HACK CPU Info V1.1
Mel Drury
The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.
How to use Project Open Data Metadata Schema guidelines to document and list agency datasets and application programming interfaces (APIs) for hosting at agency.gov/data and currently in use at data.gov
This section contains guidance to support the use of the Project Open Data metadata to list agency datasets and application programming interfaces (APIs) as hosted at agency.gov/data. Additional technical information about the schema can be found on the Metadata Resources page.
Metadata is structured information that describes, explains, locates, or otherwise makes it easier to retrieve, use, or manage an information resource (NISO 2004, ISBN: 1-880124-62-9). The challenge is to define and name standard metadata fields so that a data consumer has sufficient information to process and understand the described data. The more information that can be conveyed in a standardized regular format, the more valuable data becomes. Metadata can range from basic to advanced, from allowing one to discover the mere fact that a certain data asset exists and is about a general subject all the way to providing detailed information documenting the structure, processing history, quality, relationships, and other properties of a dataset. Making metadata machine readable greatly increases its utility, but requires more detailed standardization, defining not only field names, but also how information is encoded in the metadata fields.
Establishing a common vocabulary is the key to communication. The metadata schema specified in this memorandum is based on DCAT, a hierarchical vocabulary specific to datasets. This specification defines three types of metadata elements: Required, Required-if (conditionally required), and Expanded fields. These elements were selected to represent information that is most often looked for on the web. To assist users of other metadata standards, field mappings to equivalent elements in other standards are provided.
A Web API (Application Programming Interface) allows computer programs to dynamically query a dataset using the World Wide Web. For example, a dataset of farmers markets may be made available for download as a single file (e.g., a CSV), or may be made available to developers through a Web API, such that a computer program could use a ZIP Code to retrieve a list of farmers markets in the ZIP Code area.
The Implementation Guidance available as a part of Project Open Data describes Agency requirements for the development of metadata as per the Open Data Policy. A quick primer on the file format involved:
JSON is a lightweight data-exchange format that is very easy to read, parse and generate. Based on a subset of the JavaScript programming language, JSON is a text format that is optimized for data interchange. JSON is built on two structures: (1) a collection of name/value pairs and (2) an ordered list of values.
The Project Open Data schema is case sensitive. The schema uses a camel case convention where the first letter of some words within a field are capitalized (usually all words but the first one). While it may seem subtle which characters are uppercase and lowercase, it is necessary to follow the exact same casing as defined in the schema documented here. For example:
These fields describe the entire Public Data Listing catalog file. Publishers can also use the describedBy field to reference the default JSON Schema file used to define the schema ( -open-data.cio.gov/v1.1/schema/catalog.json) or they may refer to their own JSON Schema file if they have extended the schema with additional schema definitions. Similarly, @context can be used to reference the default JSON-LD Context used to define the schema ( -open-data.cio.gov/v1.1/schema/catalog.jsonld) or publishers can refer to their own if they have extended the schema with additional linked data vocabularies. See the Catalog section under Further Metadata Field Guidance for more details.
See the Further Metadata Field Guidance section to learn more about the use of each element, including the range of valid entries where appropriate. Consult the field mappings to find the equivalent v1.0, DCAT, Schema.org, and CKAN fields.
We added the accessLevel field to help easily sort datasets into our three existing categories: public, restricted public, and non-public. This field means an agency can run a basic filter against its enterprise data catalog to generate a public-facing list of datasets that are, or could one day be, made publicly available (or, in the case of restricted data, available under certain conditions). This field also makes it easy for anyone to generate a list of datasets that could be made available but have not yet been released by filtering accessLevel to public and accessURL to blank.
We added the rights field (formerly accessLevelComment) for data stewards to explain how to access restricted public datasets, and for agencies to have a place to record (even if only internally) the reason for not releasing a non-public dataset.
We added the systemOfRecords field for data stewards to optionally link to a relevant System of Records Notice URL. A System of Records is a group of any records under the control of any agency from which information is retrieved by the name of the individual or by some identifying number, symbol, or other identifier assigned to the individual.
Teach information systems management? Adopt this college textbook as is or personalize it online at Flat World. Change chapter titles, move content with ease, and delight in how much less your students pay. We publish peer-reviewed textbooks by expert authors. You make them perfect for your course.
Get involved with John's community by visiting and subscribing to his blog, The Week In Geek, where courseware, technology and strategy intersect and joining his Ning IT Community site where you can get more resources to teach Information Systems.
The teaching approach in Information Systems: A Manager's Guide to Harnessing Technology can change this. The text offers a proven approach that has garnered student praise, increased IS enrollment, and engaged students to think deeper and more practically about the space where business and technology meet. Every topic is related to specific business examples, so students gain an immediate appreciation of its importance. Rather than lead with technical topics, the book starts with strategic thinking, focusing on big-picture issues that have confounded experts but will engage students. And while chapters introduce concepts, cases on approachable, exciting firms across industries further challenge students to apply what they've learned, asking questions like:
Why was NetFlix able to repel Blockbuster and WalMart? How did Harrah's Casino's become twice as profitable as comparably-sized Caesar's, enabling the former to acquire the latter? How does Spain's fashion giant Zara, a firm that shuns the sort of offshore manufacturing used by every other popular clothing chain, offer cheap fashions that fly off the shelves, all while achieving growth rates and profit margins that put Gap to shame? Why do technology markets often evolve into winner-take-all or winner take-most scenarios? And how can managers compete when these dynamics are present? Why is Google more profitable than Disney? How much is Facebook really worth
A PowerPoint presentation highlighting key learning objectives and the main concepts for each chapter are available for you to use in your classroom. You can either cut and paste sections or use the presentation as a whole.
In response to the feedback from the pre-draft call for comment and initial working draft (annotated outline), NIST continued to refine the publications by organizing the guidance into two volumes and developing more actionable and focused guidance in each.
This document provides guidance on how an organization can develop information security measures to identify the adequacy of in-place security policies, procedures, and controls. It explains the measures prioritization process and how to evaluate measures.
The Tetradesmus obliquus UTEX B 72v1.1 genome was sequenced with PacBio, assembled with MECAT, andannotated using the JGI Annotation Pipeline. Incontrast to the v1.0 portal, thisannotation has been improved through the addition of Iso-Seq datato the original RNAseq transcriptome.
We have had many requests from people who want to post the ADHD-ASRS v1.1 instruments on their websites. Our preference would be that you link to this website and to our PDF of the instrument. This is because we are unable to coordinate with other sites in order to provide updates to the new methodological evidence about the instrument as it becomes available. However, these updates will be available from this site.
Adult ADHD Self-Report Scale (ASRS) Version 1.1: Background Information (PDF) Information on ASRS Distribution Scale in the General Population (PDF) DSM-5 scoring of the DSM-IV ASRS screening questions (PDF) ASRS v1.1 screener (6Q)- Scoring update(PDF) ADHD-ASRS instruments
Copyright New York University and Ronald C. Kessler, PhD. All rights reserved.
If you find it necessary to recreate the ADHD-ASRS v1.1 6-question screener either in paper or electronic format, it is important that the instrument is not altered (i.e., that all the response options are included, that the scoring algorithm is not altered, and that the two levels of shading within the response categories are included).