Unlike most gases, acetylene storage is a challenge because of its inherent pressure sensitivity. Herein, a square lattice (sql) coordination network [Cu(4,4′-bipyridine)2(BF4)2]n (sql-1-Cu-BF4) is investigated with respect to its C2H2 sorption behavior from 189 to 298 K. The C2H2 sorption studies revealed that sql-1-Cu-BF4 exhibits multistep isotherms that are temperature-dependent and consistent with the transformation from “closed” (nonporous) to four “open” (porous) phases induced by the C2H2 uptake. The Clausius–Clapeyron equation was used to calculate the performance of sql-1-Cu-BF4 for C2H2 storage at pressures >1 bar, which revealed that its volumetric working capacity at 288 K is slightly superior to acetone (174 vs 170 cm3 cm–3) over a safer pressure range (1–3.5 vs 1–15 bar). Molecular simulations provided insights into the observed switching phenomena, revealing that the layer expansion of sql-1-Cu-BF4 occurs via intercalation and inclusion of C2H2. These results indicate that switching adsorbent layered materials offer promise for utility in the context of C2H2 storage and delivery.

This is a collection of layers created by Tian Xie(Intern in DDP) in August, 2018. This collection includes Detroit Parcel Data(Parcel_collector), InfoUSA business data(BIZ_INFOUSA), and building data(Building). The building and business data have been edited by Tian during field research and have attached images.

Ten new coordination polymers constructed from two structurally related ligands, 1,1′-(1,5-pentanedidyl)bis(imidazole) (biim-5) and 2,2′-bis(1H-imidazolyl)ether (BIE), have been synthesized: Co(L1)(biim-5), [Co(L2)(biim-5)]·H2O (2), Co(L3)(biim-5), [Co(L4)(biim-5)]·4H2O (4), Co(L5)0.5(biim-5), [Co2(L6)(BIE)2]·1.5H2O (6), [Zn2(L6)(BIE)2]·2.5H2O (7), [Cd(L6)0.5(BIE)(H2O)]·H2O (8), [Zn2(L7)(BIE)2]·H2O (9) and Cd(L8)0.5(BIE)(H2O), where H2L1 = 1,2-benzenedicarboxylic acid, H2L2 = 1,3-benzenedicarboxylic acid, H2L3 = 5-OH-1,3-benzenedicarboxylic acid, H2L4 = DL-camphoric acid, H4L5 = 1,2,3,4-butanetetracarboxylic acid, H4L6 = 4,4′-oxidiphthalic acid, H4L7 = 4,4′-(hexafluoroisopropylidene)diphthalic acid, and H4L8 = 1,2,3,4-benzenetetracarboxylic acid. Compounds 1 and 4 display the same 2D layer structures with 63-hcb nets, but in 4 the water tetramers extend the layers to a 3D supramolecular framework by intermolecular hydrogen bonds. Compound 2 is an uncommon example of 2D double layers with the Schläfli symbol of (42·63·8). 3 shows a 2D sql net with large open windows, while 5 exhibits a rare 3,4-connected (83)2(85·10) topology. The crystal structures of 6 and 7 are close to being isostructural with a scarce (32·62·72)(32·4·62·7)2 topology. 8 contains two kinds of chiral layers, one left-handed and the other right-handed, with a unique topological type of (52·64)(53·62·7)2. Compound 9, related by a pseudocenter of inversion, possesses a 3D porous framework with a (3,4)-connected (4·102)2(42·104)-dmd-net. 10 shows a 1D chain structure. The structural and topological differences of these ten compounds indicate that the polycarboxylate ligands play important roles in producing novel frameworks and topologies of the coordination complexes. The infrared spectra and thermogravimetric and luminescent properties were also investigated for the compounds.

This polygon dataset shows Public Works, Parks, and Facilities open work orders in the City of Kirkland that Utility Superiors have tagged to share. This layer is meant to show the location, spatial extent, and related information of work orders projects. Data is derived from a spatial enable table views from the Lucity SQL Database. This layer has a definition query applied to show the location of open work orders.

Feature layer generated from running the Join Features solution.This links the Delta Polygons from Edmonds et. al. (2020) to the Delta Index layer.This layer cannot be downloaded as it is simply a SQL query hosted online

Three new CoII–azido complexes, [Co3(L)2(o-BDC)2(N3)2]n (1), [Co3(L)2(m-BDC)2(N3)2]n·nH2O (2), and [Co2(L)2(p-BDC)(N3)2]n·2nMeOH (3) [o-BDC = 1,2-benzenedicarboxylate, m-BDC = 1,3-benzenedicarboxylate, p-BDC = 1,4-benzenedicarboxylate, and L = di(1H-imidazol-1-yl)methane], have been constructed from di(1H-imidazol-1-yl)methane, azido, and different aromatic dicarboxylates under hydrothermal conditions. Complex 1 takes a dia topology with the Co3 units as connecting nodes, and complex 2 is a two-dimensional sql layer that further extends to a three-dimensional (3D) supramolecular network via π···π interaction between m-BDC ligands, while complex 3 forms a 3D pcu network based on Co2 units as nodes. The investigaton of magnetic properties of complexes 1–3 demonstrates that they all display ferromagnetic coupling between CoII ions but antiferromagnetic interaction between Co3 or Co2 units.

A polygon depiction of property sales from 2010 to the present that occurred in Stark County, Ohio. The Stark County Auditor's Office (SCAO) maintains records of property sales using a Computer-Assisted Mass Appraisal (CAMA) Database. This layer is a SQL view combining the sales records from the CAMA database with the Stark County parcel layer. A new view is created every morning through a combination of python scripts and SQL stored procedures. The data always reflects the most-recent information available from the previous day for both sources.

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

Thermal Springs - selected where LOCATION_NAME is like 'THERMAL %'. Geographic Extent: All of the Waikato Region. Positional accuracy is mostly to scales of 1:50000 but some locations have been ‘fixed’ using techniques of greater accuracy. The LOCATED Application holds metadata about the positional accuracy of bore and well locations. Ongoing data collection. Layer updated daily. Geographical location map references are accurate to  50 m (1:50000 scale) unless otherwise indicated by the LOCATED Application. Data Form: GIS Maps, LOCATED Application Reports Digital Format: Oracle Database – LOCATED Application and GIS Layer stored in SQL Server.For further metadata please see feature ENVIRONMENTAL_MONITORING.sdeadmin.LOCATED_THERMAL_SPRINGS in dataset LOCATED - Thermal Springs GIS Layer

This layer was compiled by the Cape Cod Comission (CCC). It consists of parcel centroid points representing A) those addresses that the Board of Health has determined to have failing Title 5 System(s), B) modeled locations where the CCC has used environmental and parcel characteristics to determine those areas where Title 5 Systems may have the potential to fail and C) those addresses that have submitted Title 5 Loans through the Barnstable County Community Septic Management Loan Program. The CCC-modeled locations are based on the CCC's Base_Layers.DBO.cape_Parcels SQL layer. CCC developed a layer consisting of (1) all parcels

Market values of properties within Stark County, Ohio. The Stark County Auditor's Office (SCAO) values properties in the county using a Computer-Assisted Mass Appraisal (CAMA) database. This layer is a SQL view combining the valuations from the CAMA database with the Stark County parcel layer. The data is always up-to-date through the current tax assessment year. The attribute table includes data on land and building values, both assessed and not-assessed. The override field indicates if a successful property appeal took place.

This is a collection of layers created by Tian Xie(Intern in DDP) in August, 2018. This collection includes Detroit Parcel Data(Parcel_collector), InfoUSA business data(BIZ_INFOUSA), and building data(Building). The building and business data have been edited by Tian during field research and have attached images.

The layers in this service reference the Beach Profiling data in the MGS_Data SQL Server database. They are provided here for the use in the Maine Beach Profiling Hub Initiative tools including Survey123, web maps, and summary tables.

The contents of this feature layer provide a visual aid for homes constructed during the period between 1945 to 1960. Data supporting the visual aids list which neighborhood these post World War II homes resides in, the style of the homes, along with its condition and integrity.The Historic Preservation Office works with the community to preserve these homes by enhancing archaeological, prehistoric, and historic resources throughout the City of Tempe. This work includes a wide range of partnerships with local homeowners, neighborhoods, developers/architects, boards/commissions, state and national agencies, as well as volunteer and non-profit preservation groupsContact: Will DukeContact E-Mail: will_duke@tempe.govContact Phone: N/ALink: N/AData Source: SQL Server/ArcGIS ServerData Source Type: GeospatialPreparation Method: N/APublish Frequency: As information changesPublish Method: AutomaticData Dictionary

This data set contains building permit information. The City of Charlottesville requires a permit prior to the commencement of any construction, alteration, movement, enlargement, replacement, demolition or change the use or occupancy of a building or structure.This data essentially the same information as the "Building Permits" data set, but it has been linked to the "Parcel Area" layer via SQL View. Note: There is a one-to-many relationship as one parcel may have multiple permits associated with it (current parcels only).The data is maintained daily and updates will display the next business day.

This layer contains visual aids (polygonal data) that represents businesses with approved sign packages. They appear as shapes of a building foot.Sign package approvals are distributed by the Department of Community Development per the guidelines located at http://www.tempe.gov/city-hall/community-development/signs. A PDF of each development's approved sign package is provided as a hyperlink.Contact: Will DukeContact E-Mail: will_duke@tempe.govContact Phone: N/ALink: N/AData Source: SQL Server/ArcGIS ServerData Source Type: GeospatialPreparation Method: N/APublish Frequency: As information changesPublish Method: AutomaticData Dictionary

This is a collection of layers created by Tian Xie(Intern in DDP) in August, 2018. This collection includes Detroit Parcel Data(Parcel_collector), InfoUSA business data(BIZ_INFOUSA), and building data(Building). The building and business data have been edited by Tian during field research and have attached images.

This dataset provides the supporting files for the paper entitled "Content-based Discovery for Web Map Service using Support Vector Machine and User Relevance Feedback", which has been accepted by PLOS ONE. The DOI of the paper is 10.1371/journal.pone.0166098. The dataset includes11689 layers from 653 OGC WMSs. It contains a archive of 11689 thumbnail images, two WMS layer metadata description files (in SQL script and CSV format respectively), an extracted image feature files and a data introduction document. Specially, the thumbnails are obtained by invoking WMS GetMap Operation for the available layers. The two layer metadata description files depict attribute fields of the layers, including keywords, abstract, boundingbox and etc. The image features were extracted from the WMS layer thumbnail images and contains total 11689 records. If you are interested in this research, please contact with hukai@whu.edu.cn or zhipeng.gui@whu.edu.cn.

Moving citation locations in the District of Columbia. The Vision Zero data contained in this layer pertain to moving violations issued by the District of Columbia's Metropolitan Police Department (MPD) and partner agencies with the authority. For example, DC's enforcement camera program cites speeders, blocking the box, and other moving offenses. Moving violation locations are summarized ticket counts based on time of day, week of year, year, and category of violation. Data was originally downloaded from the District Department of Motor Vehicle's eTIMS meter work order management system. Data was exported into DDOT’s SQL server, where the Office of the Chief Technology Officer (OCTO) geocoded citation data to the street segment level. Data was then visualized using the street segment centroid coordinates.

pcgpraqM is one of several Pima County Government Property Rights (PCGPR) layers. pcgpraqM displays Pima County Acquired Property Rights data, which include property, various easements, and other miscellaneous conveyances. The areas in question were either acquired by recorded deed or lease. Polygon features and attributes are based on recorded instruments. A nightly batch process appends all section shapefiles to create pcgpracq. The shape pcgpracq is further processed to join all area (polygon) values to related sql table attributes. If a duplicate/triplicate exists the polygon makes a copy of itself.The maintenance of this layer is handled by Pima County. For more detailed information, please refer to the original metadata, found here. PurposeShows information about property rights in Pima County.Dataset ClassificationLevel 0 - OpenKnown UsesUsed in the HP Dashboard Map.Known ErrorsThis layer has overlapping polygons that are not represented in the coverage format. Do not use a coverage format version of this layer. This layer is built from acquisition section drawings and related information stored in sql tables. This layer represents all document and/or classcode records related to an acquisition area (polygon). If an area (polygon) references more than one document and/or classcode, it creates a duplicate of itself and references the additional data. In that way all data is represented in the shapefile. In addition, the areas (polygons) are dissolved on common document data information. If a right of way area (polygon) was split during initial data entry along a section line, it is no longer represented by multiple polygons; the pieces are dissolved into one common area (polygon). As a result of the dissolve any BB_NO (unique identifier of the polygon disappears). In some cases, Pima County owned road Rights-Of-Way (ROW) do not encompass the entire portion of the overall road ROW.Data ContactPima County Information Technology Department - Geographic Information Systems201 N Stone Ave., 9th FloorTucson, AZ 85701GISDdata@pima.govUpdate FrequencyThe update of this layer is handled by Pima County. The last known update was 2014 but that date may not be accurate.