The Minimum Data Set (MDS) Frequency data summarizes health status indicators for active residents currently in nursing homes. The MDS is part of the Federally-mandated process for clinical assessment of all residents in Medicare and Medicaid certified nursing homes. This process provides a comprehensive assessment of each resident's functional capabilities and helps nursing home staff identify health problems. Care Area Assessments (CAAs) are part of this process, and provide the foundation upon which a resident's individual care plan is formulated. MDS assessments are completed for all residents in certified nursing homes, regardless of source of payment for the individual resident. MDS assessments are required for residents on admission to the nursing facility, periodically, and on discharge. All assessments are completed within specific guidelines and time frames. In most cases, participants in the assessment process are licensed health care professionals employed by the nursing home. MDS information is transmitted electronically by nursing homes to the national MDS database at CMS. When reviewing the MDS 3.0 Frequency files, some common software programs e.g., ‘Microsoft Excel’ might inaccurately strip leading zeros from designated code values (i.e., "01" becomes "1") or misinterpret code ranges as dates (i.e., O0600 ranges such as 02-04 are misread as 04-Feb). As each piece of software is unique, if you encounter an issue when reading the CSV file of Frequency data, please open the file in a plain text editor such as ‘Notepad’ or ‘TextPad’ to review the underlying data, before reaching out to CMS for assistance.

This data set contains magnetic field component and magnitude averages every minute, with components given in spacecraft, GSE and GSM coordinates. Standard deviations in the averages are given, as are differences between the averages and model field vectors. Geocentric (GSE and GSM) spacecraft position information is given, as is ISEE1-ISEE2 separation vector information. ISEE 1 spin vector direction and ISEE 1 velocity vector information, relative to the Earth and to ISEE 2, are given. Miscellaneous other parameters are also given. Data are accessible as plots, lists and files from CDAWeb, and as CDF files from CDAWeb's ftp area.

This data set contains magnetic field component and magnitude averages every minute, with components given in spacecraft, GSE and GSM coordinates. Standard deviations in the averages are given, as are differences between the averages and model field vectors. Geocentric (GSE and GSM) spacecraft position information is given, as is ISEE1-ISEE2 separation vector information. ISEE 1 spin vector direction and ISEE 1 velocity vector information, relative to the Earth and to ISEE 2, are given. Miscellaneous other parameters are also given. Data are accessible as plots, lists and files from CDAWeb, and as CDF files from CDAWeb's ftp area.

EOAD is a collection of videos captured by wearable cameras, mostly of sports activities. It contains both visual and audio modalities.

It was initiated by the HUJI and FPVSum egocentric activity datasets. However, the number of samples and diversity of activities for HUJI and FPVSum were insufficient. Therefore, we combined these datasets and populated them with new YouTube videos.

The selection of videos was based on the following criteria:

The videos should not include text overlays.

The videos should contain natural sound (no external music)

The actions in videos should be continuous (no cutting the scene or jumping in time)

Video samples were trimmed depending on scene changes for long videos (such as driving, scuba diving, and cycling). As a result, a video may have several clips depicting egocentric actions. Hence, video clips were extracted from carefully defined time intervals within videos. The final dataset includes video clips with a single action and natural audio information.

Statistics for EOAD:

30 activities

303 distinct videos

1392 video clips

2243 minutes labeled videos clips

The detailed statistics for the selected datasets and the crawled videos clips from YouTube are given below:

HUJI: 49 distinct videos - 148 video clips for 9 activities (driving, biking, motorcycle, walking, boxing, horse riding, running, skiing, stair climbing)

FPVSum: 39 distinct videos - 124 video segments for 8 activities (biking, horse riding, skiing, longboarding, rock climbing, scuba, skateboarding, surfing)

YouTube: 216 distinct videos - 1120 video clips for 27 activities (american football, basketball, bungee jumping, driving, go-kart, horse riding, ice hockey, jet ski, kayaking, kitesurfing, longboarding, motorcycle, paintball, paragliding, rafting, rock climbing, rowing, running, sailing, scuba diving, skateboarding, soccer, stair climbing, surfing, tennis, volleyball, walking)

The video clips used for training, validation and test sets for each activity are listed in Table 1. Multiple video clips may belong to a single video because of trimming it for some reasons (i.e., scene cut, temporary overlayed text on videos, or video parts unrelated to activities).

While splitting the dataset, the minimum number of videos for each activity was selected as 8. Additionally, the video samples were divided as 50%, 25%, and 25% for training (minimum four videos), validation (minimum two videos), and testing (minimum two videos), respectively. On the other hand, videos were split according to the raw video footage to prevent the mixing of similar video clips (having the same actors and scenes) into training, validation, and test sets. Therefore, we ensured that the video clips trimmed from the same videos were split together into training, validation, or test sets to satisfy a fair comparison.

Some activities have continuity throughout the video, such as scuba, longboarding, or riding horse, which also have an equal number of video segments with the number of videos. However, some activities, such as skating, occurred in a short time, making the number of video segments higher than the others. As a result, the number of video clips for training, validation, and test sets was highly imbalanced for the selected activities (i.e., jet ski and rafting have 4; however, soccer has 99 video clips for training).

                                  Table 1 - Dataset splitting for EOAD

Train

Validation

Test

Action Label

Clips

Total Duration

Clips

Total Duration

Clips

Total Duration

AmericanFootball

34

00:06:09

36

00:05:03

9

00:01:20

Basketball

43

01:13:22

19

00:08:13

10

00:28:46

Biking

9

01:58:01

6

00:32:22

11

00:36:16

Boxing

7

00:24:54

11

00:14:14

5

00:17:30

BungeeJumping

7

00:02:22

4

00:01:36

4

00:01:31

Driving

19

00:37:23

9

00:24:46

9

00:29:23

GoKart

5

00:40:00

3

00:11:46

3

00:19:46

Horseback

5

01:15:14

5

01:02:26

2

00:20:38

IceHockey

52

00:19:22

46

00:20:34

10

00:36:59

Jetski

4

00:23:35

5

00:18:42

6

00:02:43

Kayaking

28

00:43:11

22

00:14:23

4

00:11:05

Kitesurfing

30

00:21:51

17

00:05:38

6

00:01:32

Longboarding

5

00:15:40

4

00:18:03

4

00:09:11

Motorcycle

20

00:49:38

21

00:13:53

8

00:20:30

Paintball

7

00:33:52

4

00:12:08

4

00:08:52

Paragliding

11

00:28:42

4

00:10:16

4

00:19:50

Rafting

4

00:15:41

3

00:07:27

3

00:06:13

RockClimbing

6

00:49:38

2

00:21:59

2

00:18:50

Rowing

5

00:47:05

3

00:13:21

3

00:03:26

Running

21

01:21:56

19

00:46:29

11

00:42:59

Sailing

7

00:39:30

4

00:14:39

6

00:15:43

Scuba

5

00:35:02

3

00:23:43

2

00:18:52

Skate

91

00:15:53

30

00:07:01

10

00:02:03

Ski

14

01:48:15

17

01:01:59

7

00:39:15

Soccer

102

00:48:39

52

00:13:17

16

00:06:54

StairClimbing

6

01:05:32

6

00:17:18

5

00:20:22

Surfing

23

00:12:51

17

00:06:52

10

00:07:04

Tennis

34

00:27:04

9

00:06:03

9

00:03:14

Volleyball

87

00:19:14

35

00:07:46

7

00:18:58

Walking

49

00:43:02

36

00:38:25

10

00:10:23

Total

30

740

20:22:37

452

09:20:23

200

08:00:08

EOAD Code Repository

Scripts for downloading raw videos and trim them in to video clips are provided in this GitHub repository.

Regarding the questions, please contact mali.arabaci@gmail.com.

The Land Change Monitoring Assessment and Projection (LCMAP) raster dataset is a suite of five annual land surface change and five annual land cover (and land cover derivative) products. The LCMAP approach is the foundation for an integrated land change science framework led by the U.S. Geological Survey (USGS). The data were calculated using the Continuous Change Detection and Classification (CCDC) algorithm developed by Zhu and Woodcock (2014) and are derived from a time series of satellite imagery consisting of all available cloud- and shadow-free pixels in the USGS Landsat Analysis Ready Data (ARD) archive (Dwyer and others, 2018). The CCDC methodology supports the continuous tracking and characterization of changes in land cover, and condition enabling assessments of current, historical, and future processes of change. Landsat ARD, as the source data for LCMAP, are standardized Landsat data pre-processed to ensure the data meet a minimum set of requirements and are organized into a form that allows immediate analysis with a minimum of additional user effort. ARD data are provided as tiled, georegistered, surface reflectance products defined in a common equal area projection and tiled to a common grid. ARD observations must be transformed into time series vectors before further calculations using the CCDC methodology. The CCDC methodology, initially developed at Boston University (Zhu and Woodcock, 2014), has been adopted and modified by USGS for LCMAP. CCDC involves harmonic modeling that characterizes the seasonality, trends, and breaks from those trends based on the time series spectral reflectance data from multiple Landsat bands (i.e., green, red, near-infrared, short-wave infrared). The CCDC approach involves two major components: change detection and classification. The change detection component utilizes available high-quality surface reflectance data in a pixel-based time series to calculate a mathematical model for the spectral response of each pixel and to estimate the dates at which the spectral time series data diverge from past responses or patterns. The basis of change detection is the comparison of clear satellite observations with model predictions. 'Divergence' (referred to as a model 'break') often is identified as the result of an abrupt change (e.g. wildfire, logging, mining, and urban development) but may also result from a gradual shift (e.g., forest regrowth, insect infestation, disease) in the spectral signal over time. Breaks are detected by CCDC by applying a criterion based on the root mean square error of the harmonic modeling. Time periods for established models are referred to as 'model segments.' After a break is identified in the time series, a new model can be established following the break provided there are enough clear observations going forward in time. The classification component of CCDC involves using the coefficients of time series models as the inputs for land cover classification. The CCDC method has the capability to generate land cover for any date in the time series; the USGS has selected an annual time step for land cover classification. The suite of land cover and change products are nominally identified at a central point in the year, July 1. Classification is performed using a boosted decision tree method based on training data developed from 2001 NLCD land cover classes (Homer and others, 2007). The land cover legend for the Primary and Secondary Land Cover products is comparable to an Anderson level 1 classifcation scheme.

As a natural ecological fragile region, the vast desert steppe in the Inner Mongolia has a developed animal husbandry, and thus posed great impacts on soil quality. In order to accurately evaluate the current situation of soil quality in the desert steppe, it is therefore imperative to adopt a suitable method to effectively assess the soil quality in the region. In this study, the minimum data set (MDS) was established with the help of principal component analysis, Norm value calculation, and correlation analysis, and four indicators, including organic matter, sand grains, soil erosion degree, and pH, were established to evaluate the soil quality of the desert steppe in the Siziwang Banner, a county in the Inner Mongolia. The results from the minimum data set (MDS) method were validated based on the total data set (TDS) method, and the validation indicated that the MDS method can be representative of the soil quality of the study area. The results indicated: 1) the soil quality index (SQI) of 0–30 cm in more than 90% of the study area falls in the range of 0.4 and 0.6 (medium level), while the better level (SQI ≥0.6) only accounted less than 10% of the study area; 2) For the MDS indexes, soil organic matter content at all depths decreased in the southern mountains, central hills, and northern plateau, which is consistent with the changing trends of SQI; 3) The sand grain was the dominant particle in the study region, which was in accordance with the intense wind erosion; 4) The negative correlation was found between the soil pH value and SQI (the high value in pH corresponded to the low value in SQI), which reflected that soil pH has a more stressful effect on the local vegetation. Overall, the MDS indexes in this study can objectively and practically reflect the soil quality in the study area, which can provide a cost effective method for SQI assessment in the desert steppe, which is important for the further grassland ecological construction and grassland management to improve the soil quality in the desert steppes.

Set of data analysed for each individual include in this study. (XLSX)

Variance and weights estimated from both TDS and MDS indicators.

Mosquito surveillance data, obtained through various trapping methods, are compiled and shared in Excel (.xlsx) files. The E4Warning dataset template aims to assist field researchers in their data archiving efforts by aligning with the project's Data Management Plan. It consists of two main components: metadata and data. The metadata component includes information about the origin of the dataset, such as study details and licensing for usage. This ensures that all necessary contextual information is accessible. Our metadata component utilizes the template generated by MIReAD (Minimum Information for Reusable Analytical Data) to ensure high standards of data documentation and reusability of arthropod abundance data by establishing a set of guidelines for data reporting. By adopting the MIReAD template for our metadata, we align our data management practices with best practices for data standardization and transparency The data component lists and describes the specific data fields that should be included in data collection sheets. This is tailored to capture the essential variables typically collected by academic researchers and surveillance initiatives. The template serves as a comprehensive checklist to help prevent the omission of crucial information. The mosquito surveillance data template utilized by E4Warning partners is a designed document for recording data from mosquito trapping activities, which is subsequently used for modeling. Each field within the template is structured to ensure a comprehensive understanding of the surveillance efforts and the possible biases introduced by the trapping devices and attractants used. [1] Rund et al. 2019. MIReAD, a minimum information standard for reporting arthropod abundance data. Scientific Data. 6: 40.

Results of principal component analysis for the TDS indicators.

This dataset contains Soil Survey Geographic Database (SSURGO) data on minimum water table depths, clipped to the Mohawk River Watershed. SSURGO depicts information about the kinds and distribution of soils on the landscape. The soil map and data used in the SSURGO product were prepared by soil scientists as part of the National Cooperative Soil Survey. This data was collected by Stone Environmental, Inc. for the New York State Department of State with funds provided under Title 11 of the Environmental Protection Fund. This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties. Mohawk River Watershed Processing: The original dataset was clipped for use in the Mohawk River Watershed Management Plan. The data was re-projected from Albers to UTM 18N, NAD 83. Attributes of interest were extracted and summarized. View Dataset on the Gateway

The clinical component of SSNAP prospectively collects a minimum dataset for every stroke patient since December 2012 to measure processes of acute care, rehabilitation and care in the community. SSNAP also collects outcome measures at 6 months. The aim is to improve the quality of stroke care by auditing stroke services against evidence based standards.

The latest SSNAP periodic report presents national and named team results for the entire inpatient stroke care pathway. It is based on patients admitted or discharged from hospital in the most recent 4-monthly time period.

This report contains results for important measures of inpatient stroke care including stroke unit care, thrombolysis, therapy assessments and intensity, timings of interventions, and discharge standards. It also provides comparisons where appropriate against the previous 3 periodic reports to show changes over time.

A SSNAP scoring system has been derived to enable hospitals to compare their performance against other hospitals and benchmark against the national result. This summary of performance is based upon results for 44 key indicators which are grouped into 10 domains covering key aspects of stroke care. An overall score is calculated from domain scores adjusted for case ascertainment and audit compliance. Please see technical guidance for details about how the SSNAP scores are calculated.

Extremely high standards have been set with the aim of stimulating hospitals to identify where improvements are needed and drive change. Nowhere else in the world has set as stringent standards and the results should be read in this context.

SSNAP Summary Results: This excel file contains summary information by named team for 44 key indicators within 10 domains of care in addition to measures of overall performance based on the SSNAP scoring system.

Full Results Portfolio: This is a very detailed file containing results for every stroke measure collected by SSNAP. It is intended as a reference document for those who wish to drill-down in more detail than the summary results.

This data set represents the 2025 Walk-In Hunting Area boundaries for Wyoming. Walk-In Hunting Areas consist of private land where the owners have given permission for the public to access it for the purpose of hunting. These areas can change at any time. The areas and area rules are managed and enforced by the Wyoming Game and Fish Department.Data resides in ArcGIS Online where it is accessible to the Access Yes coordinators; they may create, delete, or update the spatial or attribute information at any time. However, at a minimum, the dataset is updated annually in June by these coordinators and then the tabular data is reviewed by the GIS workgroup. The dataset is then published to our distribution sites for standalone public access in July each year by the GIS workgroup, in addition to being uploaded into the Walk-in-Hunting application. If a change is made after this initial publication that impacts the public's proper understanding and use of the data (a change to the spatial component, a change to dates of access, etc.), the update will be re-published. This paragraph is intended to reflect a broad workflow and to negate the need for individual Process Steps detailing each and every change made every year to this dataset, which would be overwhelming given the short-term agreements on which access is based. Due to the nature of how the spatial data is created, it should be understood that there are likely to be topological inconsistencies inherent to the dataset.

Human Activity Recognition (HAR) refers to the capacity of machines to perceive human actions. This dataset contains information on 18 different activities collected from 90 participants (75 male and 15 female) using smartphone sensors (Accelerometer and Gyroscope). It has 1945 raw activity samples collected directly from the participants, and 9185 subsamples extracted from them. The activities are:

Stand➞ Standing still (1 min) Sit➞ Sitting still (1 min) Talk-sit➞ Talking with hand movements while sitting (1 min) Talk-stand➞ Talking with hand movements while standing or walking(1 min) Stand-sit➞ Repeatedly standing up and sitting down (5 times) Lay➞ Laying still (1 min) Lay-stand➞ Repeatedly standing up and laying down (5 times) Pick➞ Picking up an object from the floor (10 times) Jump➞ Jumping repeatedly (10 times) Push-up➞ Performing full push-ups (5 times) Sit-up➞ Performing sit-ups (5 times) Walk➞ Walking 20 meters (≈12 s) Walk-backward➞ Walking backward for 20 meters (≈20 s) Walk-circle➞ Walking along a circular path (≈ 20 s) Run➞ Running 20 meters (≈7 s) Stair-up➞ Ascending on a set of stairs (≈1 min) Stair-down➞ Descending from a set of stairs (≈50 s) Table-tennis➞ Playing table tennis (1 min)

Contents of the attached .zip files are: 1.Raw_time_domian_data.zip➞ Originally collected 1945 time-domain samples in separate .csv files. The arrangement of information in each .csv file is: Column 1, 5➞ exact time (elapsed since the start) when the Accelerometer & Gyro output was recorded (in ms) Col. 2, 3, 4➞ Acceleration along X,Y,Z axes (in m/s^2) Col. 6, 7, 8➞ Rate of rotation around X,Y,Z axes (in rad/s)

2.Trimmed_raw_data.zip➞ Samples of the previous file after certain parts of the signals that contained no information on the corresponding activity were trimmed.

3.Time_domain_subsamples.zip➞ 9185 subsamples extracted from the 1945 collected samples in a single .csv file. Arrangement of information: Col. 1–1500, 1501–3000, 3001–4500➞ Acc.meter X, Y, Z axes readings Col. 4501–6000, 6001–7500, 7501–9000➞ Gyro X, Y, Z axes readings Col. 9001➞ Class ID (0 to 17, in the order mentioned above) Col. 9002➞ length of the subsample (each signal begins from the starting column and runs its course, the remaining columns are padded with zeros) Col. 9003➞ serial no. of the subsample

4.Frequency_features.zip➞ The 1500-point DFT output of each signal of 9185 subsamples in a single .csv file. The arrangement of information is the same as above.

Samples were collected at 100 Hz, gravity acceleration was omitted from the Acc.meter data, and no filter was applied to remove noise.

The dataset is free to download, modify, and use. More information is provided in the data paper which is currently submitted: N. Sikder, A.-A. Nahid, KU-HAR: An open dataset for heterogeneous human activity recognition, Pattern Recognit. Lett. (submitted). A preprint will be available soon. Backup: drive(dot)google.com/drive/folders/1cS29tHwlu9MGM9OYq8ApInz0eDnuyPFs

Area proportion of SQI values at different depths (%).

This data set represents the 2025 hunting season Hunter Management Area boundaries for Wyoming. Hunter Management Areas consist of private land where the owners have given permission for a certain number of hunters to access the land for hunting a specific species. These areas can change at any time. The areas, permission process, and ranch rules are managed and enforced by the Wyoming Game and Fish Department.Data resides in ArcGIS Online where it is accessible to the Access Yes coordinators; they may create, delete, or update the spatial or attribute information at any time. However, at a minimum, the dataset is updated annually in June by these coordinators and then the tabular data is reviewed by the GIS workgroup. The dataset is then published to our distribution sites for standalone public access in July each year by the GIS workgroup, in addition to being uploaded into the Hunter Management Areas application. If a change is made after this initial publication that impacts the public's proper understanding and use of the data (a change to the spatial component, a change to dates of access, etc.), the update will be re-published. This paragraph is intended to reflect a broad workflow and to negate the need for individual Process Steps detailing each and every change made every year to this dataset, which would be overwhelming given the short-term agreements on which access is based. Due to the nature of how the spatial data is created, it should be understood that there are likely to be topological inconsistencies inherent to the dataset.

Background: Tea is an important economic crop in Yunnan, and the market price of premium teas such as Lao Banzhang is significantly higher than ordinary teas. For planting lands to promote, the tea industry to develop and minority lands’ economies to prosper, it is vital to evaluate and analyze suitable areas for premium tea cultivation. Methods: Climate, terrain, soil, and green cropping system in the premium tea planting areas were used as evaluation variables. The suitability of six machine learning models for predicting suitable areas of premium teas were evaluated. Result: FA+ResNet demonstrated the best performance with an accuracy score of 0.94 and a macro-F1 score of 0.93. The suitable areas of premium teas were mainly located in the southern catchment of LancangJiang River, south-central part of Dehong, a few areas in the mid-west of Lincang, central scattered areas of Pu’er, most of the southern western part of Xishuangbanna and the southern edge of Honghe. Annual mean temperature, annual mean precipitation, mist belt, annual mean relative humidity, soil type and elevation were the key components in evaluating the suitable areas of premium teas in Yunnan.

The Clean Water Act requires three components to water quality standards that set goals for and protect each States' waters. The three components are: (1) designated uses that set goals for each water body (e.g., recreational use), (2) criteria that set the minimum conditions to support the use (e.g., bacterial concentrations below certain concentrations) and (3) an antidegradation policy that maintains high quality waters so they are not allowed to degrade to meet only the minimum standards. The designated uses and criteria set the minimum standards for Tier I. Maryland's antidegradation policy has been promulgated in three regulations within the Code of Maryland Regulations (COMAR): COMAR 26.08.02.04 sets out the policy itself, COMAR 26.08.02.04-1, which is discussed here, provides for implementation of Tier II (high quality waters) of the antidegradation policy, and COMAR 26.08.02.04-2 that describes Tier III (Outstanding National Resource Waters or ONRW), the highest quality waters. No Tier III waters have been designated at this time. Tier II antidegradation implementation has the greatest immediate effect on local government planning functions so the Maryland Department of the Environment (MDE) has prepared this set of Tier II GIS data layers to provide technical assistance to local governments working to complete the Water Resources Element of their comprehensive plans as required by HB 1141. As part of this process, MDE has created this dataset representing the official record of all Maryland Tier II (high quality) stream segments as determined by MDE, the regulatory agency responsible for identification and listing of Maryland's Tier II waters. This dataset consists of a digital geospatial representation of all identified Tier II segments which includes those stream segments promulgated in (COMAR) 26.08.02.04-1, and those additional segments proposed during the current Triennial Review of Maryland Regulations, known as the pending list. Pending segments are Tier II segments awaiting promulgation. This is a vector line file that was developed using the 24,000:1 scale National Hydrography Dataset (NHD) coverage for Maryland, and each identified Tier II stream segment has been linked to the NHD using the unique common identifier (COMID) code. MDE uses Maryland Biological Stream Survey (MBSS) data for designating streams as Tier II. Using all MBSS stations sampled within a stream reach (defined as a section of stream from confluence to confluence), an arithmetic mean of the benthic index of biotic integrity (IBI) and the fish IBI is calculated. Only if the means of both the benthic and fish IBIs are greater than or equal to 4.00 is a stream reach designated as Tier II. As such, Tier II streams represent the best streams in Maryland in terms of water quality, water chemistry, habitat, and biotic assemblages. Tier II stream segments can range in length generally terminating at confluences, impoundment outfalls, and tidal boundaries. However, in planning activities, one should consider the entire upstream watershed to a Tier II stream as any changes to this watershed can potentially have an effect on the water quality of the Tier II stream. It is worth noting that once a stream segment is designated as Tier II, this designation lasts in perpetuity regardless of changes in water quality or local landuse. The publicly maintained list of all Tier II waters and for further information regarding Maryland's High Quality Waters, Tier II, please visit http://mde.maryland.gov/programs/Water/TMDL/Integrated303dReports/Pages/Antidegradation.aspxAcknowledgement of the Maryland Department of the Environment, Science Services Administration as a data source would be appreciated in products developed from these data, and such acknowledgement as is standard for citation and legal practices for data sources is expected by users of this data. Sharing new data layers developed directly from these data would also be appreciated by Maryland Department of the Environment Science Services Administration staff. MDE shall not be held liable for improper or incorrect use of this data. These data are not legal documents and are not to be used as such.This is a MD iMAP hosted service. Find more information on https://imap.maryland.gov.Feature Service Link: https://archive.geodata.md.gov/imap/rest/services/Hydrology/MD_ArchivedWaterQuality/FeatureServer/1

Excel Masterfile with minimal data set for (a) milk yield and milk components, (b) vitals (respiration rate, rectal temperature) and growth, (c) mammary gland H&E, and Masons trichrome, (d) mammary gland proliferation and apoptosis (Ki67 and TUNEL). (XLSX)

This data set represents monthly precipitation (in millimeters), and minimum and maximum temperature (in degrees Fahrenheit) over the period 1980-2023 compiled for two spatial components of the NHDPlus version 2 data suite (NHDPlusV2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. This dataset can be linked to the NHDPlusV2 by the unique identifier COMID. Reach catchment information characterizes data at the local scale. Reach catchments are accumulated upstream through the river network using a modified routing database (Schwarz and Wieczorek, 2018) to navigate the NHDPlusV2 reach network to aggregate (accumulate) the metrics derived from the reach catchment scale. The variables included are precipitation (gridMet_CAT_pr and gridMet_TOT_pr), minimum temperature (gridMet_CAT_tmmn and gridMet_TOT_tmmn), and maximum temperature (gridMet_CAT_tmmx and gridMet_TOT_tmmx) summarized from gridMET ( ...