This dataverse contains the data and supporting documents for the CES 2020 Team Module of UMass Amherst and UC Merced. This project was supported by the National Science Foundation, Grant Number SES-1948863.

This dataverse contains the data and supporting documents for the CCES 2018 University of Massachusetts Amherst team module. This project was supported by the National Science Foundation, Grant Number SES-1756447.

To evaluate the potential effects of climate change on wildlife habitat and ecological integrity in the northeastern United States from 2010 to 2080, a University of Massachusetts Amherst team derived a set of climate projections at a fine spatial resolution for the entire Northeast. The projections are based upon publicly available climate models. This dataset represents projections of the total average annual precipitation (mm/year) using one of two IPCC greenhouse gas concentration scenarios (RCP4.5 or RCP8.5). The dataset is intended to represent typical total annual precipitation expected during the years 2010-2080.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

The PERM Sponsorship Trends linear chart visualizes the number of PERM cases filed by students from University of Massachusetts-Amherst from 2020 to 2023, highlighting the trends in student sponsorship over the years. This chart provides insights into the long-term patterns of how students from various fields have successfully engaged with potential employers for permanent residency sponsorship.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

To evaluate the potential effects of climate change on wildlife habitat and ecological integrity in the northeastern United States from 2010 to 2080, a University of Massachusetts Amherst team derived a set of climate projections at a fine spatial resolution for the entire Northeast. The projections are based upon publicly available climate models.This dataset represents the mean of the maximum air temperature (degrees C) for June, July, and August for the year 2010 using one of two IPCC greenhouse gas concentration scenarios (RCP8.5). The dataset is intended to represent typical summer temperatures in the decade centered on 2010 rather than the actual temperatures during 2010. MAP UNITS ARE TEMP. IN DEGREES C MULTIPLIED BY 100 (which allows for more efficient data storage).

To evaluate the potential effects of climate change on wildlife habitat and ecological integrity in the northeastern United States from 2010 to 2080, a University of Massachusetts Amherst team derived a set of climate projections at a fine spatial resolution for the entire Northeast. The projections are based upon publicly available climate models.This dataset represents the mean of the maximum air temperature (degrees C) for June, July, and August for the year 2010 using one of two IPCC greenhouse gas concentration scenarios (RCP4.5). The dataset is intended to represent typical summer temperatures in the decade centered on 2010 rather than the actual temperatures during 2010. MAP UNITS ARE TEMP. IN DEGREES C MULTIPLIED BY 100 (which allows for more efficient data storage).

Here we archive the data needed to replicate the results from our article 'A Cash Lottery Increases Voter Turnout." The data was provided by the Student Affairs Technology Services at the University of Massachusetts Amherst and includes counts of those voting and not voting based on treatment assignment and selected demographics. In the spreadsheet, "Control" refers to the reminder email condition, "Reward" refers to the lottery email condition, and "No email" refers to those who were not sent an email at all.

This dataset is a component of a complete package of products from the Connect the Connecticut project. Connect the Connecticut is a collaborative effort to identify shared priorities for conserving the Connecticut River Watershed for future generations, considering the value of fish and wildlife species and the natural ecosystems they inhabit. Click here to download the full data package, including all documentation.

This dataset depicts the potential capability of the landscape throughout the CT River Watershed to provide habitat for Blackburnian Warbler (Dendroica fusca) based on environmental conditions existing in approximately 2010. Landscape capability integrates factors influencing climate suitability, habitat capability, and other biogeographic factors affecting the species’ prevalence in the area. All locations are scored on a scale from 0 to 1, with a value of 0 indicating no capacity to support the species and 1 indicating optimal conditions for the species.

This species dataset is one of a larger set of results developed by the Designing Sustainable Landscapes project led by Professor Kevin McGarigal of UMass Amherst. The species datasets developed under the project include the following:

1. Landscape capability datasets for a set of species intended to represent a broader set of wildlife species, and associated ecosystems, that collectively encompass a majority of the terrestrial, wetland, and coastal ecosystems of the Northeast. For each species, the datasets include projections of future landscape capability, taking into account several scenarios of possible future development, climate, and forest change, for the years 2030 and 2080.

2. Datasets for each species that compare 2010 results to future scenarios for 2030 and 2080. These include areas where the species could most likely be expected to persist, areas where it might be able colonize with future climate change, and areas where the species might experience a loss of suitable habitat.

More information and detailed documentation for the Designing Sustainable Landscapes project, which includes many additional datasets besides the species datasets, is available at: http://www.umass.edu/landeco/research/dsl/dsl.html.

The 2010 Northeast Landscape Capability Dataset for this species represents the integration of three models:

1. A habitat capability model developed using a spatially explicit, GIS-based wildlife habitat modeling framework called “HABIT@” developed by the Landscape Ecology Lab of the University of Massachusetts Amherst.

2. A climate niche model based on an analysis of the climate conditions (circa 2010) that are most suitable for the species in eastern North America.

3. A prevalence model intended to capture biogeographic factors influencing the distribution of a species that are not reflected in the habitat capability or climate niche models.

Both the climate niche and prevalence models are based on field surveys for the species (e.g., the Breeding Bird Survey). The habitat capability models developed using HABIT@ reflect the quantity, quality, and accessibility (collectively referred to as “capability”) of habitat across the landscape for each year assessed. The habitat models are based on ecological settings grids (spatial datasets) developed for the Northeast, such as cover type (largely derived from the Northeast Terrestrial Habitat Map prepared by The Nature Conservancy and Northeastern states), roads and development, and forest biomass (for forest species). The models are spatially-explicit: the value at each cell (location) depends not only on the resources available at that cell, but on resources available in the neighborhood, on the configuration of those resources, and nearby impediments to movement. However, HABIT@ does not model population dynamics or population viability.

Detailed documentation on the development of all of the species datasets, including this Northeast Landscape Capability Dataset, are available at: http://www.umass.edu/landeco/research/dsl/documents/dsl_documents.html. The documentation includes a list of all the species for which models have been or are being developed and discusses limitations and constraints for using the datasets.

The global remote digital marketing training market is estimated to be valued at USD XXX million in 2025 and is projected to grow at a CAGR of XX% during the forecast period of 2025-2033. The growth of the market is attributed to the increasing adoption of digital marketing by businesses of all sizes, the growing demand for skilled digital marketing professionals, and the increasing popularity of remote learning. Key drivers of the market include the rising demand for skilled digital marketing professionals, the increasing adoption of digital marketing by businesses of all sizes, and the growing popularity of remote learning. The market is also expected to benefit from the increasing availability of online courses and the growing number of providers offering remote digital marketing training. Key players in the market include Shelton Associates, General Assembly, BrainStation, Digital Marketing Institute, Nuclio Digital School, NYIM Training, Google Digital Garage, Coursera, Remote Skills Academy, UMass Amherst, NEXT Academy, University of the Arts London, Columbia Business School Executive Education, Ascento Learning & Development, BSI Training Academy, Informa Connect, Future Connect, and many others.

This dataset depicts the ecological integrity of locations (represented by 30 m grid cells) throughout the northeastern United States based on environmental conditions existing in approximately 2010 for aquatic systems.
The values for this dataset were extracted from the Index of Ecological Integrity, Region-wide, Version 3.2 for all aquatic systems. Updated 09/2017. The metadata for the original dataset is as follows:

This dataset was last updated 02/2017. This version includes a new tidal restrictions metric that assesses the effect of undersized culverts and bridges on tidal regime. The previous version (3.1) was updated on 05/2016 by incorporating a revised version of the land cover classification, DSLland Version 3.1, developed by UMass, which included the addition of The Nature Conservancy's Northeast lakes and ponds classification.

Ecological integrity is defined as the ability of an area (e.g., local site or landscape) to sustain important ecological functions over the long term. In particular, the functions include the long-term ability to support biodiversity and the ecosystem processes necessary to sustain biodiversity. The Index of Ecological Integrity (IEI) is expressed on a relative scale (0 to 1) for ecosystems mapped on a modified version of the Northeast Terrestrial Habitat Map developed by the Nature Conservancy and the northeastern states. Ecosystems are the finest scale level of the ecological classification hierarchy. Classes include "Northeastern Interior Pine Barrens" and "Acidic Cliff and Talus". This version of ecological integrity includes two categories of landscape metrics: • Intactness – the freedom from human impairment (anthropogenic stressors), measured as a combination of a number of stressor metrics. • Resiliency – the capacity to recover from disturbance and stress, measured as a combination of the connectedness and similarity to neighboring natural areas.

This ecological integrity dataset is one of a larger set of results developed by the Designing Sustainable Landscapes project led by Professor Kevin McGarigal of UMass Amherst. Projected future ecological integrity for 2030 and 2080 are also being developed based on models of development (urban growth), climate change, and forest change. More information and detailed documentation for the Designing Sustainable Landscapes project, which includes many additional datasets, is available at: http://www.umass.edu/landeco/research/dsl/dsl.html.

More details about the calculation of the Index of Ecological Integrity are as follows. The basic building blocks of the index are a series of Ecological Settings, each of which is a spatial dataset encompassing the Northeastern U.S. The ecological settings represent a broad but carefully selected suite of biophysical variables representing the natural and anthropogenic environment at each location for each time step used in the Designing Sustainable Landscapes project. Each ecological setting is available as a separate spatial dataset. One of the key components is the DSLland dataset, which is a modified version of the Northeast Terrestrial Wildlife Habitat Map developed by The Nature Conservancy and the northeastern states. Other settings include variables such as temperature, soil depth, above-ground live biomass, extent of development, and traffic rate. A series of metrics, such as the intensity of urban development and the degree to which ecosystems are connected, are calculated from these ecological settings.The metrics are integrated in weighted linear combinations to calculate IEI based on the opinions of expert teams as to the importance of each metric in determining the ecological integrity of the different ecosystem types. In the final IEI, results are re-scaled by ecosystem type to make comparisons more meaningful. For example, marshes are ranked relative to other marshes rather than in comparison to forests or other ecosystem types. Hence, IEI represents a cell’s percentile within its group, e.g., a cell of Laurentian-Acadian freshwater marsh with an IEI of 80 is in the top 20% of Laurentian-Acadian freshwater marshes.The specific metrics for IEI, each of which is available as a separate dataset, are the following:
Intactness Metrics: 1) Habitat loss – the intensity of habitat loss due to development in the neighborhood of each cell 2) Watershed habitat loss (aquatic metric) – the intensity of habitat loss due to development upstream of the cell 3) Road traffic – the intensity of traffic in the neighborhood of the cell 4) Mowing and plowing – the intensity of agriculture in the vicinity of the cell, reflecting mortality to organisms from mowing and plowing 5) Edge effects – the effects of human-induced edges on ecosystems 6) Watershed road salt (aquatic metric) – the density of upstream roads, a surrogate for road salt application rates 7) Watershed road sediment (aquatic metric) – the density of upstream roads, a surrogate for road sediment production rates 8) Nutrient enrichment (aquatic metric) – the intensity of residential and agricultural land uses upstream of each cell a surrogate for fertilizer application rates 9) Watershed imperviousness (aquatic metric) – the intensity of impervious surface (such as roads and buildings) upstream of the cell 10) Dams (aquatic metric) – the number and proximity of dams upstream of the cell 11) Biotic alterations – the intensity of development in the neighborhood of the cell, calculated separately as a surrogate for four effects: a) edge predators (such as raccoons and skunks), b) domestic predators (such as cats), c) invasive earthworms, and d) invasive plants.
Resiliency Metrics: 1) Connectedness – the degree to which development and ecologically dissimilar sites interfere with connections between the cell and ecologically similar neighbors 2) Aquatic connectedness – the degree to which connections along streams and rivers are diminished by barriers such as dams and culverts 3) Similarity – the similarity (lack of contrast) between the environment of a cell and its surroundings (with higher similarity implying greater resilience)

This study was a follow-up of Trachtenberg's Student Internship Program, 1975 (Log# 00073) data. The internship program, funded by the Carnegie Corporation, was designed to encourage and facilitate the entry of women into predominantly male professions. The follow-up was primarily concerned with examining the balance of the professional and personal lives of women who had applied for the internships in college. The original 1975 participants were 250 women attending Boston College, Brandeis University, Hampshire College, the Massachusetts Institute of Technology, the University of Massachusetts at Amherst and the University of Massachusetts at Boston. Of 160 participants for whom addresses were available, 104 responded (a response rate of 65%). Four were dropped from analysis because they were older than the other participants. Most participants had graduated from college around 1975 and were around 30 years of age when the follow-up was conducted in 1984. Follow-up participants completed a self-administered questionnaire. The questionnaire was adapted from Tangri's Longitudinal Study of Career Development in College-Educated Women, 1967-1981 (Log# 00009) Womens Life Paths Questionnaire. Open- and closed-ended questions were used to collect data about the participants' activities and experiences since college. Items covered topics such as education, employment, balancing work and family, and outcomes of the internship program. The Murray Research Archive holds 99 of the original record paper questionnaires. Follow-up of this sample is possible.

The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. The archive was created as an ftp archive in 1987 by David Aha and fellow graduate students at UC Irvine. Since that time, it has been widely used by students, educators, and researchers all over the world as a primary source of machine learning data sets. As an indication of the impact of the archive, it has been cited over 1000 times, making it one of the top 100 most cited "papers" in all of computer science. The current version of the web site was designed in 2007 by Arthur Asuncion and David Newman, and this project is in collaboration with Rexa.info at the University of Massachusetts Amherst. Funding support from the National Science Foundation is gratefully acknowledged. Many people deserve thanks for making the repository a success. Foremost among them are the d

The UMass Amherst Donahue Institute has compiled the 2020 Census PL-94 dataset into informative data viewer dashboards, graphs, and tables. The data are presented at the Town level (Census block group and block data are not provided). The data presented include:Population & Population ChangeRace & Ethnicity Housing UnitsThe Donohue Institute has also ranked each town within MA for each of the above parameters.