozone Differential Absorption Lidar and NASA Langley Research Center Mobile Ozone Lidar), UAV/drones, ozonesondes, AERONET sun photometers, and mobile and ship-based measurements, to characterize the land-water differences in ozone and other pollutants. Two main research sites were established as part of the campaign: an over-land site at NASA LaRC, and an over-water site at the Chesapeake Bay Bridge Tunnel. These two research sites were established to provide synchronous vertical measurements of meteorology and pollutants over water and over land. In combination with mobile observations between the two sites, pollutant gradients were able to be observed and used to better understand the fundamental processes occurring at the land-water interface. OWLETS-2 was completed from June 6, 2018 – July 6, 2018 in the upper Chesapeake Bay region. Research sites were established at the University of Maryland, Baltimore County (UMBC), Hart Miller Island (HMI), and Howard University Beltsville (HUBV), with HMI representing the over-water location and UMBC and HUBV representing the over-land sites. Similar measurements were carried out to further characterize water-land gradients in the upper Chesapeake Bay. The measurements completed during OWLETS are of importance in enhancing air quality models, and improving future satellite retrievals, particularly, NASA’s Tropospheric Emissions: Monitoring of Pollution, which is scheduled to launch in 2022.

The Civil Rights Data Collection, 2009-10 (CRDC 2009-10), is part of the Civil Rights Data Collection (CRDC) program. CRDC 2009-10 (https://ocrdata.ed.gov/) is a cross-sectional survey that collects data on key education and civil rights issues in the nation's public schools, which include student enrollment and educational programs and services, disaggregated by race/ethnicity, sex, limited English proficiency, and disability. LEAs submit administrative records about schools in the district. LEAs and BOCES-type regional education centers functioning as LEAs were sampled. Prior to 2011-12, charter schools were primarily sampled if they were part of a LEA, not if they were a separate charter school district. For CRDC 2009-10, 100% of LEAs and 100% of schools provided data. Key statistics produced from CRDC 2009-10 can provide information about critical civil rights issues as well as contextual information on the state of civil rights in the nation, including enrollment demographics, advanced placement, discipline, and special education services.

Abstract

The study was designed to assess the effectiveness of spending in Madagascar public health sector. The research evaluated the flow of financial and material resources, medication and wages from central/district to local health facilities. The survey also looked into absenteeism among basic health centers' employees.

The research was conducted in two rounds. The first round was carried out in October-November 2006 and the second round - in April-May 2007. The study was implemented using stratified random sampling. Data from 100 health centers in six provinces was analyzed.

Public Expenditure Tracking Survey in Madagascar primary education sector was conducted at the same time with this research.

Geographic coverage

Provinces: Antananarivo, Fianarantsoa, Toamasina, Mahajanga, Toliara and Antsiranana.

Analysis unit

• Centre de Santé de Base/Basic Health Centers;
• Pharmacie à Gestion District/District Pharmacies;
• Service de Santé de District/District Health Authorities;
• Basic Health Centers' employees.

Universe

The survey covers public basic health centers (CSB), district pharmacies (PhaGDis), district health authorities (SSD) and CSB workers in all six Madagascar provinces.

Kind of data

Sample survey data [ssd]

Sampling procedure

The study was conducted using stratified random sampling.

The stratified sample was set up to be representative at the national level. Madagascar has 22 regions and 111 districts, and at least one district was visited in each region. Two districts were selected in the six largest regions. 28 districts were visited in total. The selected districts were obtained through random selection, giving greater (less) weight to districts with more (less) health centers within the district. In each district, three communes were randomly selected.

Two types of health centers - CSB (Centre de Santé de Base/Basic Health Center) Type I and CSB Type II - provide basic health care in Madagascar. In the selected communes, all public health centers of Type II were visited. If public health centers of Type I were present in the commune, one was visited based on random selection.

In the province of Antananarivo 23 public primary health centers were visited, 27 facilities were visited in Fianarantsoa, 19 - in Toamasina, 24 - in Mahajanga, 12 - in Toliara and 8 - in Antsiranana.

In total, 113 health centers were visited. Approximately one-third of the health facilities (35%) were Type I. Due to closure of some health centers during either the first or the second round (or both rounds), researchers ended up with reliable panel data on 100 health centers.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available:

• Enquête au Niveau des Centres de Sante, Visite 1er Jour;
• Enquête au Niveau des Centres de Sante, Visite 2ème Jour;
• Enquête au Niveau des Centres de Sante de Base, Enquete Personnel;
• Enquête au Niveau des Centres de Sante, Enquete Phagdis;
• Questionnaire auprès des S.S.D.

In order to accurately investigate the resource flows through the different decentralized facility levels, surveys were organized at PhaGDis (Pharmacie à Gestion District/District Pharmacy) and CSB (Centre de Santé de Base/Basic Health Center) levels. At CSB level, the director was interviewed independently from the rest of the staff. To ensure compatibility, the surveys were organized at the same time. The discrict health authorities (SSD) questionnaire was administered during the second round.

Cleaning operations

Detailed information about data editing procedures is available in "Data Cleaning Guide for PETS/QSDS Surveys" in external resources.

STATA cleaning do-files and data quality reports can also be found in external resources.

The USDA-ARS Southwest Watershed Research Center (SWRC) operates the Walnut Gulch Experimental Watershed (WGEW) in southeastern Arizona as an outdoor laboratory for studying semiarid rangeland hydrologic, ecosystem, climate, and erosion processes. Since its establishment in 1953, the SWRC in Tucson, Arizona, has collected, processed, managed, and disseminated high-resolution, spatially distributed hydrologic data in support of the center's mission. Data management at the SWRC has evolved through time in response to new computing, storage, and data access technologies. In 1996, the SWRC initiated a multiyear project to upgrade rainfall and runoff sensors and convert analog systems to digital electronic systems supported by data loggers. This conversion was coupled with radio telemetry to remotely transmit recorded data to a central computer, thus greatly reducing operational overhead by reducing labor, maintenance, and data processing time. A concurrent effort was initiated to improve access to SWRC data by creating a system based on a relational database supporting access to the data via the Internet. An SWRC team made up of scientists, IT specialists, programmers, hydrologic technicians, and instrumentation specialists was formed. This effort is termed the Southwest Watershed Research Center Data Access Project (DAP). The goal of the SWRC DAP is to efficiently disseminate data to researchers; land owners, users, and managers; and to the public. Primary access to the data is provided through a Web-based user interface. In addition, data can be accessed directly from within the SWRC network. The first priority for the DAP was to assimilate and make available rainfall and runoff data collected from two instrumented field sites, the WGEW near Tombstone, Arizona, and the Santa Rita Experimental Range (SRER) south of Tucson, Arizona. Resources in this dataset:Resource Title: Data Access Project. File Name: Web Page, url: https://www.tucson.ars.ag.gov/dap Datasets were provided by the USDA-ARS Southwest Watershed Research Center. Funding for these datasets was provided by the United States Department of Agriculture, Agricultural Research Service. Please send 1 copy of the published manuscript to: Southwest Watershed Research Center, 2000 E. Allen Rd. Tucson, AZ 857119

Preparing centers attesting notary transactions