cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6073/pasta/259b374c146477477a5bc8a28eaef3e2
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 2001-02-21
DOI: 10.6073/pasta/2d17fc8aa17648cd5c4b14f2a7c434d5
title: Mega-Monsoon Experiment (MegaME) Vegetation Sampling Data from the Sevilleta National Wildlife Refuge, New Mexico
DOI: 10.6073/pasta/40e00c0df44df5e1e3c07a86054d2146
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Konza Prairie LTER collected on 2006-11-23
DOI: 10.6073/pasta/563f030be6cb09eae9d35ab7b78f3402
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1992-06-29
DOI: 10.6073/pasta/61a6cc56c82d67b3ac57fd21ffc28669
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1992-06-13
DOI: 10.6073/pasta/66a070ef076a7768ed2552a3a0027c01
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Konza Prairie LTER collected on 2006-12-09
DOI: 10.6073/pasta/6aa0b18c803917a8161168be870b8234
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Niwot Ridge LTER collected on 1999-10-13
DOI: 10.6073/pasta/821a5393972067e4886770c375e6db8b
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Niwot Ridge LTER collected on 1999-09-27
DOI: 10.6073/pasta/b0d93d675ac7ffcdb1fd486f60178b25
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 2001-03-09
DOI: 10.6073/pasta/bbf73dd2f3bd5d39259bcd10260f561c
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 2003-02-20
DOI: 10.6073/pasta/bd0829eb7aab3c7875bec17648d0b7c7
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Niwot Ridge LTER collected on 1999-08-26
DOI: 10.6073/pasta/d05e3c3870719dee07b5f5828a3a7427
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1992-02-22
DOI: 10.15468/zdhg1f
title: Test Dataset Please Ignore

description: Test Dataset Please Ignore. This resource was migrated.
DOI: 10.6073/pasta/0cee441a6147fca57936f668a3f326ea
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 2001-05-12
DOI: 10.6073/pasta/160d7c5becb94790ac68a99ede31b4b5
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Niwot Ridge LTER collected on 2000-03-05
DOI: 10.6073/pasta/1d837ad8bb8aac0b7491674d52ad280f
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Konza Prairie LTER collected on 2007-01-26
DOI: 10.6073/pasta/2cd16cc799fd179de76a786cf8747716
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1992-08-16
DOI: 10.6073/pasta/32eb637c9bee1143eaae9353386f8684
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Konza Prairie LTER collected on 2006-12-25
DOI: 10.6073/pasta/39eaf5d36d19f72944459be7a070c6d3
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 2003-04-09
DOI: 10.6073/pasta/559e1d0ee7eae51ba19c614ab1b58c8d
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 2001-04-26
DOI: 10.6073/pasta/5c18eca8e5f574632d8b5a82edd22c7b
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1992-10-03
DOI: 10.6073/pasta/60c882dbaabf8616d0b13cfbb4a057b7
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Niwot Ridge LTER collected on 1999-10-29
DOI: 10.6073/pasta/644beda7531def42bedac0dd75a3c515
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Niwot Ridge LTER collected on 1999-11-14
DOI: 10.6073/pasta/707ed9e069af59486c6e94263deb4e07
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 2001-05-28
DOI: 10.6073/pasta/9062a5cc7f38d73c4626c96b01354d15
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 2001-09-01
DOI: 10.6073/pasta/ae58b4002057d3ceb975aca5a732bb64
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1992-07-31
DOI: 10.6073/pasta/b86c95e6f7f32b2eb7d0ac4751d354e2
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 2001-06-29
DOI: 10.6073/pasta/ba457cadf948d17b1b37640ec4eee80b
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1992-09-01
DOI: 10.6073/pasta/bc761daf0c7e4b323bc7d98203787f11
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1992-07-15
DOI: 10.6073/pasta/e9cc8cdab3dc9632c42cc5f20340d742
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Niwot Ridge LTER collected on 2000-01-17
DOI: 10.6073/pasta/4a59aac1da7481accae47d37644a776b
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1992-10-19
DOI: 10.6073/pasta/5d485ebe19f8aa4817f3fb2dd7fab372
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Niwot Ridge LTER collected on 2000-07-11
DOI: 10.6073/pasta/6e8b5fbbbf1ddecb521fe77cfa2747ec
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Niwot Ridge LTER collected on 2000-08-12
DOI: 10.6073/pasta/724ece8782e318faa15f8a3019ac4d7e
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 2003-04-25
DOI: 10.6073/pasta/7dd14bca49c6788e135c1ed4e26e5b50
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 2003-05-27
DOI: 10.6073/pasta/930d9751c0e973a855ab3b400cdf22a1
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 2001-10-19
DOI: 10.6073/pasta/96ce079a1a90bff17a2a3ea2b2519bd9
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1992-12-22
DOI: 10.6073/pasta/c2a7e327002244847a557d4f5c0ef80b
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 2001-11-04
DOI: 10.6073/pasta/c58bd7a85944633ddfbc6408cf6fb377
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Konza Prairie LTER collected on 2007-02-27
DOI: 10.6073/pasta/d8cf12b5a0849b876ffae3a6c75a9740
title: Burn Study Sites Quadrat Data for the Net Primary Production Study at the Sevilleta National Wildlife Refuge, New Mexico (2004-present)
DOI: 10.6073/pasta/ea6a4f52f17106b9e183d2e222b76198
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 2002-01-07
DOI: 10.6073/pasta/f4eedb49f0fbcf23f0b5b99a9aa06681
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Niwot Ridge LTER collected on 2000-06-09
DOI: 10.6073/pasta/079196f521c1e34807159b9db5389198
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Konza Prairie LTER collected on 2007-04-16
DOI: 10.6073/pasta/07c71e514d138629e9df5b757f5c1d2c
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Niwot Ridge LTER collected on 2000-09-13
DOI: 10.6073/pasta/11a4e6f7f444cba4aee1ee4998cc3482
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1993-01-23
DOI: 10.6073/pasta/20f868e25ed8596e4ed208d8b6a4c7b3
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 2002-08-03
DOI: 10.6073/pasta/2b7eb3860088d44b1ec83788c8268a2a
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 2002-06-16
DOI: 10.6073/pasta/5b63e5413292bddc90fba8bdd7cfc92f
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1993-03-12
DOI: 10.6073/pasta/881500871afed16189656e005470187c
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 2003-07-14
DOI: 10.6073/pasta/8b405d232c821772b478204c60b66d5a
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 1993-02-24
DOI: 10.6073/pasta/9b9b71b2e31ca282a8d3eb3c2a63a7c6
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for North Temperate Lakes LTER collected on 2002-02-08
DOI: 10.6073/pasta/e55b31f3f81d9d5d86977cf81a596626
title: LEDAPS corrected Landsat Enhanced Thematic Mapper image data for Niwot Ridge LTER collected on 2000-10-15
DOI: 10.5281/zenodo.8024894
title: Risk Product Detection Dataset (RPDD) from Alibaba's e-commerce platform

description: This is a risk product detection dataset (RPDD) that is based on a real risk detection scenario from Alibaba's e-commerce platform. All the <em>.csv </em>files are the initial data which consists of edges, node features, supervised information as well as IDs of candidate items. More details can see in <em>README.md</em>, we list the type of each column in each file as follows. 1. RPDD_edges.csv (edge file): <br> source_node_id int, target_node_id int, source_node_type string, target_node_type string, edge_type string<br> 2. RPDD_nodes.csv (node file):<br> node_id int, node_type string, node_atts string (notice that node_atts are 256-dimensional feature vector strings with delimiter ":")<br> 3. RPDD_train_labels.csv (training labels):<br> item_id int, label int<br> 4. RPDD_test_ids.csv (testing ids):<br> item_id int<br> 5. RPDD_test_labels.csv (testing labels):<br> item_id int, label int Besides that,<em> graph.bin</em> is the format in DGL which is constructed by all <em>*.csv</em> files, and a general method to load this graph as follows: <pre><code class="language-python">from dgl import load_graphs #should install dgl ds,_ = load_graphs("./graph.bin") g = ds[0] print(g)</code></pre> And then you can easily get a large-scale heterogeneous graph with 157,814,864 edges and 13,806,619 nodes, our graph task is node classification: detect risk product.
DOI: 10.5281/zenodo.8100221
title: Risk Commodity Dataset (RCDD) from Alibaba's e-commerce platform

description: This is a risk commodity detection dataset (RCDD) that is based on a real risk detection scenario from Alibaba's e-commerce platform. All the <em>.csv </em>files are the initial data which consists of edges, node features, supervised information as well as IDs of candidate items. More details can see in <em>README.md</em>, we list the type of each column in each file as follows. 1. RCDD_edges.csv (edge file): <br> source_node_id int, target_node_id int, source_node_type string, target_node_type string, edge_type string<br> 2. RCDD_nodes.csv (node file):<br> node_id int, node_type string, node_atts string (notice that node_atts are 256-dimensional feature vector strings with delimiter ":")<br> 3. RCDD_train_labels.csv (training labels):<br> item_id int, label int<br> 4. RCDD_test_ids.csv (testing ids):<br> item_id int<br> 5. RCDD_test_labels.csv (testing labels):<br> item_id int, label int Besides that,<em> graph.bin</em> is the format in DGL which is constructed by all <em>*.csv</em> files, and a general method to load this graph as follows: <pre><code class="language-python">from dgl import load_graphs #should install dgl ds,_ = load_graphs("./graph.bin") g = ds[0] print(g)</code></pre> And then you can easily get a large-scale heterogeneous graph with 157,814,864 edges and 13,806,619 nodes, our graph task is node classification: detect risk product.
DOI: 10.5281/zenodo.8100246
title: Risk Commodity Dataset (RCDD) from Alibaba's e-commerce platform

description: This is a risk commodity detection dataset (RCDD) that is based on a real risk detection scenario from Alibaba's e-commerce platform. All the <em>.csv </em>files are the initial data which consists of edges, node features, supervised information as well as IDs of candidate items. More details can see in <em>README.md</em>, we list the type of each column in each file as follows: 1. RCDD_edges.csv (edge file): <br> source_node_id int, target_node_id int, source_node_type string, target_node_type string, edge_type string<br> 2. RCDD_nodes.csv (node file):<br> node_id int, node_type string, node_atts string (notice that node_atts are 256-dimensional feature vector strings with delimiter ":")<br> 3. RCDD_train_labels.csv (training labels):<br> item_id int, label int<br> 4. RCDD_test_ids.csv (testing ids):<br> item_id int<br> 5. RCDD_test_labels.csv (testing labels):<br> item_id int, label int Besides that,<em> graph.bin</em> is the format in DGL which is constructed by all <em>*.csv</em> files, and a general method to load this graph as follows: <pre><code class="language-python">from dgl import load_graphs #should install dgl ds,_ = load_graphs("./graph.bin") g = ds[0] print(g)</code></pre> And then you can easily get a large-scale heterogeneous graph with 157,814,864 edges and 13,806,619 nodes, our graph task is node classification: detect risk product.
DOI: 10.6084/m9.figshare.23612668
title: Racism, sexism, and remnants of slavery in job advertisements

description: Abstract Job advertisements may present elements of racial oppression, albeit in subtle ways, as in the expressions “good looks” and “good hygiene.” This acknowledgment makes us reflect that the job market, and society in general, is racially structured, not only in the permanence and professional advancement of black women but even in recruitment, including online job advertisements. This research aims to understand how the publication of job advertisements with phenotypic characteristics such as “good looks” contribute to the perpetuation of racism and sexism. Specifically, we aim to understand how resistance to racist labor market practices and structure occurs. We invoke references of decoloniality and intersectionality and use thematic analysis in a corpus of 285 job advertisements on four classified sites and four black employability initiatives present on LinkedIn. In the results and discussion, we indicate that an ideal of a white worker is perpetuated in the domestic work market and that good image and good hygiene are euphemistic expressions that mask racism in the labor market, which is a remnant of slavery. Current black employability initiatives highlight the need to think about affirmative action for black men and women, historically excluded from more prestigious and remunerative jobs and decision-making spaces. This movement, between oppression, resistance, and black existence, operates a decolonial project of intervention in reality, the main contribution of this research.
DOI: 10.6084/m9.figshare.23612668.v1
title: Racism, sexism, and remnants of slavery in job advertisements

description: Abstract Job advertisements may present elements of racial oppression, albeit in subtle ways, as in the expressions “good looks” and “good hygiene.” This acknowledgment makes us reflect that the job market, and society in general, is racially structured, not only in the permanence and professional advancement of black women but even in recruitment, including online job advertisements. This research aims to understand how the publication of job advertisements with phenotypic characteristics such as “good looks” contribute to the perpetuation of racism and sexism. Specifically, we aim to understand how resistance to racist labor market practices and structure occurs. We invoke references of decoloniality and intersectionality and use thematic analysis in a corpus of 285 job advertisements on four classified sites and four black employability initiatives present on LinkedIn. In the results and discussion, we indicate that an ideal of a white worker is perpetuated in the domestic work market and that good image and good hygiene are euphemistic expressions that mask racism in the labor market, which is a remnant of slavery. Current black employability initiatives highlight the need to think about affirmative action for black men and women, historically excluded from more prestigious and remunerative jobs and decision-making spaces. This movement, between oppression, resistance, and black existence, operates a decolonial project of intervention in reality, the main contribution of this research.
DOI: 10.6084/m9.figshare.23612670
title: Weed interference periods in lettuce crop

description: ABSTRACT The knowledge of the adequate period for the beginning of weed management in lettuce crop can increase the efficiency of production, reducing the costs with management. The aim of this study was to determine the pre-interference period (PIP), the critical time for weed removal (CTWR) and the critical period of weed control (CPWC) in three lettuce cultivars, Elisa, Lucy Brown and Solaris. Coexistence and the absence of weeds were evaluated for each cultivar. The design was in randomized blocks with seven treatments (0, 7, 14, 21, 28, 35 and 42 days after transplanting - DAT) of coexistence and the same seven periods of weed absence with four replicates in each experiment. The highest infestation densities were with the species Oxalis latifolia, Coronopus didymus and Amaranthus hybridus. The leaf area and number of leaves per plant were reduced by up to 50% and 66%, respectively, in relation to the longest period of coexistence with weeds and yield reduced by 48% (Elisa), 40% (Lucy Brown) and 33% (Solaris). The cultivar Solaris showed greater tolerance to weed interference. Based on yield, PIP was 1, 3 and 3 DAT and CTWR was 37, 37 and 35 DAT, and CPWC was 2 to 37, 3 to 37 and 3 to 35 for the cultivars Elisa, Lucy Brown and Solaris, respectively.
DOI: 10.6084/m9.figshare.23612670.v1
title: Weed interference periods in lettuce crop

description: ABSTRACT The knowledge of the adequate period for the beginning of weed management in lettuce crop can increase the efficiency of production, reducing the costs with management. The aim of this study was to determine the pre-interference period (PIP), the critical time for weed removal (CTWR) and the critical period of weed control (CPWC) in three lettuce cultivars, Elisa, Lucy Brown and Solaris. Coexistence and the absence of weeds were evaluated for each cultivar. The design was in randomized blocks with seven treatments (0, 7, 14, 21, 28, 35 and 42 days after transplanting - DAT) of coexistence and the same seven periods of weed absence with four replicates in each experiment. The highest infestation densities were with the species Oxalis latifolia, Coronopus didymus and Amaranthus hybridus. The leaf area and number of leaves per plant were reduced by up to 50% and 66%, respectively, in relation to the longest period of coexistence with weeds and yield reduced by 48% (Elisa), 40% (Lucy Brown) and 33% (Solaris). The cultivar Solaris showed greater tolerance to weed interference. Based on yield, PIP was 1, 3 and 3 DAT and CTWR was 37, 37 and 35 DAT, and CPWC was 2 to 37, 3 to 37 and 3 to 35 for the cultivars Elisa, Lucy Brown and Solaris, respectively.
DOI: 10.6084/m9.figshare.23573965
title: Size and sex composition of three carcharhiniform sharks landed by a coastal artisanal fleet from the northeastern coast of Brazil

description: Abstract Sharks are among the marine organisms most impacted by intense fisheries, a worrying fact since they are key components in the trophic structure of ecosystems. In this context, fish landing data can help managers in shark conservation. This study characterized the landings of Carcharhinus acronotus, Mustelus cf. canis, and Sphyrna mokarran in the Mucuripe Embayment, Ceará, Brazil, along three study periods (1998–1999, 2006–2008, and 2015–2016). A total of 223 specimens were recorded from 227 site visits. Carcharhinus acronotus specimens measured from 50 to 139 cm in total length (TL), with a 1.3:1 sex ratio in favor of males. Most of the specimens recorded were adults (55.1% of males and 62.9% of females). Mustelus cf. canis varied from 64 to 133 cm in TL, with a sex ratio in favor of females (3.1:1). Most of the specimens recorded were adults (50% of males and 68% of females). Sphyrna mokarran specimens varied from 116 to 380 cm in TL, with a sex ratio of 7:1 in favor of males. Most of the specimens recorded were juveniles. This is the first fishery-dependent study to provide information on size and sex composition of these species for most of the southwestern equatorial Atlantic.
DOI: 10.6084/m9.figshare.23573965.v1
title: Size and sex composition of three carcharhiniform sharks landed by a coastal artisanal fleet from the northeastern coast of Brazil

description: Abstract Sharks are among the marine organisms most impacted by intense fisheries, a worrying fact since they are key components in the trophic structure of ecosystems. In this context, fish landing data can help managers in shark conservation. This study characterized the landings of Carcharhinus acronotus, Mustelus cf. canis, and Sphyrna mokarran in the Mucuripe Embayment, Ceará, Brazil, along three study periods (1998–1999, 2006–2008, and 2015–2016). A total of 223 specimens were recorded from 227 site visits. Carcharhinus acronotus specimens measured from 50 to 139 cm in total length (TL), with a 1.3:1 sex ratio in favor of males. Most of the specimens recorded were adults (55.1% of males and 62.9% of females). Mustelus cf. canis varied from 64 to 133 cm in TL, with a sex ratio in favor of females (3.1:1). Most of the specimens recorded were adults (50% of males and 68% of females). Sphyrna mokarran specimens varied from 116 to 380 cm in TL, with a sex ratio of 7:1 in favor of males. Most of the specimens recorded were juveniles. This is the first fishery-dependent study to provide information on size and sex composition of these species for most of the southwestern equatorial Atlantic.
DOI: 10.6084/m9.figshare.23612671
title: Construction and Validation of a Protocol Targeting Patients on Oral Anticoagulation with Warfarin

description: Abstract Background Warfarin is an oral anticoagulant that is very useful in preventing thromboembolism, though it is considered a drug with a high risk of causing adverse events. Considering the practical challenges in controlling oral anticoagulation, the patients on warfarin could benefit from educational strategies aimed at behavioral changes, active participation in self-care, and adherence to drug therapy. Objective The aim was to construct and validate the EmpoderACO protocol for behavioral changes in warfarin patients. Methods The methodological steps were: definition of concepts and domains of self-care, identification of objectives, construction and selection of items, assessment of content validity, and pre-test in the target population. Results Relevance, adequacy, clarity, and internal reliability of the instrument’s items were assessed by a multidisciplinary judges committee (JC) through the E-surv web platform, obtaining an average agreement of ≥0.91. The understanding of the instrument measured by the target population revealed adequate clarity with a coefficient average of 0.96. Conclusion EmpoderACO can aid in qualifying the communication process between medical professionals and patients, as well as in improving adherence to both treatment and clinical outcomes, and can be replicated in healthcare settings.