cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/qyrjma
title: Apennocoris pilosulus: rediscovery of a forgotten big-eyed bug taxon from New Caledonia (Heteroptera: Lygaeoidea: Geocoridae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Kóbor, Péter (2020): Apennocoris pilosulus: rediscovery of a forgotten big-eyed bug taxon from New Caledonia (Heteroptera: Lygaeoidea: Geocoridae). Zootaxa 4822 (2): 285-292, DOI: https://doi.org/10.11646/zootaxa.4822.2.10
DOI: 10.5281/zenodo.4453489
title: qdgc Kenya

description: QDGC tables delivered in geopackage file<br> - - - - - - - - - - - - - - - - - - - - - -<br> QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth.<br> <br> <br> Within each geopackage file you will find a number of tables with these names:<br> <br> <br> -tbl_qdgc_01<br> -tbl_qdgc_02<br> -tbl_qdgc_03<br> -tbl_qdgc_04<br> -tbl_qdgc_05<br> -etc<br> <br> <br> The attributes for each table are:<br> <br> <br> qdgc Unique Quarter Degree Grid Cell reference string<br> area_reference Country<br> level_qdgc QDGC level<br> cellsize degrees decimal degree for the longitudal and latitudal length of the cell<br> lon_center Longitude center of the cell<br> lat_center Latitudal center of the cell<br> area_km2 Calculated area for the cell<br> geom Geometry<br> <br> <br> Metadata<br> --------<br> Geodata GCS_WGS_1984<br> Datum: D_WGS_1984<br> Prime Meridian: 0<br> <br> <br> Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this:<br> - st_area(st_transform(geom, 102022))/1000000)<br> <br> <br> Licensing<br> ---------<br> Creative Commons Attribution 4.0 International<br> <br> <br> Conditions<br> ----------<br> Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it.<br> <br> <br> Thankyou<br> --------<br> The work has over the years been supported and receicved advice and moral support from many organisations and stakeholders. Here are some of them:<br> - Tanzania Wildlife Research Institute<br> - Dept of Biology, NTNU, Norway<br> - Norwegian Environment Agency<br> - Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey<br> <br> <br> References<br> ----------<br> * http://en.wikipedia.org/wiki/QDGC<br> * http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/<br> * http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection<br> * http://www.safe.com<br> <br> <br> <br> <br> Ragnvald Larsen<br> Trondheim 20th of January, 2021<br> <br> <br> ragnvald@mindland.com<br> www.mindland.com
DOI: 10.5281/zenodo.4453488
title: qdgc Kenya

description: QDGC tables delivered in geopackage file<br> - - - - - - - - - - - - - - - - - - - - - -<br> QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth.<br> <br> <br> Within each geopackage file you will find a number of tables with these names:<br> <br> <br> -tbl_qdgc_01<br> -tbl_qdgc_02<br> -tbl_qdgc_03<br> -tbl_qdgc_04<br> -tbl_qdgc_05<br> -etc<br> <br> <br> The attributes for each table are:<br> <br> <br> qdgc Unique Quarter Degree Grid Cell reference string<br> area_reference Country<br> level_qdgc QDGC level<br> cellsize degrees decimal degree for the longitudal and latitudal length of the cell<br> lon_center Longitude center of the cell<br> lat_center Latitudal center of the cell<br> area_km2 Calculated area for the cell<br> geom Geometry<br> <br> <br> Metadata<br> --------<br> Geodata GCS_WGS_1984<br> Datum: D_WGS_1984<br> Prime Meridian: 0<br> <br> <br> Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this:<br> - st_area(st_transform(geom, 102022))/1000000)<br> <br> <br> Licensing<br> ---------<br> Creative Commons Attribution 4.0 International<br> <br> <br> Conditions<br> ----------<br> Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it.<br> <br> <br> Thankyou<br> --------<br> The work has over the years been supported and receicved advice and moral support from many organisations and stakeholders. Here are some of them:<br> - Tanzania Wildlife Research Institute<br> - Dept of Biology, NTNU, Norway<br> - Norwegian Environment Agency<br> - Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey<br> <br> <br> References<br> ----------<br> * http://en.wikipedia.org/wiki/QDGC<br> * http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/<br> * http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection<br> * http://www.safe.com<br> <br> <br> <br> <br> Ragnvald Larsen<br> Trondheim 20th of January, 2021<br> <br> <br> ragnvald@mindland.com<br> www.mindland.com
DOI: 10.21227/fd7j-6e96
title: PCB-RE: CAD-threat to Hardware Security due to E-waste Globalization

description: Sensitive PCBs may end up at an adversarial e-waste location with the exile of any susceptible component to cement its intelligence and security. We introduce an original threat that can break the intelligence and explicate the vulnerabilities of that measurement.In this work we developed an automatic tool for printed circuit board reverse engineering (PCB-RE) in which it is assumed that the PCBs are devoid of any components or silkscreens, with only the wiring traces being accessible on the various layers. This models the scenario where the PCBs are damaged and/or discarded. In the latter case, sensitive PCBs may end up at an adversarial e-waste location. We demonstrate that our PCB RE tool extracts the correct bill of materials (BOM) for 10 different PCs by analyzing only the metal layers. We demonstrate that the proposed RE method can detect any integrated circuit (IC) on the PCB with no false positives and false negatives. This scheme also identifies passive components, such as resistors, capacitors, and inductors. Thus, we show that PCB security can be annihilated simply by metal trace analysis.
DOI: 10.21227/ke29-2j13
title: PCB-RE: CAD-threat to Hardware Security due to E-waste Globalization

description: Sensitive PCBs may end up at an adversarial e-waste location with the exile of any susceptible component to cement its intelligence and security. We introduce an original threat that can break the intelligence and explicate the vulnerabilities of that measurement.In this work we developed an automatic tool for printed circuit board reverse engineering (PCB-RE) in which it is assumed that the PCBs are devoid of any components or silkscreens, with only the wiring traces being accessible on the various layers. This models the scenario where the PCBs are damaged and/or discarded. In the latter case, sensitive PCBs may end up at an adversarial e-waste location. We demonstrate that our PCB RE tool extracts the correct bill of materials (BOM) for 10 different PCs by analyzing only the metal layers. We demonstrate that the proposed RE method can detect any integrated circuit (IC) on the PCB with no false positives and false negatives. This scheme also identifies passive components, such as resistors, capacitors, and inductors. Thus, we show that PCB security can be annihilated simply by metal trace analysis.
DOI: 10.5281/zenodo.4453481
title: InpactorDB: A Plant classified lineage-level LTR retrotransposon reference library for free-alignment methods based on Machine Learning

description: LTR retrotransposons are mobile elements that make up the major part of most plant genomes. Their identification and annotation via bioinformatics approaches represent a major challenge in the era of massive plant genome sequencing. In addition to their involvement in the variation in genome size, these elements are also associated in the function and structure of different chromosomal regions and in the alteration of the function of coding regions, among others. Several plant retrotransposon sequence databases of LTR retrotransposons are available with public access such as PGSB, RepetDB or restricted access such as Repbase. Although they are useful for approaches to identify LTR-RTs in new genomes by similarity, the elements of these databases are not classified down to the lineage/family level. with great depth. Here, we present InpactorDB a semi-curated dataset composed of 130,511 elements from 195 plant genomes (belonging to 108 plant species), classified down to the lineage level. This data set has been used to train two deep neural networks (one fully connected and one convolutional) for fast classification of elements. Used in lineage-level classification approaches, we obtain a score above 98% of F1-score, precision and recall. In order to classify elements of the ‘LTR_STRUC’ and ‘EDTA’ datasets, we used the methodology proposed by Inpactor, which uses homology-based strategy with known coding domains belonging to LTR-RTs. We utilized the RexDB domain library as reference. LTR-RTs were classified into superfamilies, Gypsy (RLG) or Copia (RLC) and sub-classified into lineages according to the similarities of five different amino acid reference domains (GAG, AP, RT, RNAseH, and INT domains). In addition, we applied filters to remove keep only intact elements: 1) to remove predicted elements with domains from two different superfamilies (i.e. Gypsy and Copia), 2) or elements with domains belonging to two or more different lineages, 3) to remove elements with lengths different than those reported by Gypsy Database (GyDB) with a tolerance of 20%, 4) to delete incomplete elements which has less than three identified domains, and 5) to remove elements with insertions of TE class II (reported in Repbase). The final non-redundant version of InpactorDB consists of 67,305 LTR retrotransposons. Both redundant and non-redundant versions of InpactorDB are available in Fasta format in which sequences have identifiers with the following general Identification code: &gt;Superfamily-Lineage-plant_family-specie-source-length-ID, Where Superfamily can is either RLC (for Copia) or RLG (for Gypsy), Lineage/family follows following the RexDB nomenclature, source (can be Repbase, RepetDB, PGSB, LTR_STRUC or EDTA datasets), length, and ID, is a unique number which identify each element inside the InpactorDB.
DOI: 10.21227/j2ee-ts64
title: Humanoid Terrain Characterization Dataset

description: This dataset was collected from force, current, angle (magnetic rotary encoder), and inertial sensors of the NAO humanoid robot while walking on Vinyl, Gravel, Wood, Concrete, Artificial grass, and Asphalt without a slope and while walking on Vinyl, Gravel, and Wood with a slope of 2 degrees. In total, counting all different axes and components of each sensor, we monitored 27 parameters on-board of the robot. The data was collected while the robot was moving 0.4 meters in the Forward, Backward, Left, and Right directions, and the data collection process was repeated five times for each terrain in each direction. Moreover, the forward and backward iterations had a maximum step size of 0.02 m, step height of 0.01 m and maximum step frequency of 0.005 while the left and right iterations had a maximum step size of 0.12 m, step height of 0.02 m and maximum step frequency of 0.005.
DOI: 10.21227/94am-be65
title: PCB-RE: CAD-threat to Hardware Security due to E-waste Globalization

description: Sensitive PCBs may end up at an adversarial e-waste location with the exile of any susceptible component to cement its intelligence and security. We introduce an original threat that can break the intelligence and explicate the vulnerabilities of that measurement.In this work we developed an automatic tool for printed circuit board reverse engineering (PCB-RE) in which it is assumed that the PCBs are devoid of any components or silkscreens, with only the wiring traces being accessible on the various layers. This models the scenario where the PCBs are damaged and/or discarded. In the latter case, sensitive PCBs may end up at an adversarial e-waste location. We demonstrate that our PCB RE tool extracts the correct bill of materials (BOM) for 10 different PCs by analyzing only the metal layers. We demonstrate that the proposed RE method can detect any integrated circuit (IC) on the PCB with no false positives and false negatives. This scheme also identifies passive components, such as resistors, capacitors, and inductors. Thus, we show that PCB security can be annihilated simply by metal trace analysis.
DOI: 10.5517/ccdc.csd.cc251wmr
title: CCDC 1997814: Experimental Crystal Structure Determination

description: Related Article: Antonio A. García-Valdivia, Fatin Jannus, Amalia García-García, Duane Choquesillo-Lazarte, Belén Fernández, Marta Medina-O'donnell, José A. Lupiáñez, Javier Cepeda, Fernando J. Reyes-Zurita, Antonio Rodríguez-Diéguez|2021|J.Inorg.Biochem.|215|111308|doi:10.1016/j.jinorgbio.2020.111308
DOI: 10.5281/zenodo.4453513
title: qdgc Gambia

description: QDGC tables delivered in geopackage file<br> - - - - - - - - - - - - - - - - - - - - - -<br> QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth.<br> <br> <br> Within each geopackage file you will find a number of tables with these names:<br> <br> <br> -tbl_qdgc_01<br> -tbl_qdgc_02<br> -tbl_qdgc_03<br> -tbl_qdgc_04<br> -tbl_qdgc_05<br> -etc<br> <br> <br> The attributes for each table are:<br> <br> <br> qdgc Unique Quarter Degree Grid Cell reference string<br> area_reference Country<br> level_qdgc QDGC level<br> cellsize degrees decimal degree for the longitudal and latitudal length of the cell<br> lon_center Longitude center of the cell<br> lat_center Latitudal center of the cell<br> area_km2 Calculated area for the cell<br> geom Geometry<br> <br> <br> Metadata<br> --------<br> Geodata GCS_WGS_1984<br> Datum: D_WGS_1984<br> Prime Meridian: 0<br> <br> <br> Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this:<br> - st_area(st_transform(geom, 102022))/1000000)<br> <br> <br> Licensing<br> ---------<br> Creative Commons Attribution 4.0 International<br> <br> <br> Conditions<br> ----------<br> Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it.<br> <br> <br> Thankyou<br> --------<br> The work has over the years been supported and receicved advice and moral support from many organisations and stakeholders. Here are some of them:<br> - Tanzania Wildlife Research Institute<br> - Dept of Biology, NTNU, Norway<br> - Norwegian Environment Agency<br> - Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey<br> <br> <br> References<br> ----------<br> * http://en.wikipedia.org/wiki/QDGC<br> * http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/<br> * http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection<br> * http://www.safe.com<br> <br> <br> <br> <br> Ragnvald Larsen<br> Trondheim 20th of January, 2021<br> <br> <br> ragnvald@mindland.com<br> www.mindland.com
DOI: 10.5281/zenodo.4453512
title: qdgc Gambia

description: QDGC tables delivered in geopackage file<br> - - - - - - - - - - - - - - - - - - - - - -<br> QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth.<br> <br> <br> Within each geopackage file you will find a number of tables with these names:<br> <br> <br> -tbl_qdgc_01<br> -tbl_qdgc_02<br> -tbl_qdgc_03<br> -tbl_qdgc_04<br> -tbl_qdgc_05<br> -etc<br> <br> <br> The attributes for each table are:<br> <br> <br> qdgc Unique Quarter Degree Grid Cell reference string<br> area_reference Country<br> level_qdgc QDGC level<br> cellsize degrees decimal degree for the longitudal and latitudal length of the cell<br> lon_center Longitude center of the cell<br> lat_center Latitudal center of the cell<br> area_km2 Calculated area for the cell<br> geom Geometry<br> <br> <br> Metadata<br> --------<br> Geodata GCS_WGS_1984<br> Datum: D_WGS_1984<br> Prime Meridian: 0<br> <br> <br> Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this:<br> - st_area(st_transform(geom, 102022))/1000000)<br> <br> <br> Licensing<br> ---------<br> Creative Commons Attribution 4.0 International<br> <br> <br> Conditions<br> ----------<br> Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it.<br> <br> <br> Thankyou<br> --------<br> The work has over the years been supported and receicved advice and moral support from many organisations and stakeholders. Here are some of them:<br> - Tanzania Wildlife Research Institute<br> - Dept of Biology, NTNU, Norway<br> - Norwegian Environment Agency<br> - Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey<br> <br> <br> References<br> ----------<br> * http://en.wikipedia.org/wiki/QDGC<br> * http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/<br> * http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection<br> * http://www.safe.com<br> <br> <br> <br> <br> Ragnvald Larsen<br> Trondheim 20th of January, 2021<br> <br> <br> ragnvald@mindland.com<br> www.mindland.com
DOI: 10.15468/e958sd
title: New species of Condylostylus Bigot (Diptera: Dolichopodidae) from Comoros Madagascar, Tanzania and South Africa, with key to Afrotropical species

description: This dataset contains the digitized treatments in Plazi based on the original journal article Grichanov, Igor Ya. (2020): New species of Condylostylus Bigot (Diptera: Dolichopodidae) from Comoros Madagascar, Tanzania and South Africa, with key to Afrotropical species. Zootaxa 4830 (1): 62-74, DOI: https://doi.org/10.11646/zootaxa.4830.1.2
DOI: 10.5281/zenodo.4453509
title: Data for Developmental cell programs are co-opted in inflammatory skin disease - filtered, annotated anndata object

description: Dataset to accompany BioRxiv preprint "Poised cell circuits in human skin are activated in disease" (https://www.biorxiv.org/content/10.1101/2020.11.05.369363v1). Data in AnnData (h5ad) format. Preprocessed to exclude doublets and low quality cells (&lt;200 genes and &gt;20% mitochondrial content). Includes neighborhood graph and UMAP coordinates and annotation from the manuscript in metadata. Full dataset with annotation ("anno_final") in obs.
description: This has been updated - the previous version had multiple old annotations that are no longer in use,
DOI: 10.5281/zenodo.4453525
title: Image captioning dataset for human activities

description: An image captioning dataset including images of humans performing various activities. The included images include the following activities: <code>walking, running, sleeping, swimming, sitting, jumping, riding, climbing, drinking and reading.</code>
DOI: 10.5281/zenodo.4453524
title: Image captioning dataset for human activities

description: An image captioning dataset including images of humans performing various activities. The included images include the following activities: <code>walking, running, sleeping, swimming, sitting, jumping, riding, climbing, drinking and reading.</code>
DOI: 10.5281/zenodo.4453497
title: PaCa_Control1_R1_001.fastq

description: Control1_Read1
DOI: 10.5281/zenodo.4453496
title: PaCa_Control1_R1_001.fastq

description: Control1_Read1
DOI: 10.5519/qd.s2j1qce9
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (294 records)
DOI: 10.5281/zenodo.4453526
title: Data Show Case Study

description: This dataset includes trace information as well as details about the code structure for four open source projects (Chess, Gantt, iTrust, JHotDraw). Each subfolder that starts with JSON contains the following JSON files: classes.json:This file specifies the list of Java classes for each case study. superclasses.json: This file specifies the list of Java superclasses andn subclasses within a Java project. interfaces.json: This file specifies the list of Java interfaces and implementations within a Java project. fieldclasses.json: This file specifies the list of variables declared within a class. fieldmethods.json: This file specifies the list of variables used within a method. parameters.json: This file specifies the list of parameters and return values within a method. methods.json: This file specifies the list of Java methods, along with the class that each method belongs to for every system. methodcalls.json: This file specifies the list of method calls made within a Java project. requirements.json: This file specifies the list of requirements under consideration for each system. traces.json: This file specifies the requirement-to-method trace values for each requirement-to-method entry within the trace matrix for each system. Each subfolder that starts with MYSQL contains the equivalent MYSQL scripts for the JSON files above: classes.sql fieldclasses.sql interfaces.sql methodcalls.sql methodcallsexecuted.sql methods.sql parameters.sql requirements.sql fieldmethods.sql superclasses.sql traces.sql tracesclasses.sql
DOI: 10.5281/zenodo.4337818
title: Data Show Case Study

description: This dataset includes trace information as well as details about the code structure for four open source projects (Chess, Gantt, iTrust, JHotDraw). Each subfolder that starts with JSON contains the following JSON files: classes.json:This file specifies the list of Java classes for each case study. superclasses.json: This file specifies the list of Java superclasses andn subclasses within a Java project. interfaces.json: This file specifies the list of Java interfaces and implementations within a Java project. fieldclasses.json: This file specifies the list of variables declared within a class. fieldmethods.json: This file specifies the list of variables used within a method. parameters.json: This file specifies the list of parameters and return values within a method. methods.json: This file specifies the list of Java methods, along with the class that each method belongs to for every system. methodcalls.json: This file specifies the list of method calls made within a Java project. requirements.json: This file specifies the list of requirements under consideration for each system. traces.json: This file specifies the requirement-to-method trace values for each requirement-to-method entry within the trace matrix for each system. Each subfolder that starts with MYSQL contains the equivalent MYSQL scripts for the JSON files above: classes.sql fieldclasses.sql interfaces.sql methodcalls.sql methodcallsexecuted.sql methods.sql parameters.sql requirements.sql fieldmethods.sql superclasses.sql traces.sql tracesclasses.sql
DOI: 10.5519/qd.76p28z44
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (91 records)